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Why We Can Be Confident of Turing Test Capability Within a Quarter Century
The advent of strong AI (exceeding human intelligence) is the most important transformation this century will see, and it will happen within 25 years, says Ray Kurzweil, who will present this paper at The Dartmouth Artificial Intelligence Conference: The next 50 years (AI@50) on July 14, 2006.
Published on KurzweilAI.net July 13, 2006. Excerpted from The
Singularity is Near, When Humans Transcend Biology by
Ray Kurzweil.
Consider another argument put forth by Turing. So far we have
constructed only fairly simple and predictable artifacts. When
we increase the complexity of our machines, there may, perhaps,
be surprises in store for us. He draws a parallel with a fission
pile. Below a certain "critical" size, nothing much happens: but
above the critical size, the sparks begin to fly. So too, perhaps,
with brains and machines. Most brains and all machines are, at
present "sub-critical"—they react to incoming stimuli in
a stodgy and uninteresting way, have no ideas of their own, can
produce only stock responses—but a few brains at present,
and possibly some machines in the future, are super-critical,
and scintillate on their own account. Turing is suggesting that
it is only a matter of complexity, and that above a certain level
of complexity a qualitative difference appears, so that "super-critical"
machines will be quite unlike the simple ones hitherto envisaged.
—J. R. Lucas, Oxford philosopher, in his 1961 essay "Minds,
Machines, and Gödel"1
Given that superintelligence will one day be technologically
feasible, will people choose to develop it? This question can
pretty confidently be answered in the affirmative. Associated
with every step along the road to superintelligence are enormous
economic payoffs. The computer industry invests huge sums in the
next generation of hardware and software, and it will continue
doing so as long as there is a competitive pressure and profits
to be made. People want better computers and smarter software,
and they want the benefits these machines can help produce. Better
medical drugs; relief for humans from the need to perform boring
or dangerous jobs; entertainment—there is no end to the list
of consumer-benefits. There is also a strong military motive to
develop artificial intelligence. And nowhere on the path is there
any natural stopping point where technophobics could plausibly
argue "hither but not further. —Nick Bostrom, "How Long
Before Superintelligence?" 1997
It is hard to think of any problem that a superintelligence
could not either solve or at least help us solve. Disease, poverty,
environmental destruction, unnecessary suffering of all kinds:
these are things that a superintelligence equipped with advanced
nanotechnology would be capable of eliminating. Additionally,
a superintelligence could give us indefinite lifespan, either
by stopping and reversing the aging process through the use of
nanomedicine, or by offering us the option to upload ourselves.
A superintelligence could also create opportunities for us to
vastly increase our own intellectual and emotional capabilities,
and it could assist us in creating a highly appealing experiential
world in which we could live lives devoted to joyful game-playing,
relating to each other, experiencing, personal growth, and to
living closer to our ideals —Nick Bostrom, "Ethical Issues
in Advanced Artificial Intelligence," 2003
Will robots inherit the earth? Yes, but they will be our children.
—Marvin Minsky, 1995
Of the three primary revolutions underlying the Singularity (G,
N, and R), the most profound is R, which refers to the creation
of nonbiological intelligence that exceeds that of unenhanced humans.
A more intelligent process will inherently outcompete one that is
less intelligent, making intelligence the most powerful force in
the universe.
While the "R" in GNR stands for robotics, the real issue involved
here is strong AI (artificial intelligence that exceeds human intelligence).
The standard reason for emphasizing robotics in this formulation
is that intelligence needs an embodiment, a physical presence, to
affect the world. I disagree with the emphasis on physical presence,
however, for I believe that the central concern is intelligence.
Intelligence will inherently find a way to influence the world,
including creating its own means for embodiment and physical manipulation.
Furthermore, we can include physical skills as a fundamental part
of intelligence; a large portion of the human brain (the cerebellum,
comprising more than half our neurons), for example, is devoted
to coordinating our skills and muscles.
Artificial intelligence at human levels will necessarily greatly
exceed human intelligence for several reasons. As I pointed out
earlier machines can readily share their knowledge. As unenhanced
humans we do not have the means of sharing the vast patterns of
interneuronal connections and neurotransmitter-concentration levels
that comprise our learning, knowledge, and skills, other than through
slow, language-based communication. Of course, even this method
of communication has been very beneficial, as it has distinguished
us from other animals and has been an enabling factor in the creation
of technology.
Human skills are able to develop only in ways that have been evolutionarily
encouraged. Those skills, which are primarily based on massively
parallel pattern recognition, provide proficiency for certain tasks,
such as distinguishing faces, identifying objects, and recognizing
language sounds. But they're not suited for many others, such as
determining patterns in financial data. Once we fully master pattern-recognition
paradigms, machine methods can apply these techniques to any type
of pattern.2
Machines can pool their resources in ways that humans cannot. Although
teams of humans can accomplish both physical and mental feats that
individual humans cannot achieve, machines can more easily and readily
aggregate their computational, memory and communications resources.
As discussed earlier, the Internet is evolving into a worldwide
grid of computing resources that can be instantly brought together
to form massive supercomputers.
Machines have exacting memories. Contemporary computers can master
billions of facts accurately, a capability that is doubling every
year.3 The underlying speed and price-performance of
computing itself is doubling every year, and the rate of doubling
is itself accelerating.
As human knowledge migrates to the Web, machines will be able to
read, understand, and synthesize all human-machine information.
The last time a biological human was able to grasp all human scientific
knowledge was hundreds of years ago.
Another advantage of machine intelligence is that it can consistently
perform at peak levels and can combine peak skills. Among humans
one person may have mastered music composition, while another may
have mastered transistor design, but given the fixed architecture
of our brains we do not have the capacity (or the time) to develop
and utilize the highest level of skill in every increasingly specialized
area. Humans also vary a great deal in a particular skill, so that
when we speak, say, of human levels of composing music, do we mean
Beethoven, or do we mean the average person? Nonbiological intelligence
will be able to match and exceed peak human skills in each area.
For these reasons, once a computer is able to match the subtlety
and range of human intelligence, it will necessarily soar past it,
and then continue its double- exponential ascent.
A key question regarding the Singularity is whether the "chicken"
(strong AI) or the "egg" (nanotechnology) will come first. In other
words, will strong AI lead to full nanotechnology (molecular manufacturing
assemblers that can turn information into physical products), or
will full nanotechnology lead to strong AI? The logic of the first
premise is that strong AI would imply superhuman AI for the reasons
just cited; and superhuman AI would be in a position to solve any
remaining design problems required to implement full nanotechnology.
The second premise is based on the realization that the hardware
requirements for strong AI will be met by nanotechnology-based computation.
Likewise the software requirements will be facilitated by nanobots
that could create highly detailed scans of human brain functioning
and thereby achieve the completion of reverse engineering the human
brain.
Both premises are logical; it's clear that either technology can
assist the other. The reality is that progress in both areas will
necessarily use our most advanced tools, so advances in each field
will simultaneously facilitate the other. However, I do expect that
full MNT will emerge prior to strong AI, but only by a few years
(around 2025 for nanotechnology, around 2029 for strong AI).
As revolutionary as nanotechnology will be, strong AI will have
far more profound consequences. Nanotechnology is powerful but not
necessarily intelligent. We can devise ways of at least trying to
manage the enormous powers of nanotechnology, but superintelligence
innately cannot be controlled.
Runaway AI. Once strong AI is achieved, it can readily be
advanced and its powers multiplied, as that is the fundamental nature
of machine abilities. As one strong AI immediately begets many strong
AIs, the latter access their own design, understand and improve
it, and thereby very rapidly evolve into a yet more capable, more
intelligent AI, with the cycle repeating itself indefinitely. Each
cycle not only creates a more intelligent AI but takes less time
than the cycle before it, as is the nature of technological evolution
(or any evolutionary process). The premise is that once strong AI
is achieved, it will immediately become a runaway phenomenon of
rapidly escalating superintelligence.4
My own view is only slightly different. The logic of runaway AI
is valid, but we still need to consider the timing. Achieving human
levels in a machine will not immediately cause a runaway
phenomenon. Consider that a human level of intelligence has limitations.
We have examples of this today—about six billion of them. Consider
a scenario in which you took one hundred humans from, say, a shopping
mall. This group would constitute examples of reasonably well educated
humans. Yet if this group was presented with the task of improving
human intelligence, it wouldn't get very far, even if provided with
the templates of human intelligence. It would probably have a hard
time creating a simple computer. Speeding up the thinking and expanding
the memory capacities of these one hundred humans would not immediately
solve this problem.
I pointed out above that machines will match (and quickly exceed)
peak human skills in each area of skill. So instead, let's take
one hundred scientists and engineers. A group of technically trained
people with the right backgrounds would be capable of improving
accessible designs. If a machine attained equivalence to one hundred
(and eventually one thousand, then one million) technically trained
humans, each operating much faster than a biological human, a rapid
acceleration of intelligence would ultimately follow.
However, this acceleration won't happen immediately when a computer
passes the Turing test. The Turing test is comparable to matching
the capabilities of an average, educated human and thus is closer
to the example of humans from a shopping mall. It will take time
for computers to master all of the requisite skills and to marry
these skills with all the necessary knowledge bases.
Once we've succeeded in creating a machine that can pass the Turing
test (around 2029), the succeeding period will be an era of consolidation
in which nonbiological intelligence will make rapid gains. However,
the extraordinary expansion contemplated for the Singularity, in
which human intelligence is multiplied by billions, won't take place
until the mid-2040s (as discussed in chapter 3 of The Singularity
is Near).
The AI Winter
There's this stupid myth out there that A.I. has failed, but
A.I. is everywhere around you every second of the day. People just
don't notice it. You've got A.I. systems in cars, tuning the parameters
of the fuel injection systems. When you land in an airplane, your
gate gets chosen by an A.I. scheduling system. Every time you use
a piece of Microsoft software, you've got an A.I. system trying
to figure out what you're doing, like writing a letter, and it does
a pretty damned good job. Every time you see a movie with computer-generated
characters, they're all little A.I. characters behaving as a group.
Every time you play a video game, you're playing against an A.I.
system. —Rodney Brooks, director of the MIT AI Lab5
I still run into people who claim that artificial intelligence
withered in the 1980s, an argument that is comparable to insisting
that the Internet died in the dot-com bust of the early 2000s.6
The bandwidth and price-performance of Internet technologies, the
number of nodes (servers), and the dollar volume of e-commerce all
accelerated smoothly through the boom as well as the bust and the
period since. The same has been true for AI.
The technology hype cycle for a paradigm shift—railroads,
AI, Internet, telecommunications, possibly now nanotechnology—typically
starts with a period of unrealistic expectations based on a lack
of understanding of all the enabling factors required. Although
utilization of the new paradigm does increase exponentially, early
growth is slow until the knee of the exponential-growth curve is
realized. While the widespread expectations for revolutionary change
are accurate, they are incorrectly timed. When the prospects do
not quickly pan out, a period of disillusionment sets in. Nevertheless
exponential growth continues unabated, and years later a more mature
and more realistic transformation does occur.
We saw this in the railroad frenzy of the nineteenth century, which
was followed by widespread bankruptcies. (I have some of these early
unpaid railroad bonds in my collection of historical documents.)
And we are still feeling the effects of the e-commerce and telecommunications
busts of several years ago, which helped fuel a recession from which
we are now recovering.
AI experienced a similar premature optimism in the wake of programs
such as the 1957 General Problem Solver created by Allen Newell,
J. C. Shaw, and Herbert Simon, which was able to find proofs for
theorems that had stumped mathematicians such as Bertrand Russell,
and early programs from the MIT Artificial Intelligence Laboratory,
which could answer SAT questions (such as analogies and story problems)
at the level of college students.7 A rash of AI companies
occurred in the 1970s, but when profits did not materialize there
was an AI "bust" in the 1980s, which has become known as the "AI
winter." Many observers still think that the AI winter was the end
of the story and that nothing has since come of the AI field.
Yet today many thousands of AI applications are deeply embedded
in the infrastructure of every industry. Most of these applications
were research projects ten to fifteen years ago. People who ask,
"Whatever happened to AI?" remind me of travelers to the rain forest
who wonder, "Where are all these many species that are supposed
to live here?" when hundreds of species of flora and fauna are flourishing
only a few dozen meters away, deeply integrated into the local ecology.
We are well into the era of "narrow AI," which refers to artificial
intelligence that performs a useful and specific function that once
required human intelligence to perform, and does so at human levels
or better. Often narrow AI systems greatly exceed the speed of humans,
as well as provide the ability to manage and consider thousands
of variables simultaneously. I describe a broad variety of narrow
AI examples below.
These time frames for AI's technology cycle (a couple of decades
of growing enthusiasm, a decade of disillusionment, then a decade
and a half of solid advance in adoption) may seem lengthy, compared
to the relatively rapid phases of the Internet and telecommunications
cycles (measured in years, not decades), but two factors must be
considered. First, the Internet and telecommunications cycles were
relatively recent, so they are more affected by the acceleration
of paradigm shift (as discussed in chapter 1 of The Singularity
is Near). So recent adoption cycles (boom, bust, and recovery)
will be much faster than ones that started forty years ago. Second,
the AI revolution is the most profound transformation that human
civilization will experience, so it will take longer to mature than
less complex technologies. It is characterized by the mastery of
the most important and most powerful attribute of human civilization,
indeed of the entire sweep of evolution on our planet: intelligence.
It's the nature of technology to understand a phenomenon and then
engineer systems that concentrate and focus that phenomenon to greatly
amplify it. For example, scientists discovered a subtle property
of curved surfaces known as Bernoulli's principle: a gas (such as
air) travels more quickly over a curved surface than over a flat
surface. Thus, air pressure over a curved surface is lower than
over a flat surface. By understanding, focusing, and amplifying
the implications of this subtle observation, our engineering created
all of aviation. Once we understand the principles of intelligence,
we will have a similar opportunity to focus, concentrate, and amplify
its powers.
As I reviewed in chapter 4 of The Singularity is Near, every
aspect of understanding, modeling, and simulating the human brain
is accelerating: the price-performance and temporal and spatial
resolution of brain scanning, the amount of data and knowledge available
about brain function, and the sophistication of the models and simulations
of the brain's varied regions.
We already have a set of powerful tools that emerged from AI research
and that have been refined and improved over several decades of
development. The brain reverse-engineering project will greatly
augment this toolkit by also providing a panoply of new, biologically
inspired, self-organizing techniques. We will ultimately be able
to apply engineering's ability to focus and amplify human intelligence
vastly beyond the hundred trillion extremely slow interneuronal
connections that each of us struggles with today. Intelligence will
then be fully subject to the law of accelerating returns, which
is currently doubling the power of information technologies every
year.
An underlying problem with artificial intelligence that I have
personally experienced in my forty years in this area is that as
soon as an AI technique works, it's no longer considered AI and
is spun off as its own field (for example, character recognition,
speech recognition, machine vision, robotics, data mining, medical
informatics, automated investing).
Computer scientist Elaine Rich defines AI as "the study of how
to make computers do things at which, at the moment, people are
better." Rodney Brooks, director of the MIT AI Lab, puts it a different
way: "Every time we figure out a piece of it, it stops being magical;
we say, Oh, that's just a computation." I am also reminded
of Watson's remark to Sherlock Holmes, "I thought at first that
you had done something clever, but I see that there was nothing
in it after all."8 That has been our experience as AI
scientists. The enchantment of intelligence seems to be reduced
to "nothing" when we fully understand its methods. The mystery that
is left is the intrigue inspired by the remaining, not as yet understood
methods of intelligence.
AI's Toolkit
AI is the study of techniques for solving exponentially hard
problems in polynomial time by exploiting knowledge about the problem
domain. —Elaine Rich
It has only been recently that we have been able to obtain sufficiently
detailed models of how human brain regions function to influence
AI design. Prior to that, in the absence of tools that could peer
into the brain with sufficient resolution, AI scientists and engineers
developed their own techniques. Just as aviation engineers did not
model the ability to fly on the flight of birds, these early AI
methods were not based on reverse engineering natural intelligence.
A small sample of these approaches is reviewed here. Since their
adoption, they have grown in sophistication, which has enabled the
creation of practical products that avoid the fragility and high
error rates of earlier systems.
Expert systems. In the 1970s AI was often equated with one
specific method: expert systems. This involves the development of
specific logical rules to simulate the decision-making processes
of human experts. A key part of the procedure entails knowledge
engineers interviewing domain experts such as doctors and engineers
to codify their decision-making rules.
There were early successes in this area, such as medical diagnostic
systems that compared well to human physicians, at least in limited
tests. For example, a system called MYCIN, which was designed to
diagnose and recommend remedial treatment for infectious diseases,
was developed through the 1970s. In 1979 a team of expert evaluators
compared diagnosis and treatment recommendations by MYCIN to those
of human doctors and found that MYCIN did as well as or better than
any of the physicians.9
It became apparent from this research that human decision making
typically is based not on definitive logic rules but rather on "softer"
types of evidence. A dark spot on a medical imaging test may suggest
cancer, but other factors such as its exact shape, location, and
contrast are likely to influence a diagnosis. The hunches of human
decision making are usually influenced by combining many pieces
of evidence from prior experience, none definitive by itself. Often
we are not even consciously aware of many of the rules that we use.
By the late 1980s expert systems were incorporating the idea of
uncertainty and could combine many sources of probabilistic evidence
to make a decision. The MYCIN system pioneered this approach. A
typical MYCIN "rule" reads:
If the infection which requires therapy is meningitis, and the
type of the infection is fungal, and organisms were not seen on
the stain of the culture, and the patient is not a compromised host,
and the patient has been to an area that is endemic for coccidiomycoses,
and the race of the patient is Black, Asian, or Indian, and the
cryptococcal antigen in the csf test was not positive, THEN there
is a 50 percent chance that cryptococcus is not one of the organisms
which is causing the infection.
Although a single probabilistic rule such as this would not be
sufficient by itself to make a useful statement, by combining thousands
of such rules the evidence can be marshaled and combined to make
reliable decisions.
Probably the longest-running expert system project is CYC (for
enCYClopedic), created by Doug Lenat and his colleagues at Cycorp.
Initiated in 1984, CYC has been coding commonsense knowledge to
provide machines with an ability to understand the unspoken assumptions
underlying human ideas and reasoning. The project has evolved from
hard-coded logical rules to probabilistic ones and now includes
means of extracting knowledge from written sources (with human supervision).
The original goal was to generate one million rules, which reflects
only a small portion of what the average human knows about the world.
Lenat's latest goal is for CYC to master "100 million things, about
the number a typical person knows about the world by 1997."10
Another ambitious expert system is being pursued by Darryl Macer,
associate professor of Biological Sciences at the University of
Tsukuba in Japan. He plans to develop a system incorporating all
human ideas.11 One application would be to inform policy
makers of which ideas are held by which community.
Bayesian nets. Over the last decade a technique called Bayesian
logic has created a robust mathematical foundation for combining
thousands or even millions of such probabilistic rules in what are
called "belief networks" or Bayesian nets. Originally devised by
English mathematician Thomas Bayes, and published posthumously in
1763, the approach is intended to determine the likelihood of future
events based similar occurrences in the past.12 Many
expert systems based on Bayesian techniques gather data from experience
in an ongoing fashion, thereby continually learning and improving
their decision making.
The most promising type of spam filters are based on this method.
I personally use a spam filter called SpamBayes, which trains itself
on e-mail that you have identified as either "spam" or "okay."13
You start out by presenting a folder of each to the filter. It trains
its Bayesian belief network on these two files and analyzes the
patterns of each, thus enabling it to automatically move subsequent
e-mail into the proper category. It continues to train itself on
every subsequent e-mail, especially when it's corrected by the user.
This filter has made the spam situation manageable for me, which
is saying a lot, as it weeds out two hundred to three hundred spam
messages each day, letting over one hundred "good" messages through.
Only about 1 percent of the messages it identifies as "okay" are
actually spam; it almost never marks a good message as spam. The
system is almost as accurate as I would be and much faster.
Markov models. Another method that is good at applying probabilistic
networks to complex sequences of information involves Markov models.14
Andrei Andreyevich Markov (1856–1922), a renowned mathematician,
established a theory of "Markov chains," which was refined by Norbert
Wiener (1894–1964) in 1923. The theory provided a method to
evaluate the likelihood that a certain sequence of events would
occur. It has been popular, for example, in speech recognition,
in which the sequential events are phonemes (parts of speech). The
Markov models used in speech recognition code the likelihood that
specific patterns of sound are found in each phoneme, how the phonemes
influence each other, and likely orders of phonemes. The system
can also include probability networks on higher levels of language,
such as the order of words. The actual probabilities in the models
are trained on actual speech and language data, so the method is
self-organizing.
Markov modeling was one of the methods my colleagues and I used
in our own speech-recognition development.15 Unlike phonetic
approaches, in which specific rules about phoneme sequences are
explicitly coded by human linguists, we did not tell the system
that there are approximately forty-four phonemes in English, nor
did we tell it what sequences of phonemes were more likely than
others. We let the system discover these "rules" for itself from
thousands of hours of transcribed human speech data. The advantage
of this approach over hand-coded rules is that the models develop
subtle probabilistic rules of which human experts are not necessarily
aware.
Neural nets. Another popular self-organizing method that
has also been used in speech recognition and a wide variety of other
pattern-recognition tasks is neural nets. This technique involves
simulating a simplified model of neurons and interneuronal connections.
One basic approach to neural nets can be described as follows. Each
point of a given input (for speech, each point represents two dimensions,
one being frequency and the other time; for images, each point would
be a pixel in a two-dimensional image) is randomly connected to
the inputs of the first layer of simulated neurons. Every connection
has an associated synaptic strength, which represents its importance
and which is set at a random value. Each neuron adds up the signals
coming into it. If the combined signal exceeds a particular threshold,
the neuron fires and sends a signal to its output connection; if
the combined input signal does not exceed the threshold, the neuron
does not fire, and its output is zero. The output of each neuron
is randomly connected to the inputs of the neurons in the next layer.
There are multiple layers (generally three or more), and the layers
may be organized in a variety of configurations. For example, one
layer may feed back to an earlier layer. At the top layer, the output
of one or more neurons, also randomly selected, provides the answer
(For an algorithmic description of neural nets, see this endnote.16).
Since the neural-net wiring and synaptic weights are initially
set randomly, the answers of an untrained neural net will be random.
The key to a neural net, therefore, is that it must learn its subject
matter. Like the mammalian brains on which it's loosely modeled,
a neural net starts out ignorant. The neural net's teacher—which
may be a human, a computer program, or perhaps another, more mature
neural net that has already learned its lessons—rewards the
student neural net when it generates the right output and punishes
it when it does not. This feedback is in turn used by the student
neural net to adjust the strengths of each interneuronal connection.
Connections that were consistent with the right answer are made
stronger. Those that advocated a wrong answer are weakened. Over
time, the neural net organizes itself to provide the right answers
without coaching. Experiments have shown that neural nets can learn
their subject matter even with unreliable teachers. If the teacher
is correct only 60 percent of the time, the student neural net will
still learn its lessons.
A powerful, well-taught neural net can emulate a wide range of
human pattern-recognition faculties. Systems using multilayer neural
nets have shown impressive results in a wide variety of pattern-recognition
tasks, including recognizing handwriting, human faces, fraud in
commercial transactions such as credit-card charges, and many others.
In my own experience in using neural nets in such contexts, the
most challenging engineering task is not coding the nets but in
providing automated lessons for them to learn their subject matter.
The current trend in neural nets is to take advantage of more realistic
and more complex models of how actual biological neural nets work,
now that we are developing detailed models of neural functioning
from brain reverse engineering.17 Since we do have several
decades of experience in using self-organizing paradigms, new insights
from brain studies can be quickly adapted to neural-net experiments.
Neural nets are also naturally amenable to parallel processing,
since that is how the brain works. The human brain does not have
a central processor that simulates each neuron. Rather, we can consider
each neuron and each interneuronal connection to be an individual
slow processor. Extensive work is under way to develop specialized
chips that implement neural-net architectures in parallel to provide
substantially greater throughput.18
Genetic algorithms (GAs). Another self-organizing paradigm
inspired by nature is genetic, or evolutionary, algorithms, which
emulate evolution, including sexual reproduction and mutations.
Here is a simplified description of how they work. First, determine
a way to code possible solutions to a given problem. If the problem
is optimizing the design parameters for a jet engine, define a list
of the parameters (with a specific number of bits assigned to each
parameter). This list is regarded as the genetic code in the genetic
algorithm. Then randomly generate thousands or more genetic codes.
Each such genetic code (which represents one set of design parameters)
is considered a simulated "solution" organism.
Now evaluate each simulated organism in a simulated environment
by using a defined method to evaluate each set of parameters. This
evaluation is a key to the success of a genetic algorithm. In our
example, we would apply each solution organism to a jet-engine simulation
and determine how successful that set of parameters is, according
to whatever criteria we are interested in (fuel consumption, speed,
and so on). The best solution organisms (the best designs) are allowed
to survive, and the rest are eliminated.
Now have each of the survivors multiply themselves until they reach
the same number of solution creatures. This is done by simulating
sexual reproduction. In other words, each new offspring solution
draws part of its genetic code from one parent and another part
from a second parent. Usually no distinction is made between male
or female organisms; it's sufficient to generate an offspring from
two arbitrary parents. As they multiply, allow some mutation (random
change) in the chromosomes to occur.
We've now defined one generation of simulated evolution; now repeat
these steps for each subsequent generation. At the end of each generation
determine how much the designs have improved. When the improvement
in the evaluation of the design creatures from one generation to
the next becomes very small, we stop this iterative cycle of improvement
and use the best design(s) in the last generation .(For an algorithmic
description of genetic algorithms, see this endnote19).
The key to a GA is that the human designers don't directly program
a solution; rather, they let one emerge through an iterative process
of simulated competition and improvement. As we discussed, biological
evolution is smart but slow, so to enhance its intelligence we retain
its discernment while greatly speeding up its ponderous pace. The
computer is fast enough to simulate many generations in a matter
of hours or days or weeks. But we have to go through this iterative
process only once; once we have let this simulated evolution run
its course, we can apply the evolved and highly refined rules to
real problems in a rapid fashion.
Like neural nets GAs are a way to harness the subtle but profound
patterns that exist in chaotic data. A key requirement for their
success is a valid way of evaluating each possible solution. This
evaluation needs to be fast because it must take account of many
thousands of possible solutions for each generation of simulated
evolution.
GAs are adept at handling problems with too many variables to compute
precise analytic solutions. The design of a jet engine, for example,
involves more than one hundred variables and requires satisfying
dozens of constraints. GAs used by researchers at General Electric
were able to come up with engine designs that met the constraints
more precisely than conventional methods.
When using GAs you must, however, be careful what you ask for.
University of Sussex researcher Jon Bird used a GA to optimally
design an oscillator circuit. Several attempts generated conventional
designs using a small number of transistors, but the winning design
was not an oscillator at all but a simple radio circuit. Apparently
the GA discovered that the radio circuit picked up an oscillating
hum from a nearby computer.20 The GA's solution worked
only in the exact location on the table where it was asked to solve
the problem.
Genetic algorithms, part of the field of chaos or complexity theory,
are being increasingly used to solve otherwise intractable business
problems, such as optimizing complex supply chains. This approach
is beginning to supplant more analytic methods throughout industry.
(See examples below.) The paradigm is also adept at recognizing
patterns, and is often combined with neural nets and other self-organizing
methods. It's also a reasonable way to write computer software,
particularly software that needs to find delicate balances for competing
resources.
In the novel usr/bin/god, Cory Doctorow, a leading science-fiction
writer, uses an intriguing variation of a GA to evolve an AI. The
GA generates a large number of intelligent systems based on various
intricate combinations of techniques, with each combination characterized
by its genetic code. These systems then evolve using a GA.
The evaluation function works as follows: each system logs on to
various human chat rooms and tries to pass for a human, basically
a covert Turing test. If one of the humans in a chat room says something
like "What are you, a chatterbot?" (chatterbot meaning an automatic
program, that at today's level of development is expected to not
understand language at a human level), the evaluation is over, that
system ends its interactions, and reports its score to the GA. The
score is determined by how long it was able to pass for human without
being challenged in this way. The GA evolves more and more intricate
combinations of techniques that are increasingly capable of passing
for human.
The main difficulty with this idea is that the evaluation function
is fairly slow, although it will take an appreciable amount of time
only once the systems are reasonably intelligent. Also, the evaluations
can take place largely in parallel. It's an interesting idea and
may actually be a useful method to finish the job of passing the
Turing test, once we get to the point where we have sufficiently
sophisticated algorithms to feed into such a GA, so that evolving
a Turing-capable AI is feasible.
Recursive search. Often we need to search through vast number
of combinations of possible solutions to solve a given problem.
A classic example is in playing games such as chess. As a player
considers her next move, she can list all of her possible moves,
and then, for each such move, all possible countermoves by the opponent,
and so on. It is difficult, however, for human players to keep a
huge "tree" of move-countermove sequences in their heads, and so
they rely on pattern recognition—recognizing situations based
on prior experience—whereas machines use logical analysis of
millions of moves and countermoves.
Such a logical tree is at the heart of most game-playing programs.
Consider how this is done. We construct a program called Pick Best
Next Step to select each move. Pick Best Next Step starts by listing
all of the possible moves from the current state of the board. (If
the problem was solving a mathematical theorem, rather than game
moves, the program would list all of the possible next steps in
a proof.) For each move the program constructs a hypothetical board
that reflects what would happen if we made this move. For each such
hypothetical board, we now need to consider what our opponent would
do if we made this move. Now recursion comes in, because Pick Best
Next Step simply calls Pick Best Next Step (in other words, itself)
to pick the best move for our opponent. In calling itself, Pick
Best Next Step then lists all of the legal moves for our opponent.
The program keeps calling itself, looking ahead as many moves as
we have time to consider, which results in the generation of a huge
move-countermove tree. This is another example of exponential growth,
because to look ahead an additional move (or countermove) requires
multiplying the amount of available computation by about five. Key
to the success of the recursive formula is pruning this huge tree
of possibilities and ultimately stopping its growth. In the game
context, if a board looks hopeless for either side, the program
can stop the expansion of the move-countermove tree from that point
(called a "terminal leaf" of the tree) and consider the most recently
considered move to be a likely win or loss. When all of these nested
program calls are completed, the program will have determined the
best possible move for the current actual board within the limits
of the depth of recursive expansion that it had time to pursue,
and the quality of its pruning algorithm (For an algorithmic description
of recursive search, see this endnote.21).
The recursive formula is often effective at mathematics. Rather
than game moves, the "moves" are the axioms of the field of math
being addressed, as well as previously proved theorems. The expansion
at each point is the possible axioms (or previously proved theorems)
that can be applied to a proof at each step. (This was the approach
used by Newell, Shaw, and Simons's General Problem Solver.)
From these examples it may appear that recursion is well suited
only for problems in which we have crisply defined rules and objectives.
But it has also shown promise in computer generation of artistic
creations. For example, a program I designed called Ray Kurzweil's
Cybernetic Poet uses a recursive approach.22 The program
establishes a set of goals for each word—achieving a certain
rhythmic pattern, poem structure, and word choice that is desirable
at that point in the poem. If the program is unable to find a word
that meets these criteria, it backs up and erases the previous word
it has written, reestablishes the criteria it had originally set
for the word just erased, and goes from there. If that also leads
to a dead end, it backs up again, thus moving backwards and forwards.
Eventually, it forces itself to make up its mind by relaxing some
of the constraints if all paths lead to dead ends.
Deep Fritz Draws: Are Humans Getting Smarter,
or Are Computers Getting Stupider?
We find one example of qualitative improvements in software
in the world of computer chess, which, according to common
wisdom, is governed only by the brute-force expansion of computer
hardware. In a chess tournament in October 2002 with top-ranking
human player Vladimir Kramnik, the Deep Fritz software achieved
a draw. I point out that Deep Fritz has available only about
1.3 percent of the brute-force computation as the previous
computer champion, Deep Blue. Despite that, it plays chess
at about the same level because of its superior pattern recognition–based
pruning algorithm (see below). In six years a program like
Deep Fritz will again achieve Deep Blue's ability to analyze
two hundred million board positions per second.
Deep Fritz–like chess programs running on ordinary personal
computers will routinely defeat all humans later in this decade.
In The Age of Intelligent Machines, which I wrote
between 1986 and 1989, I predicted that a computer would defeat
the human world chess champion by the end of the 1990s. I
also noted that computers were gaining about forty-five points
per year in their chess ratings, whereas the best human playing
was essentially fixed, so this projected a crossover point
in 1998. Indeed, Deep Blue did defeat Gary Kasparov in a highly
publicized tournament in 1997.
Yet in the Deep Fritz–Kramnik match, the current reigning
computer program was able to achieve only a tie. Five years
had passed since Deep Blue's victory, so what are we to make
of this situation? Should we conclude that:
1. Humans are getting smarter, or at least better at chess?
2. Computers are getting worse at chess? If so, should we
conclude that the much- publicized improvement in computer
speed over the past five years was not all it was cracked
up to be? Or, that computer software is getting worse, at
least in chess?
The specialized-hardware advantage. Neither of the
above conclusions is warranted. The correct conclusion is
that software is getting better because Deep Fritz essentially
matched the performance of Deep Blue, yet with far smaller
computational resources. To gain some insight into these questions,
we need to examine a few essentials. When I wrote my predictions
of computer chess in the late 1980s, Carnegie Mellon University
was embarked on a program to develop specialized chips for
conducting the "minimax" algorithm (the standard game-playing
method that relies on building trees of move-countermove sequences,
and then evaluating the terminal-leaf positions of each branch
of the tree) specifically for chess moves.
Based on this specialized hardware CMU's 1988 chess machine,
HiTech, was able to analyze 175,000 board positions per second.
It achieved a chess rating of 2,359, only about 440 points
below the human world champion.
A year later, in 1989, CMU's Deep Thought machine increased
this capacity to one million board positions per second and
achieved a rating of 2,400. IBM eventually took over the project
and renamed it Deep Blue but kept the basic CMU architecture.
The version of Deep Blue that defeated Kasparov in 1997 had
256 special-purpose chess processors working in parallel,
which analyzed two hundred million board positions per second.
It is important to note the use of specialized hardware to
accelerate the specific calculations needed to generate the
minimax algorithm for chess moves. It's well-known to computer-systems
designers that specialized hardware generally can implement
a specific algorithm at least one hundred times faster than
a general-purpose computer. Specialized ASICs (Application-specific
Integrated Circuits) require significant development efforts
and costs, but for critical calculations that are needed on
a repetitive basis (for example, decoding MP3 files or rendering
graphics primitives for video games), this expenditure can
be well worth the investment.
Deep Blue versus Deep Fritz. Because there had always
been a great deal of focus on the milestone of a computer's
being able to defeat a human opponent, support was available
for investing in special-purpose chess circuits. Although
there was some lingering controversy regarding the parameters
of the Deep Blue–Kasparov match, the level of interest
in computer chess waned considerably after 1997. After all,
the goal had been achieved, and there was little point in
beating a dead horse. IBM canceled work on the project, and
there has been no work on specialized chess chips since that
time. The focus of research in the various domains spun out
of AI has been placed instead on problems of greater consequence,
such as guiding airplanes, missiles, and factory robots, understanding
natural language, diagnosing electrocardiograms and blood-cell
images, detecting credit-card fraud, and a myriad of other
successful narrow AI applications.
Computer hardware has nonetheless continued its exponential
increase, with personal-computer speeds doubling every year
since 1997. Thus the general-purpose Pentium processors used
by Deep Fritz are about thirty-two times faster than processors
in 1997. Deep Fritz uses a network of only eight personal
computers, so the hardware is equivalent to 256 1997-class
personal computers. Compare that to Deep Blue, which used
256 specialized chess processors, each of which was about
one hundred times faster than 1997 personal computers (of
course, only for computing chess minimax). So Deep Blue was
25,600 times faster than a 1997 PC and one hundred times faster
than Deep Fritz. This analysis is confirmed by the reported
speeds of the two systems: Deep Blue can analyze 200 million
board positions per second compared to only about 2.5 million
for Deep Fritz.
Significant software gains. So what can we say about
the software in Deep Fritz? Although chess machines are usually
referred to as examples of brute-force calculation, there
is one important aspect of these systems that does require
qualitative judgment. The combinatorial explosion of possible
move-countermove sequences is rather formidable.
In The Age of Intelligent Machines I estimated that
it would take about forty billion years to make one move if
we failed to prune the move-countermove tree and attempted
to make a "perfect" move in a typical game. (Assuming about
thirty moves each in a typical game and about eight possible
moves per play, we have 830 possible move sequences;
analyzing one billion move sequences per second would take
1018 seconds or forty billion years.) Thus a practical
system needs to continually prune away unpromising lines of
action. This requires insight and is essentially a pattern-recognition
judgment.
Humans, even world-class chess masters, perform the minimax
algorithm extremely slowly, generally performing less than
one move-countermove analysis per second. So how is it that
a chess master can compete at all with computer systems? The
answer is that we possess formidable powers of pattern recognition,
which enable us to prune the tree with great insight.
It's precisely in this area that Deep Fritz has improved
considerably over Deep Blue. Deep Fritz has only slightly
more computation available than CMU's Deep Thought yet is
rated almost 400 points higher.
Are human chess players doomed? Another prediction
I made in The Age of Intelligent Machines was that
once computers did perform as well or better as humans in
chess, we would either think more of computer intelligence,
less of human intelligence, or less of chess, and that if
history is a guide, the last of these would be the likely
outcome. Indeed, that is precisely what happened. Soon after
Deep Blue's victory we began to hear a lot about how chess
is really just a simple game of calculating combinations and
that the computer victory just demonstrated that it was a
better calculator.
The reality is slightly more complex. The ability of humans
to perform well in chess is clearly not due to our calculating
prowess, at which we are in fact rather poor. We use instead
a quintessentially human form of judgment. For this type of
qualitative judgment, Deep Fritz represents genuine progress
over earlier systems. (Incidentally, humans have made no progress
in the last five years, with the top human scores remaining
just below 2,800. Kasparov is rated at 2,795 and Kramnik at
2,794.)
Where we go from here? Now that computer chess is relying
on software running on ordinary personal computers, chess
programs will continue to benefit from the ongoing acceleration
of computer power. By 2009 a program like Deep Fritz will
again achieve Deep Blue's ability to analyze two hundred million
board positions per second. With the opportunity to harvest
computation on the Internet, we will be able to achieve this
potential several years sooner than 2009. (Internet harvesting
of computers will require more ubiquitous broadband communication,
but that's coming, too).
With these inevitable speed increases, as well as ongoing
improvements in pattern recognition, computer chess ratings
will continue to edge higher. Deep Fritz–like programs
running on ordinary personal computers will routinely defeat
all humans later in this decade. Then we'll really lose interest
in chess.
|
Combining methods. The most powerful approach to building
robust AI systems is to combine approaches, which is how the human
brain works. As we discussed, the brain is not one big neural net
but instead consists of hundreds of regions, each of which is optimized
for processing information in a different way. None of these regions
by itself operates at what we would consider human levels of performance,
but clearly by definition the overall system does exactly that.
I've used this approach in my own AI work, especially in pattern
recognition. In speech recognition, for example, we implemented
a number of different pattern-recognition systems based on different
paradigms. Some were specifically programmed with knowledge of phonetic
and linguistic constraints from experts. Some were based on rules
to parse sentences (which involves creating sentence diagrams showing
word usage, similar to the diagrams taught in grade school). Some
were based on self-organizing techniques, such as Markov models,
trained on extensive libraries of recorded and annotated human speech.
We then programmed a software "expert manager" to learn the strengths
and weaknesses of the different "experts" (recognizers) and combine
their results in optimal ways. In this fashion, a particular technique
that by itself might produce unreliable results can nonetheless
contribute to increasing the overall accuracy of the system.
There are many intricate ways to combine the varied methods in
AI's toolbox. For example, one can use a genetic algorithm to evolve
the optimal topology (organization of nodes and connections) for
a neural net or a Markov model. The final output of the GA-evolved
neural net can then be used to control the parameters of a recursive
search algorithm. We can add in powerful signal- and image-processing
techniques that have been developed for pattern-processing systems.
Each specific application calls for a different architecture. Computer
science professor and AI entrepreneur Ben Goertzel has written a
series of books and articles that describe strategies and architectures
for combining the diverse methods underlying intelligence. His "Novamente"
architecture is intended to provide a framework for general purpose
AI.23
The above basic descriptions provide only a glimpse into how increasingly
sophisticated current AI systems are designed. It's beyond the scope
of this book to provide a comprehensive description of the techniques
of AI, and even a doctoral program in computer science is unable
to cover all of the varied approaches in use today.
Many of the examples of real-world narrow AI systems described
in the next section use a variety of methods integrated and optimized
for each particular task. Narrow AI is strengthening as a result
of several concurrent trends: continued exponential gains in computational
resources, extensive real-world experience with thousands of applications,
and fresh insights into how the human brain makes intelligent decisions.
A Narrow AI Sampler
When I wrote my first AI book, The Age of Intelligent Machines
in the late 1980s, I had to conduct extensive investigations to
find a few successful examples of AI in practice. The Internet was
not yet prevalent, so I had to go to real libraries and visit the
AI research centers in the United States, Europe, and Asia. I included
in my book pretty much all of the reasonable examples I could identify.
In my research for this book my experience has been altogether different.
I have been inundated with thousands of compelling examples. In
our reporting on the KurzweilAI.net Web site, we feature several
dramatic systems every day.24
A 2003 study by Business Communication Company projected a $21
billion market by 2007 for AI applications, with average annual
growth of 12.2 percent from 2002 to 2007.25 Leading industries
for AI applications include business intelligence, customer relations,
finance, defense and domestic security, and education. Here is a
small sample of narrow AI in action.
Military and intelligence. The U.S. military has been an
avid user of AI systems. Pattern-recognition software systems guide
autonomous weapons such as cruise missiles, which can fly thousands
of miles to find a specific building or even a specific window.26
Although the relevant details of the terrain that the missile flies
over are programmed ahead of time, variations in weather, ground
cover, and other factors require a flexible level of real-time image
recognition.
The army has developed prototypes of self-organizing communication
networks (called "mesh networks") to automatically configure many
thousands of communication nodes when a platoon is dropped into
a new location.27
Expert systems incorporating Bayesian networks and GAs are used
to optimize complex supply chains that coordinate millions of provisions,
supplies, and weapons based on rapidly changing battlefield requirements.
AI systems are routinely employed to simulate the performance of
weapons, including nuclear bombs and missiles.
Advance warning of the September 11, 2001, terrorist attacks was
apparently detected by the National Security Agency's AI-based Echelon
system, which analyzes the agency's extensive monitoring of communications
traffic.28 Unfortunately, Echelon's warnings were not
reviewed by human agents until it was too late.
The 2002 military campaign in Afghanistan saw the debut of the
armed Predator, an unmanned robotic flying fighter. Although the
Air Force's Predator had been under development for many years,
arming it with Army-supplied missiles was a last-minute improvisation
that proved remarkably successful. In the Iraq war that began in
2003 the armed Predator (operated by the CIA) and other flying unmanned
aerial vehicles (UAVs) destroyed thousands of enemy tanks and missile
sites.
All of the military services are using robots. The army utilizes
them to search caves (in Afghanistan) and buildings. The navy uses
small robotic ships to protect its aircraft carriers. As I discuss
in chapter 6 of The Singularity is Near, moving soldiers
away from battle is a rapidly growing trend.
Space exploration. NASA is building self-understanding into
the software controlling its unmanned spacecraft. Because Mars is
about three light-minutes from Earth, and Jupiter around forty light-minutes
(depending on the exact position of the planets), communication
between spacecraft headed there and earthbound controllers is significantly
delayed. For this reason it's important that the software controlling
these missions have the capability of performing its own tactical
decision making. To accomplish this NASA software is being designed
to include a model of the software's own capabilities and those
of the spacecraft, as well as the challenges each mission is likely
to encounter. Such AI-based systems are capable of reasoning through
new situations rather than just following preprogrammed rules. This
approach enabled the craft Deep Space One in 1999 to use its own
technical knowledge to devise a series of original plans to overcome
a stuck switch that threatened to destroy its mission of exploring
an asteroid.29 The AI system's first plan failed to work,
but its second plan saved the mission. "These systems have a commonsense
model of the physics of their internal components," explains Brian
Williams, coinventor of Deep Space One's autonomous software and
now a scientist at MIT's Space Systems and AI laboratories. "[The
spacecraft] can reason from that model to determine what is wrong
and to know how to act."
Using a network of computers NASA used GAs to evolve an antenna
design for three Space Technology 5 satellites that will study the
Earth's magnetic field. Millions of possible designs competed in
the simulated evolution. According to NASA scientist and project
leader Jason Lohn, "We are now using the [GA] software to design
tiny microscopic machines, including gyroscopes, for spaceflight
navigation. The software also may invent designs that no human designer
would ever think of."30
Another NASA AI system learned on its own to distinguish stars
from galaxies in very faint images with an accuracy surpassing that
of human astronomers.
New land-based robotic telescopes are able to make their own decisions
on where to look and how to optimize the likelihood of finding desired
phenomena. Called "autonomous, semi-intelligent observatories,"
the systems can adjust to the weather, notice items of interest,
and decide on their own to track them. They are able to detect very
subtle phenomena, such as a star blinking for a nanosecond, which
may indicate a small asteroid in the outer regions of our solar
system passing in front of the light from that star.31
One such system, called Moving Object and Transient Event Search
System (MOTESS) has identified on its own 180 new asteroids and
several comets during its first two years of operation. "We have
an intelligent observing system," explained University of Exeter
astronomer Alasdair Allan. "It thinks and reacts for itself, deciding
whether something it has discovered is interesting enough to need
more observations. If more observations are needed, it just goes
ahead and gets them."
Similar systems are used by the military to automatically analyze
data from spy satellites. Current satellite technology is capable
of observing ground-level features about an inch in size and is
not affected by bad weather, clouds, or darkness.32 The
massive amount of data continually generated would not be manageable
without automated image recognition programmed to look for relevant
developments.
Medicine. If you obtain an electrocardiogram (ECG) your
doctor is likely to receive an automated diagnosis using pattern
recognition applied to ECG recordings. My own company (Kurzweil
Technologies) is working with United Therapeutics to develop a new
generation of automated ECG analysis for long-term unobtrusive monitoring
(via sensors embedded in clothing and wireless communication using
a cell phone) of the early warning signs of heart disease.33
Other pattern-recognition systems are used to diagnose a variety
of imaging data.
Every major drug developer is using AI programs to do pattern recognition
and intelligent data mining in the development of new drug therapies.
For example SRI International is building flexible knowledge bases
that encode everything we know about a dozen disease agents, including
tuberculosis and H. pylori (the bacteria that cause ulcers).34
The goal is to apply intelligent data-mining tools (software that
can search for new relationships in data) to find new ways to kill
or disrupt the metabolisms of these pathogens.
Similar systems are being applied to performing the automatic discovery
of new therapies for other diseases, as well as understanding the
function of genes and their roles in disease.35 For example
Abbott Laboratories claims that six human researchers in one of
its new labs equipped with AI-based robotic and data-analysis systems
are able to match the results of two hundred scientists in its older
drug-development labs.36
Men with elevated prostate-specific antigen (PSA) levels typically
undergo surgical biopsy, but about 75 percent of these men do not
have prostate cancer. A new test, based on pattern recognition of
proteins in the blood, would reduce this false positive rate to
about 29 percent.37 The test is based on an AI program
designed by Correlogic Systems in Bethesda, Maryland, and the accuracy
is expected to improve further with continued development.
Pattern recognition applied to protein patterns has also been used
in the detection of ovarian cancer. The best contemporary test for
ovarian cancer, called CA-125, employed in combination with ultrasound,
misses almost all early-stage tumors. "By the time it is now diagnosed,
ovarian cancer is too often deadly," says Emanuel Petricoin III,
codirector of the Clinical Proteomics Program run by the FDA and
the National Cancer Institute. Petricoin is the lead developer of
a new AI-based test looking for unique patterns of proteins found
only in the presence of cancer. In an evaluation involving hundreds
of blood samples, the test was, according to Petricoin, "an astonishing
100% accurate in detecting cancer, even at the earliest stages."38
About 10 percent of all Pap-smear slides in the United States are
analyzed by a self-learning AI program called FocalPoint, developed
by TriPath Imaging. The developers started out by interviewing pathologists
on the criteria they use. The AI system then continued to learn
by watching expert pathologists. Only the best human diagnosticians
were allowed to be observed by the program. "That's the advantage
of an expert system," explains Bob Schmidt, TriPath's technical
product manager. "It allows you to replicate your very best people."
Ohio State University Health System has developed a computerized
physician order-entry (CPOE) system based on an expert system with
extensive knowledge across multiple specialties.39 The
system automatically checks every order for possible allergies in
the patient, drug interactions, duplications, drug restrictions,
dosing guidelines, and appropriateness given information about the
patient from the hospital's laboratory and radiology departments.
Science and math. A "robot scientist" has been developed
at the University of Wales that combines an AI-based system capable
of formulating original theories, a robotic system that can automatically
carry out experiments, and a reasoning engine to evaluate results.
The researchers provided their creation with a model of gene expression
in yeast. The system "automatically originates hypotheses to explain
observations, devises experiments to test these hypotheses, physically
runs the experiments using a laboratory robot, interprets the results
to falsify hypotheses inconsistent with the data, and then repeats
the cycle."40 The system is capable of improving its
performance by learning from its own experience. The experiments
designed by the robot scientist were three times less expensive
than those designed by human scientists. A test of the machine against
a group of human scientists showed that the discoveries made by
the machine were comparable to those made by the humans.
Mike Young, director of biology at the University of Wales, was
one of the human scientists who lost to the machine. He explains
that "the robot did beat me, but only because I hit the wrong key
at one point."
A long-standing conjecture in algebra was finally proved by an
AI system at Argonne National Laboratory. Human mathematicians called
the proof "creative."
Business, finance, and manufacturing. Companies in every
industry are using AI systems to control and optimize logistics,
detect fraud and money laundering, and perform intelligent data
mining on the horde of information they gather each day. Wal-Mart,
for example, gathers vast amounts of information from its transactions
with shoppers. AI-based tools using neural nets and expert systems
review this data to provide market-research reports for managers.
This intelligent data mining allows them to make remarkably accurate
predictions of the inventory required for each product in each store
for each day.41
AI-based programs are routinely used to detect fraud in financial
transactions. Future Route, an English company, for example, offers
iHex, based on AI routines developed at Oxford University, to detect
fraud in credit-card transactions and loan applications.42
The system continuously generates and updates its own rules based
on its experience. First Union Home Equity Bank in Charlotte, North
Carolina, uses Loan Arranger, a similar AI-based system to decide
whether to approve mortgage applications.43
The NASDAQ similarly uses a learning program called the Securities
Observation, News Analysis, and Regulation (SONAR) system to monitor
all trades for fraud as well as the possibility of insider trading.44
As of the end of 2003 more than 180 incidents had been detected
by SONAR and referred to the U.S. Securities and Exchange Commission
and Department of Justice. These included several cases that later
received significant news coverage.
Ascent Technology, founded by Patrick Winston, who directed MIT's
AI Lab from 1972 through 1997, has designed an GA-based system called
SmartAirport Operations Center (SAOC) that can optimize the complex
logistics of an airport, such as balancing work assignments of hundreds
of employees, making gate and equipment assignments, and managing
a myriad of other details.45 Winston points out that
"figuring out ways to optimize a complicated situation is what genetic
algorithms do." SAOC has raised productivity by approximately 30
percent in the airports where it has been implemented.
Ascent's first contract was to apply its AI techniques to managing
the logistics for the 1991 Desert Storm campaign in Iraq. DARPA
claimed that AI-based logistic-planning systems, including the Ascent
system, resulted in more savings than the entire government research
investment in AI over several decades.46
A recent trend in software is for AI systems to monitor a complex
software system's performance, recognize malfunctions, and determine
the best way to recover automatically without necessarily informing
the human user. The idea stems from the realization that as software
systems become more complex, like humans, they will never be perfect,
and that eliminating all bugs is impossible. As humans, we use the
same strategy: we don't expect to be perfect, but we usually try
to recover from inevitable mistakes. "We want to stand this notion
of systems management on its head," says Armando Fox, the head of
Stanford University's Software Infrastructures Group, who is working
on what is now called "autonomic computing." Fox adds, "The system
has to be able to set itself up, it has to optimize itself. It has
to repair itself, and if something goes wrong, it has to know how
to respond to external threats." IBM, Microsoft, and other software
vendors are all developing systems that incorporate autonomic capabilities.
Manufacturing and robotics. Computer-integrated manufacturing
(CIM) increasingly employs AI techniques to optimize the use of
resources, streamline logistics, and reduce inventories through
just-in-time purchasing of parts and supplies. A new trend in CIM
systems is to use "case-based reasoning" rather than hard-coded,
rule-based expert systems. Such reasoning codes knowledge as "cases,"
which are examples of problems with solutions. Initial cases are
usually designed by the engineers, but the key to a successful case-based
reasoning system is its ability to gather new cases from actual
experience. The system is then able to apply the reasoning from
its stored cases to new situations.
Robots are extensively used in manufacturing. The latest generation
of robots uses flexible AI-based machine-vision systems—from
companies such as Cognex Corporation in Natick, Massachusetts—that
can respond flexibly to varying conditions. This reduces the need
for precise setup for the robot to operate correctly. Brian Carlisle,
CEO of Adept Technologies, a Livermore, California, factory-automation
company, points out that "even if labor costs were eliminated [as
a consideration], a strong case can still be made for automating
with robots and other flexible automation. In addition to quality
and throughput, users gain by enabling rapid product changeover
and evolution that can't be matched with hard tooling."
One of AI's leading roboticists, Hans Moravec, has founded a company
called Seegrid to apply his machine-vision technology to applications
in manufacturing, materials handling, and military missions.47
Moravec's software enables a device (a robot or just a material-handling
cart) to walk or roll through an unstructured environment and in
a single pass build a reliable "voxel" (three-dimensional pixel)
map of the environment. The robot can then use the map and its own
reasoning ability to determine an optimal and obstacle-free path
to carry out its assigned mission.
This technology enables autonomous carts to transfer materials
throughout a manufacturing process without the high degree of preparation
required with conventional preprogrammed robotic systems. In military
situations autonomous vehicles could carry out precise missions
while adjusting to rapidly changing environments and battlefield
conditions.
Machine vision is also improving the ability of robots to interact
with humans. Using small, inexpensive cameras, head- and eye-tracking
software can sense where a human user is, allowing robots, as well
as virtual personalities on a screen, to maintain eye contact, a
key element for natural interactions. Head- and eye-tracking systems
have been developed at Carnegie Mellon University and MIT and are
offered by small companies such as Seeing Machines of Australia.
An impressive demonstration of machine vision was a vehicle that
was driven by an AI system with no human intervention for almost
the entire distance from Washington, D.C., to San Diego.48
Bruce Buchanan, computer-science professor at the University of
Pittsburgh and president of the American Association of Artificial
Intelligence, pointed out that this feat would have been "unheard
of 10 years ago."
Palo Alto Research Center (PARC) is developing a swarm of robots
that can navigate in complex environments, such as a disaster zone,
and find items of interest, such as humans who may be injured. In
a September 2004 demonstration at an AI conference in San Jose,
they demonstrated a group of self-organizing robots on a mock but
realistic disaster area.49 The robots moved over the
rough terrain, communicated with one another, used pattern recognition
on images, and detected body heat to locate humans.
Speech and language. Dealing naturally with language is
the most challenging task of all for artificial intelligence. No
simple tricks, short of fully mastering the principles of human
intelligence, will allow a computerized system to convincingly emulate
human conversation, even if restricted to just text messages. This
was Turing's enduring insight in designing his eponymous test based
entirely on written language..
Although not yet at human levels, natural language-processing systems
are making solid progress. Search engines have become so popular
that "Google" has gone from a proper noun to a common verb, and
its technology has revolutionized research and access to knowledge.
Google and other search engines use AI-based statistical-learning
methods and logical inference to determine the ranking of links.
The most obvious failing of these search engines is their inability
to understand the context of words. Although an experienced user
learns how to design a string of keywords to find the most relevant
sites (for example, a search for "computer chip" is likely to avoid
references to potato chips that a search for "chip" alone might
turn up) what we would really like to be able to do is converse
with our search engines in natural language. Microsoft has developed
a natural-language search engine called Ask MSR (Ask MicroSoft Research),
which actually answers natural-language questions such as "When
was Mickey Mantle born?"50 After the system parses the
sentence to determine the parts of speech (subject, verb, object,
adjective and adverb modifiers, and so on), a special search engine
then finds matches based on the parsed sentence. The found documents
are searched for sentences that appear to answer the question, and
the possible answers are ranked. At least 75 percent of the time,
the correct answer is in the top three ranked positions, and incorrect
answers are usually obvious (such as "Mickey Mantle was born in
3"). The researchers hope to include knowledge bases that will lower
the rank of many of the nonsensical answers.
Microsoft researcher Eric Brill, who has led research on Ask MSR,
has also attempted an even more difficult task: building a system
that provides answers of about fifty words to more complex questions,
such as, "How are the recipients of the Nobel Prize selected?" One
of the strategies used by this system is to find an appropriate
FAQ section on the Web that answers the query.
Natural-language systems combined with large-vocabulary, speaker-independent
(that is, responsive to any speaker) speech recognition over the
phone are entering the marketplace to conduct routine transactions.
You can talk to British Airways' virtual travel agent about anything
you like as long as it has to do with booking flights on British
Airways.51 You're also likely to talk to a virtual person
if you call Verizon for customer service or Charles Schwab and Merrill
Lynch to conduct financial transactions. These systems, while they
can be annoying to some people, are reasonably adept at responding
appropriately to the often ambiguous and fragmented way people speak.
Microsoft and other companies are offering systems that allow a
business to create virtual agents to book reservations for travel
and hotels and conduct routine transactions of all kinds through
two-way, reasonably natural voice dialogues.
Not every caller is satisfied with the ability of these virtual
agents to get the job done, but most systems provide a means to
get a human on the line. Companies using these systems report that
they reduce the need for human service agents up to 80 percent.
Aside from the money saved, reducing the size of call centers has
management benefit. Call-center jobs have very high turnover rates
because of low job satisfaction.
It's said that men are loath to ask others for directions, but
car vendors are betting that both male and female drivers will be
willing to ask their own car for help in getting to their destination.
In 2005 the Acura RL and Honda Odyssey will be offering a system
from IBM that allows users to converse with their cars.52
Driving directions will include street names (for example, "turn
left on Main Street, then right on Second Avenue"). Users can ask
such questions as, "Where is the nearest Italian restaurant?" or
they can enter specific locations by voice, ask for clarifications
on directions, and give commands to the car itself (such as "turn
up the air conditioning"). The Acura RL will also track road conditions
and highlight traffic congestion on its screen in real time. The
speech recognition is claimed to be speaker-independent and to be
unaffected by engine sound, wind, and other noises. The system will
reportedly recognize 1.7 million street and city names, in addition
to nearly one thousand commands.
Computer language translation continues to improve gradually. Because
this is a Turing-level task—that is, it requires full human-level
understanding of language to perform at human levels—it will
be one of the last application areas to compete with human performance.
Franz Josef Och, a computer scientist at the University of Southern
California, has developed a technique that can generate a new language-translation
system between any pair of languages in a matter of hours or days.53
All he needs is a "Rosetta stone"—that is, text in one language
and the translation of that text in the other language—although
he needs millions of words of such translated text. Using a self-organizing
technique, the system is able to develop its own statistical models
of how text is translated from one language to the other and develops
these models in both directions.
This contrasts with other translation systems, in which linguists
painstakingly code grammar rules with long lists of exceptions to
each rule. Och's system recently received the highest score in a
competition of translation systems conducted by the U.S. Commerce
Department's National Institute of Standards and Technology.
Entertainment and sports. In an amusing and intriguing application
of GAs, Oxford scientist Torsten Reil created animated creatures
with simulated joints and muscles and a neural net for a brain.
He then assigned them a task: to walk. He used a GA to evolve this
capability, which involved seven hundred parameters. "If you look
at that system with your human eyes, there's no way you can do it
on your own, because the system is just too complex," Reil points
out. "That's where evolution comes in."54
While some of the evolved creatures walked in a smooth and convincing
way, the research demonstrated a well-known attribute of GAs: you
get what you ask for. Some creatures figured out novel new ways
of passing for walking. According to Weil, "We got some creatures
that didn't walk at all, but had these very strange ways of moving
forward: crawling or doing somersaults."
Software is being developed that can automatically extract excerpts
from a video of a sports game that show the more important plays.55
A team at Trinity College in Dublin is working on table-based games
like pool, in which software tracks the location of each ball and
is programmed to identify when a significant shot has been made.
A team at the University of Florence is working on soccer. This
software tracks the location of each player and can determine the
type of play being made (such as free kicking or attempting a goal),
when a goal is achieved, when a penalty is earned, and other key
events.
The Digital Biology Interest Group at University College in London
is designing Formula One race cars by breeding them using GAs.56
The AI winter is long since over. We are well into the spring of
narrow AI. Most of the examples above were research projects just
ten to fifteen years ago. If all the AI systems in the world suddenly
stopped functioning, our economic infrastructure would grind to
a halt. Your bank would cease doing business. Most transportation
would be crippled. Most communications would fail. This was not
the case a decade ago. Of course, our AI systems are not smart enough—yet—to
organize such a conspiracy.
Strong AI
If you understand something in only one way, then you don't
really understand it at all. This is because, if something goes
wrong, you get stuck with a thought that just sits in your mind
with nowhere to go. The secret of what anything means to us depends
on how we've connected it to all the other things we know. This
is why, when someone learns 'by rote,' we say that they don't
really understand. However, if you have several different representations
then, when one approach fails you can try another. Of course,
making too many indiscriminate connections will turn a mind to
mush. But well-connected representations let you turn ideas around
in your mind, to envision things from many perspectives until
you find one that works for you. And that's what we mean by thinking!
—Marvin Minsky57
Advancing computer performance is like water slowly flooding
the landscape. A half century ago it began to drown the lowlands,
driving out human calculators and record clerks, but leaving most
of us dry. Now the flood has reached the foothills, and our outposts
there are contemplating retreat. We feel safe on our peaks, but,
at the present rate, those too will be submerged within another
half century. I propose that we build Arks as that day nears,
and adopt a seafaring life! For now, though, we must rely on our
representatives in the lowlands to tell us what water is really
like.
Our representatives on the foothills of chess and theorem-proving
report signs of intelligence. Why didn't we get similar reports
decades before, from the lowlands, as computers surpassed humans
in arithmetic and rote memorization? Actually, we did, at the
time. Computers that calculated like thousands of mathematicians
were hailed as "giant brains," and inspired the first
generation of AI research. After all, the machines were doing
something beyond any animal, that needed human intelligence, concentration
and years of training. But it is hard to recapture that magic
now. One reason is that computers' demonstrated stupidity in other
areas biases our judgment. Another relates to our own ineptitude.
We do arithmetic or keep records so painstakingly and externally,
that the small mechanical steps in a long calculation are obvious,
while the big picture often escapes us. Like Deep Blue's builders,
we see the process too much from the inside to appreciate the
subtlety that it may have on the outside. But there is a non-obviousness
in snowstorms or tornadoes that emerge from the repetitive arithmetic
of weather simulations, or in rippling tyrannosaur skin from movie
animation calculations. We rarely call it intelligence, but "artificial
reality" may be an even more profound concept than artificial
intelligence (Moravec 1998).
The mental steps underlying good human chess playing and theorem
proving are complex and hidden, putting a mechanical interpretation
out of reach. Those who can follow the play naturally describe
it instead in mentalistic language, using terms like strategy,
understanding and creativity. When a machine manages to be simultaneously
meaningful and surprising in the same rich way, it too compels
a mentalistic interpretation. Of course, somewhere behind the
scenes, there are programmers who, in principle, have a mechanical
interpretation. But even for them, that interpretation loses its
grip as the working program fills its memory with details too
voluminous for them to grasp.
As the rising flood reaches more populated heights, machines
will begin to do well in areas a greater number can appreciate.
The visceral sense of a thinking presence in machinery will become
increasingly widespread. When the highest peaks are covered, there
will be machines than can interact as intelligently as any human
on any subject. The presence of minds in machines will then become
self-evident. —Hans Moravec58
Because of the exponential nature of progress in information-based
technologies, performance often shifts quickly from pathetic to
daunting. In many diverse realms, as the examples in the previous
section make clear, the performance of narrow AI is already impressive.
The range of intelligent tasks in which machines can now compete
with human intelligence is continually expanding. In a cartoon in
The Age of Spiritual Machines, a defensive "human race" is
seen writing out signs that state what only people (and not machines)
can do.59 Littered on the floor are the signs the human
race has already discarded, because machines can now perform these
functions: diagnose an electrocardiogram, compose in the style of
Bach, recognize faces, guide a missile, play Ping-Pong, play master
chess, pick stocks, improvise jazz, prove important theorems, and
understand continuous speech. Back in 1999 these tasks were no longer
solely the province of human intelligence; machines could do them
all.
On the wall behind the man symbolizing the human race were signs
he had written out describing the tasks that were still the sole
province of humans: have common sense, review a movie, hold press
conferences, translate speech, clean a house, and drive cars. If
we were to redesign this cartoon in a few years, some of these signs
would also be likely to end up on the floor. When CYC reaches one
hundred million items of commonsense knowledge, perhaps human superiority
in the realm of commonsense reasoning won't be so clear.
The era of household robots, although still fairly primitive today,
has already started. Ten years from now, it's likely we will consider
"clean a house" as within the capabilities of machines. As for driving
cars, robots with no human intervention have already driven nearly
across the United States on ordinary roads with other normal traffic.
We are not yet ready to turn over all steering wheels to machines,
but there are serious proposals to create electronic highways on
which cars (with people in them) will drive by themselves.
The three tasks that have to do with human-level understanding
of natural language—reviewing a movie, holding a press conference,
and translating speech—are the most difficult. Once we can
take down these signs, we'll have Turing-level machines, and the
era of strong AI will have started.
This era will creep up on us. As long as there are any discrepancies
between human and machine performance—areas in which humans
outperform machines—strong AI skeptics will seize on these
differences. But our experience in each area of skill and knowledge
is likely to follow that of Kasparov. Our perceptions of performance
will shift quickly from pathetic to daunting as the knee of the
exponential curve is reached for each human capability.
How will strong AI be achieved? Most of the material in this book
is intended to lay out the fundamental requirements for both hardware
and software and explain why we can be confident that these requirements
will be met in nonbiological systems. The continuation of the exponential
growth of the price-performance of computation to achieve hardware
capable of emulating human intelligence was still controversial
in 1999. There has been so much progress in developing the technology
for three-dimensional computing over the past five years that relatively
few knowledgeable observers now doubt that this will happen. Even
just taking the semiconductor industry's published ITRS road map,
which runs to 2018, we can project human-level hardware at reasonable
cost by that year.60
I've stated the case in chapter 4 The Singularity is Near
of why we can have confidence that we will have detailed models
and simulations of all regions of the human brain by the late 2020s.
Until recently, our tools for peering into the brain did not have
the spatial and temporal resolution, bandwidth, or price-performance
to produce adequate data to create sufficiently detailed models.
This is now changing. The emerging generation of scanning and sensing
tools can analyze and detect neurons and neural components with
exquisite accuracy, while operating in real time.
Future tools will provide far greater resolution and capacity.
By the 2020s, we will be able to send scanning and sensing nanobots
into the capillaries of the brain to scan it from inside. We've
shown the ability to translate the data from diverse sources of
brain scanning and sensing into models and computer simulations
that hold up well to experimental comparison with the performance
of the biological versions of these regions. We already have compelling
models and simulations for several important brain regions. As I
argued in chapter 4 of The Singularity is Near, it's a conservative
projection to expect detailed and realistic models of all brain
regions by the late 2020s.
One simple statement of the strong AI scenario is that we will
learn the principles of operation of human intelligence from reverse
engineering all the brain's regions, and we will apply these principles
to the brain-capable computing platforms that will exist in the
2020s. We already have an effective toolkit for narrow AI. Through
the ongoing refinement of these methods, the development of new
algorithms, and the trend toward combining multiple methods into
intricate architectures, narrow AI will continue to become less
narrow. That is, AI applications will have broader domains, and
their performance will become more flexible. AI systems will develop
multiple ways of approaching each problem, just as humans do. Most
important, the new insights and paradigms resulting from the acceleration
of brain reverse engineering will greatly enrich this set of tools
on an ongoing basis. This process is well under way.
It's often said that the brain works differently from a computer,
so we cannot apply our insights about brain function into workable
nonbiological systems. This view completely ignores the field of
self-organizing systems, for which we have a set of increasingly
sophisticated mathematical tools. As I discussed in chapter 4 of
The Singularity is Near, the brain differs in a number of
important ways from that of conventional, contemporary computers.
If you open up your Palm Pilot and cut a wire, there's a good chance
you will break the machine. Yet we routinely lose many neurons and
interneuronal connections with no ill effect, because the brain
is self-organizing and relies on distributed patterns in which many
specific details are not important.
When we get to the mid- to late 2020s, we will have access to a
generation of extremely detailed brain-region models. Ultimately
the toolkit will be greatly enriched with these new models and simulations
and will encompass a full knowledge of how the brain works. As we
apply the toolkit to intelligent tasks, we will draw upon the entire
range of tools, some derived directly from brain reverse engineering,
some merely inspired by what we know about the brain, and some not
based on the brain at all but on decades of AI research.
Part of the brain's strategy is to learn information, rather than
having knowledge hard-coded from the start. ("Instinct" is the term
we use to refer to such innate knowledge.) Learning will be an important
aspect of AI, as well. In my experience in developing pattern-recognition
systems in character recognition, speech recognition, and financial
analysis, providing for the AI's education is the most challenging
and important part of the engineering. With the accumulated knowledge
of human civilization increasingly accessible online, future AIs
will have the opportunity to conduct their education by accessing
this vast body of information.
The education of AIs will be much faster than that of unenhanced
humans. The twenty-year time span required to provide a basic education
to biological humans could be compressed into a matter of weeks
or less. Also, because nonbiological intelligence can share its
patterns of learning and knowledge, only one AI has to master each
particular skill. As I pointed out, we trained one set of research
computers to understand speech, but then the hundreds of thousands
of people who acquired our speech-recognition software had to load
only the already trained patterns into their computers.
One of the many skills that nonbiological intelligence will achieve
with the completion of the human brain–reverse engineering
project is sufficient mastery of language and shared human knowledge
to pass the Turing test. The Turing test is important not so much
for its practical significance but rather because it will demarcate
a crucial threshold. As I have pointed out, there is no simple means
to pass a Turing test, other than to convincingly emulate the flexibility,
subtlety, and suppleness of human intelligence. Having captured
that capability in our technology, it will then be subject to engineering's
ability to concentrate, focus, and amplify it..
Variations of the Turing test have been proposed. The annual Loebner
Prize contest awards a bronze prize to the chatterbot (conversational
bot) best able to convince human judges that it's human.61
The criteria for winning the silver prize is based on Turing's original
test, and it obviously has yet to be awarded. The gold prize is
based on visual and auditory communication. In other words, the
AI must have a convincing face and voice, as transmitted over a
terminal, and thus it must appear to the human judge as if he or
she is interacting with a real person over a videophone. On the
face of it, the gold prize sounds more difficult. I've argued that
it may actually be easier, because judges may pay less attention
to the text portion of the language being communicated and could
be distracted by a convincing facial and voice animation. In fact,
we already have real-time facial animation, and while it is not
quite up to these modified Turing standards, it's reasonably close.
We also have very natural-sounding voice synthesis, which is often
confused with recordings of human speech, although more work is
needed on prosodics (intonation). We're likely to achieve satisfactory
facial animation and voice production sooner than the Turing-level
language and knowledge capabilities.
Turing was carefully imprecise in setting the rules for his test,
and significant literature has been devoted to the subtleties of
establishing the exact procedures for determining how to assess
when the Turing test has been passed.62 In 2002 I negotiated
the rules for a Turing-test wager with Mitch Kapor on the Long Now
Web site.63 The question underlying our twenty-thousand-dollar
bet, the proceeds of which go to the charity of the winner's choice,
was, "Will the Turing test be passed by a machine by 2029?" I said
yes, and Kapor said no. It took us months of dialogue to arrive
at the intricate rules to implement our wager. Simply defining "machine"
and "human," for example, was not a straightforward matter. Is the
human judge allowed to have any nonbiological thinking processes
in his or her brain? Conversely, can the machine have any biological
aspects?
Because the definition of the Turing test will vary from person
to person, Turing test-capable machines will not arrive on a single
day, and there will be a period during which we will hear claims
that machines have passed the threshold. Invariably, these early
claims will be debunked by knowledgeable observers, probably including
myself. By the time there is a broad consensus that the Turing test
has been passed, the actual threshold will have long since been
achieved.
Edward Feigenbaum proposes a variation of the Turing test, which
assesses not a machine's ability to pass for human in casual, everyday
dialogue but its ability to pass for a scientific expert in a specific
field.64 The Feigenbaum test (FT) may be more significant
than the Turing test, because FT-capable machines, being technically
proficient, will be capable of improving their own designs. Feigenbaum
describes his test in this way:
Two players play the FT game. One player is chosen from among
the elite practitioners in each of three pre-selected fields of
natural science, engineering, or medicine. (The number could be
larger, but for this challenge not greater than ten). Let's say
we choose the fields from among those covered in the U.S. National
Academy.... For example, we could choose astrophysics, computer
science, and molecular biology. In each round of the game, the behavior
of the two players (elite scientist and computer) is judged by another
Academy member in that particular domain of discourse, e.g., an
astrophysicist judging astrophysics behavior. Of course the identity
of the players is hidden from the judge as it is in the Turing test.
The judge poses problems, asks questions, asks for explanations,
theories, and so on—as one might do with a colleague. Can the
human judge choose, at better than chance level, which is his National
Academy colleague and which is the computer?
Of course Feigenbaum overlooks the possibility that the computer
might already be a National Academy colleague, but he is obviously
assuming that machines will not yet have invaded institutions that
today comprise exclusively biological humans. While it may appear
that the FT is more difficult than the Turing test, the entire history
of AI reveals that machines started with the skills of professionals
and only gradually moved towards the language skills of a child.
Early AI systems demonstrated their prowess initially in professional
fields such as proving mathematical theorems and diagnosing medical
conditions. These early systems would not be able to pass the FT,
however, because they do not have the language skills and the flexible
ability to model knowledge from different perspectives, which are
needed to engage in the professional dialogue inherent in the FT.
This language ability is essentially the same ability needed in
the Turing test. Reasoning in many technical fields is not necessarily
more difficult than the commonsense reasoning engaged in by most
human adults. I would expect that machines will pass the FT, at
least in some disciplines, around the same time as they pass the
Turing test. Passing the FT in all disciplines is likely to take
longer, however. This is why I see the 2030s as a period of consolidation,
as machine intelligence rapidly expands its skills and incorporates
the vast knowledge bases of our biological human and machine civilization.
By the 2040s we will have the opportunity to apply the accumulated
knowledge and skills of our civilization to computational platforms
that are billions of times more capable than unassisted biological
human intelligence.
The advent of strong AI is the most important transformation this
century will see. Indeed, it's comparable in importance to the advent
of biology itself. It will mean that a creation of biology has finally
mastered its own intelligence and discovered means to overcome its
limitations. Once the principles of operation of human intelligence
are understood, expanding its abilities will be conducted by human
scientists and engineers whose own biological intelligence will
have been greatly amplified through an intimate merger with nonbiological
intelligence. Over time, the nonbiological portion will predominate.
I have discussed aspects of the impact of this transformation throughout
The Singularity is Near, which I focus on in chapter 6. Intelligence
is the ability to solve problems with limited resources, including
limitations of time. The Singularity will be characterized by the
rapid cycle of human intelligence—increasingly nonbiological—
capable of comprehending and leveraging its own powers.
1 As quoted by Douglas Hofstadter in Gödel, Escher,
Bach: An Eternal Golden Braid (New York: Basic Books, 1979).
2 The author runs a company, FATKAT (Financial Accelerating
Transactions by Kurzweil Adaptive Technologies), which applies computerized
pattern recognition to financial data to make stock-market investment
decisions, http://www.FatKat.com.
3 See discussion in chapter 2 on price-performance improvements
in computer memory and electronics in general.
4 Runaway AI refers to a scenario where, as Max More describes
"superintelligent machines, initially harnessed for human
benefit, soon leave us behind." Max More, "Embrace, Don't Relinquish,
the Future," http://www.kurzweilai.net/articles/art0106.html?printable=1
See also Damien Broderick's description of the "Seed AI" as "A
self-improving seed AI could run glacially slowly on a limited machine
substrate. The point is, so long as it has the capacity to improve
itself, at some point it will do so convulsively, bursting through
any architectural bottlenecks to design its own improved hardware,
maybe even build it (if it's allowed control of tools in a fabrication
plant)." Damien Broderick, "Tearing toward the Spike," presented
at "Australia at the Crossroads? Scenarios and Strategies for the
Future," (April 31 - May 2, 2000), published on KurzweilAI.net May
7, 2001: http://www.kurzweilai.net/meme/frame.html?main=/articles/art0173.html
5 David Talbot, "Lord of the Robots," Technology Review
(April 2002).
6 Heather Havenstein writes that the "inflated notions spawned
by science fiction writers about the convergence of humans and machines
tarnished the image of AI in the 1980s because AI was perceived
as failing to live up to its potential." Heather Havenstein, "Spring
comes to AI winter: A thousand applications bloom in medicine, customer
service, education and manufacturing," Computerworld, February
14, 2005, http://www.computerworld.com/softwaretopics/software/story/0,10801,99691,00.html
This tarnished image led to "AI Winter," defined as "a term coined
by Richard Gabriel for the (circa 1990-94?) crash of the wave of
enthusiasm for the AI language Lisp and AI itself, following a boom
in the 1980s." Duane Rettig wrote "…companies rode the great
AI wave in the early 80's, when large corporations poured billions
of dollars into the AI hype that promised thinking machines in 10
years. When the promises turned out to be harder than originally
thought, the AI wave crashed, and Lisp crashed with it because of
its association with AI. We refer to it as the AI Winter." Duane
Rettig quoted in "AI Winter," http://c2.com/cgi/wiki?AiWinter
7 The General Problem Solver (GPS) computer program, written
in 1957, was able to solve problems through rules that allowed the
GPS to divide a problem's goals into subgoals, and then check if
obtaining a particular subgoal would bring the GPS closer to solving
the overall goal. In the early 1960s Thomas Evan wrote ANALOGY,
a "program [that] solves geometric-analogy problems of the form
A:B::C:? taken from IQ tests and college entrance exams." Boicho
Kokinov and Robert M. French, Computational Models of Analogy-Making,
in Nadel, L. (Ed.) Encyclopedia of Cognitive Science, Vol.
1, (London: Nature Publishing Group, 2003) pp.113-118. See also
A. Newell, J.C. Shaw, and H.A. Simon, "Report on a general problem-solving
program," Proceedings of the International Conference on Information
Processing, (Paris: UNESCO House, 1959) pp. 256-264; and Thomas
Evans, "A Heuristic Program to Solve Geometric-Analogy Problems,"
in Semantic Information Processing, M. Minsky, Editor, (Cambridge,
MA: MIT Press, 1968).
8 Sir Arthur Conan Doyle, "The Red-Headed League," 1890,
available at http://www.eastoftheweb.com/short-stories/UBooks/RedHead.shtml.
9 V. Yu et al., "Antimicrobial Selection by a Computer:
A Blinded Evaluation by Infectious Diseases Experts," JAMA 242.12
(1979): 1279–82.
10 Gary H. Anthes, "Computerizing Common Sense," Computerworld,
April 8, 2002, http://www.computerworld.com/news/2002/story/0,11280,69881,00.html.
11 Kristen Philipkoski, "Now Here's a Really Big Idea,"
Wired News, November 25, 2002, http://www.wired.com/news/technology/0,1282,56374,00.html,
reporting on Darryl Macer, "The Next Challenge Is to Map the Human
Mind," Nature 420 (November 14, 2002): 121; see also a description
of the project at http://www.biol.tsukuba.ac.jp/~macer/index.html.
12 Thomas Bayes, "An Essay Towards Solving a Problem in
the Doctrine of Chances," published in 1763, two years after his
death in 1761.
13 SpamBayes spam filter, http://spambayes.sourceforge.net.
14 Lawrence R. Rabiner, "A Tutorial on Hidden Markov Models
and Selected Applications in Speech Recognition," Proceedings
of the IEEE 77 (1989): 257–86. For a mathematical treatment
of Markov models, see http://jedlik.phy.bme.hu/~gerjanos/HMM/node2.html.
15 Kurzweil Applied Intelligence (KAI), founded by the author
in 1982, was sold in 1997 for $100 million and is now part of ScanSoft
(formerly called Kurzweil Computer Products, the author's first
company, which was sold to Xerox in 1980), now a public company.
KAI introduced the first commercially marketed large-vocabulary
speech-recognition system in 1987 (Kurzweil Voice Report, with a
ten-thousand-word vocabulary).
16 Here is the basic schema for a neural net algorithm.
Many variations are possible, and the designer of the system needs
to provide certain critical parameters and methods, detailed below.
Creating a neural-net solution to a problem involves the
following steps:
- Define the input.
- Define the topology of the neural net (i.e., the layers
of neurons and the connections between the neurons).
- Train the neural net on examples of the problem.
- Run the trained neural net to solve new examples of the
problem.
- Take your neural-net company public. These steps (except
for the last one) are detailed below:
The Problem Input
The problem input to the neural net consists of a series
of numbers. This input can be:
- In a visual pattern-recognition system, a two-dimensional
array of numbers representing the pixels of an image; or
- In an auditory (e.g., speech) recognition system, a two-dimensional
array of numbers representing a sound, in which the first
dimension represents parameters of the sound (e.g., frequency
components) and the second dimension represents different
points in time; or
- In an arbitrary pattern-recognition system, an n-dimensional
array of numbers representing the input pattern.
Defining the Topology
To set up the neural net, the architecture of each neuron
consists of:
- Multiple inputs in which each input is "connected" to
either the output of another neuron, or one of the input
numbers.
- Generally, a single output, which is connected either
to the input of another neuron (which is usually in a higher
layer), or to the final output.
Set up the First Layer of Neurons
- Create N0 neurons in the first layer. For each
of these neurons, "connect" each of the multiple inputs
of the neuron to "points" (i.e., numbers) in the problem
input. These connections can be determined randomly or using
an evolutionary algorithm (see below).
- Assign an initial "synaptic strength" to each connection
created. These weights can start out all the same, can be
assigned randomly, or can be determined in another way (see
below).
Set up the Additional Layers of Neurons
Set up a total of M layers of neurons. For each layer, set
up the neurons in that layer. For layeri:
- Create Ni neurons in layeri. For
each of these neurons, "connect" each of the multiple inputs
of the neuron to the outputs of the neurons in layeri–1
(see variations below).
- Assign an initial "synaptic strength" to each connection
created. These weights can start out all the same, can be
assigned randomly, or can be determined in another way (see
below).
- The outputs of the neurons in layerM are the outputs of
the neural net (see variations below).
The Recognition Trials
How Each Neuron Works Once the neuron is set up, it does
the following for each recognition trial.
- Each weighted input to the neuron is computed by multiplying
the output of the other neuron (or initial input) that the
input to this neuron is connected to by the synaptic strength
of that connection.
- All of these weighted inputs to the neuron are summed.
- If this sum is greater than the firing threshold of this
neuron, then this is neuron is considered to fire and its
output is 1. Otherwise, its output is 0 (see variations
below).
Do the Following for Each Recognition Trial
For each layer, from layer0 to layerM:
For each neuron in the layer:
- Sum its weighted inputs (each weighted input = the output
of the other neuron [or initial input] that the input to
this neuron is connected to multiplied by the synaptic strength
of that connection).
- If this sum of weighted inputs is greater than the firing
threshold for this neuron, set the output of this neuron
= 1, otherwise set it to 0.
To Train the Neural Net
- Run repeated recognition trials on sample problems.
- After each trial, adjust the synaptic strengths of all
the interneuronal connections to improve the performance
of the neural net on this trial (see the discussion below
on how to do this).
- Continue this training until the accuracy rate of the
neural net is no longer improving (i.e., reaches an asymptote).
Key Design Decisions
In the simple schema above, the designer of this neural-net
algorithm needs to determine at the outset:
- What the input numbers represent.
- The number of layers of neurons.
- The number of neurons in each layer. (Each layer does
not necessarily need to have the same number of neurons.)
- The number of inputs to each neuron in each layer. The
number of inputs (i.e., interneuronal connections) can also
vary from neuron to neuron and from layer to layer.
- The actual "wiring" (i.e., the connections). For each
neuron in each layer, this consists of a list of other neurons,
the outputs of which constitute the inputs to this neuron.
This represents a key design area. There are a number of
possible ways to do this:
- (i) Wire the neural net randomly; or
- (ii) Use an evolutionary algorithm (see below) to
determine an optimal wiring; or
- (iii) Use the system designer's best judgment in determining
the wiring.
- The initial synaptic strengths (i.e., weights) of each
connection. There are a number of possible ways to do this:
- (i) Set the synaptic strengths to the same value;
or
- (ii) Set the synaptic strengths to different random
values; or
- (iii) Use an evolutionary algorithm to determine an
optimal set of initial values; or
- (iv) Use the system designer's best judgment in determining
the initial values.
- The firing threshold of each neuron.
- Determine the output. The output can be:
- (i) the outputs of layerM of neurons; or
- (ii) the output of a single output neuron, the inputs
of which are the outputs of the neurons in layerM.
- (iii) a function of (e.g., a sum of) the outputs of
the neurons in layerM; or
- (iv) another function of neuron outputs in multiple
layers.
- Determine how the synaptic strengths of all the connections
are adjusted during the training of this neural net. This
is a key design decision and is the subject of a great deal
of research and discussion. There are a number of possible
ways to do this:
- (i) For each recognition trial, increment or decrement
each synaptic strength by a (generally small) fixed
amount so that the neural net's output more closely
matches the correct answer. One way to do this is to
try both incrementing and decrementing and see which
has the more desirable effect. This can be time-consuming,
so other methods exist for making local decisions on
whether to increment or decrement each synaptic strength.
- (ii) Other statistical methods exist for modifying
the synaptic strengths after each recognition trial
so that the performance of the neural net on that trial
more closely matches the correct answer.
Note that neural-net training will work even if the answers
to the training trials are not all correct. This allows using
real-world training data that may have an inherent error rate.
One key to the success of a neural net–based recognition
system is the amount of data used for training. Usually a
very substantial amount is needed to obtain satisfactory results.
Just like human students, the amount of time that a neural
net spends learning its lessons is a key factor in its performance.
Variations
Many variations of the above are feasible:
- There are different ways of determining the topology.
In particular, the interneuronal wiring can be set either
randomly or using an evolutionary algorithm.
- There are different ways of setting the initial synaptic
strengths.
- The inputs to the neurons in layeri do not
necessarily need to come from the outputs of the neurons
in layeri–1. Alternatively, the inputs to
the neurons in each layer can come from any lower layer
or any layer.
- There are different ways to determine the final output.
- The method described above results in an "all or nothing"
(1 or 0) firing called a nonlinearity. There are other nonlinear
functions that can be used. Commonly a function is used
that goes from 0 to 1 in a rapid but more gradual fashion.
Also, the outputs can be numbers other than 0 and 1.
- The different methods for adjusting the synaptic strengths
during training represent key design decisions. The above
schema describes a "synchronous" neural net, in which each
recognition trial proceeds by computing the outputs of each
layer, starting with layer0 through layerM. In
a true parallel system, in which each neuron is operating
independently of the others, the neurons can operate "asynchronously"
(i.e., independently). In an asynchronous approach, each
neuron is constantly scanning its inputs and fires whenever
the sum of its weighted inputs exceeds its threshold (or
whatever its output function specifies).
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17 See Chapter 4 for a detailed discussion of brain reverse-engineering.
As one example of the progression, S. J. Thorpe writes, "We have
really only just begun what will certainly be a long term project
aimed at reverse engineering the primate visual system. For the
moment, we have only explored some very simple architectures, involving
essentially just feed-forward architectures involving a relatively
small numbers of layers… In the years to come, we will strive
to incorporate as many of the computational tricks used by the primate
and human visual system as possible. More to the point, it seems
that by adopting the spiking neuron approach, it will soon be possible
to develop sophisticated systems capable of simulating very large
neuronal networks in real time." S.J. Thorpe et al., "Reverse engineering
of the visual system using networks of spiking neurons," Proceedings
of the IEEE 2000 International Symposium on Circuits and Systems,
IEEE press. IV: 405-408, http://www.sccn.ucsd.edu/~arno/mypapers/thorpe.pdf
18 T. Schoenauer et. al. write, "Over the past years a huge
diversity of hardware for artificial neural networks (ANN) has been
designed… Today one can choose from a wide range of neural
network hardware. Designs differ in terms of architectural approaches,
such as neurochips, accelerator boards and multi-board neurocomputers,
as well as concerning the purpose of the system, such as the ANN
algorithm(s) and the system's versatility… Digital neurohardware
can be classified by the:[sic] system architecture, degree of parallelism,
typical neural network partition per processor, inter-processor
communication network and numerical representation." T. Schoenauer,
A. Jahnke, U. Roth and H. Klar, "Digital Neurohardware: Principles
and Perspectives," in Proc. Neuronale Netze in der Anwendung
– Neural Networks in Applications NN'98, Magdeburg, invited
paper (February 1998): 101-106, http://bwrc.eecs.berkeley.edu/People/kcamera/neural/papers/schoenauer98digital.pdf.
See also:Yihua Liao, "Neural Networks in Hardware: A Survey," 2001,
http://ailab.das.ucdavis.edu/~yihua/research/NNhardware.pdf
19 Here is the basic schema for a genetic (evolutionary)
algorithm. Many variations are possible, and the designer of the
system needs to provide certain critical parameters and methods,
detailed below.
The Evolutionary Algorithm
Create N solution "creatures". Each one has:
- A genetic code: a sequence of numbers that characterize
a possible solution to the problem. The numbers can represent
critical parameters, steps to a solution, rules, etc.
- For each generation of evolution, do the following:
- Do the following for each of the N solution creatures:
- Apply this solution creature's solution (as represented
by its genetic code) to the problem, or simulated environment.
Rate the solution.
- Pick the L solution creatures with the highest ratings
to survive into the next generation.
- Eliminate the (N–L) nonsurviving solution creatures.
- Create (N–L) new solution creatures from the L surviving
solution creatures by:
(i) Making copies of the L surviving creatures. Introduce
small random variations into each copy; or
(ii) Create additional solution creatures by combining parts
of the genetic code (using "sexual" reproduction, or otherwise
combining portions of the chromosomes) from the L surviving
creatures; or
(iii) Doing a combination of (i) and (ii).
- Determine whether or not to continue evolving: Improvement
= (highest rating in this generation)–(highest rating
in the previous generation). If Improvement < Improvement
Threshold then we're done.
- The solution creature with the highest rating from the
last generation of evolution has the best solution. Apply
the solution defined by its genetic code to the problem.
Key Design Decisions
In the simple schema above, the designer needs to determine
at the outset:
- Key parameters:
- N
- L
- Improvement threshold
- What the numbers in the genetic code represent and how
the solution is computed from the genetic code.
- A method for determining the N solution creatures in the
first generation. In general, these need only be "reasonable"
attempts at a solution. If these first-generation solutions
are too far afield, the evolutionary algorithm may have
difficulty converging on a good solution. It is often worthwhile
to create the initial solution creatures in such a way that
they are reasonably diverse. This will help prevent the
evolutionary process from just finding a "locally" optimal
solution.
- How the solutions are rated.
- How the surviving solution creatures reproduce.
Variations
Many variations of the above are feasible. For example:
- There does not need to be a fixed number of surviving
solutions creatures (L) from each generation. The survival
rule(s) can allow for a variable number of survivors.
- There does not need to be a fixed number of new solution
creatures created in each generation (N–L). The procreation
rules can be independent of the size of the population.
Procreation can be related to survival, thereby allowing
the fittest solution creatures to procreate the most.
- The decision as to whether or not to continue evolving
can be varied. It can consider more than just the highest-rated
solution creature from the most recent generation(s). It
can also consider a trend that goes beyond just the last
two generations
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20 Sam Williams, "When Machines Breed," August 12, 2004,
http://www.salon.com/tech/feature/2004/08/12/evolvable_hardware/index_np.html.
21 Here is the basic scheme (algorithm description) of recursive
search. Many variations are possible, and the designer of the system
needs to provide certain critical parameters and methods, detailed
below.
The Recursive Algorithm
Define a function (program) "Pick Best Next Step." The function
returns a value of "SUCCESS" (we've solved the problem) or
"FAILURE" (we didn't solve it). If it returns with a value
of SUCCESS, then the function also returns the sequence of
steps that solved the problem. Pick Best Next Step does the
following:
- Determine if the program can escape from continued recursion
at this point. This bullet, and the next two bullets deal
with this escape decision. First, determine if the problem
has now been solved. Since this call to Pick Best Next Step
probably came from the program calling itself, we may now
have a satisfactory solution. Examples are:
- (i) In the context of a game (for example, chess),
the last move allows us to win (such as checkmate).
- (ii) In the context of solving a mathematical theorem,
the last step proves the theorem.
- (iii) In the context of an artistic program (for example,
a computer poet or composer), the last step matches
the goals for the next word or note.
If the problem has been satisfactorily solved, the program
returns with a value of "SUCCESS" and the sequence of steps
that caused the success.
If the problem has not been solved, determine if a solution
is now hopeless. Examples are:
- (i) In the context of a game (such as chess), this move
causes us to lose (checkmate for the other side).
- (ii) In the context of solving a mathematical theorem,
this step violates the theorem.
- (iii) In the context of an artistic creation, this step
violates the goals for the next word or note.
- If the solution at this point has been deemed hopeless,
the program returns with a value of "FAILURE."
- If the problem has been neither solved nor deemed hopeless
at this point of recursive expansion, determine whether
or not the expansion should be abandoned anyway. This is
a key aspect of the design and takes into consideration
the limited amount of computer time we have to spend. Examples
are:
- (i) In the context of a game (such as chess), this
move puts our side sufficiently "ahead" or "behind."
Making this determination may not be straightforward
and is the primary design decision. However, simple
approaches (such as adding up piece values) can still
provide good results. If the program determines that
our side is sufficiently ahead, then Pick Best Next
Step returns in a similar manner to a determination
that our side has won (that is, with a value of "SUCCESS").
If the program determines that our side is sufficiently
behind, then Pick Best Next Step returns in a similar
manner to a determination that our side has lost (that
is, with a value of "FAILURE").
- (ii) In the context of solving a mathematical theorem,
this step involves determining if the sequence of steps
in the proof is unlikely to yield a proof. If so, then
this path should be abandoned, and Pick Best Next Step
returns in a similar manner to a determination that
this step violates the theorem (that is, with a value
of "FAILURE"). There is no "soft" equivalent of success.
We can't return with a value of "SUCCESS" until we have
actually solved the problem. That's the nature of math.
- (iii) In the context of an artistic program (such
as a computer poet or composer), this step involves
determining if the sequence of steps (such as the words
in a poem, notes in a song) is unlikely to satisfy the
goals for the next step. If so, then this path should
be abandoned, and Pick Best Next Step returns in a similar
manner to a determination that this step violates the
goals for the next step (that is, with a value of "FAILURE").
- If Pick Best Next Step has not returned (because the program
has neither determined success nor failure nor made a determination
that this path should be abandoned at this point), then
we have not escaped from continued recursive expansion.
In this case, we now generate a list of all possible next
steps at this point. This is where the precise statement
of the problem comes in:
- (i) In the context of a game (such as chess), this
involves generating all possible moves for "our" side
for the current state of the board. This involves a
straightforward codification of the rules of the game.
- (ii) In the context of finding a proof for a mathematical
theorem, this involves listing the possible axioms or
previously proved theorems that can be applied at this
point in the solution.
- (iii) In the context of a cybernetic art program,
this involves listing the possible words/notes/line
segments that could be used at this point. For each
such possible next step:
- (i) Create the hypothetical situation that would
exist if this step were implemented. In a game,
this means the hypothetical state of the board.
In a mathematical proof, this means adding this
step (for example, axiom) to the proof. In an art
program, this means, adding this word/note/line
segment.
- (ii) Now call Pick Best Next Step to examine this
hypothetical situation. This is, of course, where
the recursion comes in because the program is now
calling itself.
- (iii) If the above call to Pick Best Next Step
returns with a value of "SUCCESS," then return from
the call to Pick Best Next Step (that we are now
in) also with a value of "SUCCESS." Otherwise consider
the next possible step.
If all the possible next steps have been considered
without finding a step that resulted in a return from
the call to Pick Best Next Step with a value of "SUCCESS,"
then return from this call to Pick Best Next Step
(that we are now in) with a value of "FAILURE."
END OF PICK BEST NEXT STEP
If the original call to Pick Best Next Move returns
with a value of "SUCCESS," it will also return the
correct sequence of steps:
- (i) In the context of a game, the first step in this
sequence is the next move you should make.
- (ii) In the context of a mathematical proof, the full
sequence of steps is the proof.
- (iii) In the context of a cybernetic art program, the
sequence of steps is your work of art.
If the original call to Pick Best Next Step is "FAILURE,"
then you need to go back to the drawing board.
Key Design Decisions
In the simple schema above, the designer of the recursive
algorithm needs to determine the following at the outset:
- The key to a recursive algorithm is the determination
in Pick Best Next Step of when to abandon the recursive
expansion. This is easy when the program has achieved clear
success (such as checkmate in chess or the requisite solution
in a math or combinatorial problem) or clear failure. It
is more difficult when a clear win or loss has not yet been
achieved. Abandoning a line of inquiry before a well-defined
outcome is necessary because otherwise the program might
run for billions of years (or at least until the warranty
on your computer runs out).
- The other primary requirement for the recursive algorithm
is a straightforward codification of the problem. In a game
like chess, that's easy. But in other situations, a clear
definition of the problem is not always so easy to come
by
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22 See Kurzweil Cyberart, http://www.KurzweilCyberArt.com,
for further description of Ray Kurzweil's Cybernetic Poet and to
download a free copy of the program. See U.S. Patent # 6,647,395
"Poet Personalities," inventors: Ray Kurzweil and John Keklak. Abstract:
"A method of generating a poet personality including reading poems,
each of the poems containing text, generating analysis models, each
of the analysis models representing one of poems and storing the
analysis models in a personality data structure. The personality
data structure further includes weights, each of the weights associated
with each of the analysis models. The weights include integer values."
23 Ben Goertzel, The Structure of Intelligence, 1993,
Springer-Verlag. Ben Goertzel, The Evolving Mind, 1993, Gordon
and Breach. Ben Goertzel, Chaotic Logic, 1994, Plenum. Ben
Goertzel, From Complexity to Creativity, 1997, Plenum. For
a link to Ben Goertzel's books and essays, see http://www.goertzel.org/work.html
24 KurzweilAI.net (http://www.KurzweilAI.net)
provides hundreds of articles by one hundred "big thinkers" and
other features on "accelerating intelligence." The site offers a
free daily or weekly newsletter on the latest developments in the
areas covered by this book. To subscribe, enter your e-mail address
(which is maintained in strict confidence and is not shared with
anyone) on the home page.
25 John Gosney, Business Communications Company, "Artificial
Intelligence: Burgeoning Applications in Industry," June 2003, http://www.bccresearch.com/comm/G275.html.
26 Kathleen Melymuka, "Good Morning, Dave . . . ," Computerworld,
November 11, 2002, http://www.computerworld.com/industrytopics/defense/story/0,10801,75728,00.html.
27 JTRS Technology Awareness Bulletin, August 2004, http://jtrs.army.mil/sections/technicalinformation/fset_technical.html?tech_aware_2004-8.
28 Otis Port, Michael Arndt, and John Carey, "Smart Tools,"
spring 2003, http://www.businessweek.com/bw50/content/mar2003/a3826072.htm.
29 Wade Roush, "Immobots Take Control: From Photocopiers
to Space Probes, Machines Injected with Robotic Self-Awareness Are
Reliable Problem Solvers," Technology Review (December 2002/January
2003), http://www.occm.de/roush1202.pdf.
30 Jason Lohn quoted in NASA news release "NASA 'Evolutionary'
Software Automatically Designs Antenna," http://www.nasa.gov/lb/centers/ames/news/releases/2004/04_55AR.html
31 Robert Roy Britt, "Automatic Astronomy: New Robotic Telescopes
See and Think," June 4, 2003, http://www.space.com/businesstechnology/technology/automated_astronomy_030604.html.
32 H. Keith Melton, "Spies in the Digital Age," http://www.cnn.com/SPECIALS/cold.war/experience/spies/melton.essay.
33 "United Therapeutics (UT) is a biotechnology company
focused on developing chronic therapies for life threatening conditions
in three therapeutic areas: cardiovascular, oncology and infectious
diseases" (http://www.unither.com).
Kurzweil Technologies is working with UT to develop pattern recognition–based
analysis from either "Holter" monitoring (twenty-four-hour recordings)
or "Event" monitoring (thirty days or more).
34 Kristen Philipkoski, "A Map That Maps Gene Functions,"
Wired News, May 28, 2002, http://www.wired.com/news/medtech/0,1286,52723,00.html.
35 Jennifer Ouellette, "Bioinformatics Moves into the Mainstream,"
The Industrial Physicist (October/November 2003), http://www.sciencemasters.com/bioinformatics.pdf.
36 Port, Arndt, and Carey, "Smart Tools."
37 "Protein Patterns in Blood May Predict Prostate Cancer
Diagnosis," National Cancer Institute, October 15, 2002, http://www.nci.nih.gov/newscenter/ProstateProteomics,
reporting on E. F. Petricoin et al., "Serum Proteomic Patterns for
Detection of Prostate Cancer," Journal of the National Cancer
Institute 94 (2002): 1576–78.
38 Charlene Laino, "New Blood Test Spots Cancer," December
13, 2002, http://my.webmd.com/content/Article/56/65831.htm;
Emanuel F. Petricoin III et al., "Use of Proteomic Patterns in Serum
to Identify Ovarian Cancer," The Lancet 359.9306 (February
16, 2002): 572–77.
39 Mark Hagland, "Doctor's Orders," January 2003, http://www.healthcare-
informatics.com/issues/2003/01_03/cpoe.htm.
40 Ross D. King et al., "Functional Genomic Hypothesis Generation
and Experimentation by a Robot Scientist," Nature 427 (January
15, 2004): 247–52.
41 Port, Arndt, and Carey, "Smart Tools."
42 "Future Route Releases AI-Based Fraud Detection Product,"
August 18, 2004, http://www.finextra.com/fullstory.asp?id=12365.
43 John Hackett, "Computers Are Learning the Business,"
Collections World, April 24, 2001, http://www.creditcollectionsworld.com/news/042401_2.htm.
44 "Innovative Use of Artificial Intelligence, Monitoring
NASDAQ for Potential Insider Trading and Fraud," AAAI press release,
July 30, 2003, http://www.aaai.org/Pressroom/Releases/release-03-0730.html.
45 "Adaptive Learning, Fly the Brainy Skies," Wired
News, March 2002, http://www.wired.com/wired/archive/10.03/everywhere.html?pg=2.
46 Introduction to Artificial Intelligence, EL 629, Maxwell
Air Force Base, Air University Library course www.au.af.mil/au/aul/school/acsc/ai02.htm.
47 See www.Seegrid.com.
48 No Hands Across America Web site, http://cart.frc.ri.cmu.edu/users/hpm/project.archive/reference.file/nhaa.html,
and "Carnegie Mellon Researchers Will Prove Autonomous Driving Technologies
During a 3,000 Mile, Hands-off-the-Wheel Trip from Pittsburgh to
San Diego," Carnegie Mellon press release, http://www-
2.cs.cmu.edu/afs/cs/user/tjochem/www/nhaa/official_press_release.html;
Robert J. Derocher, "Almost Human," September 2001, http://www.insight-mag.com/insight/01/09/col-2-pt-1-ClickCulture.htm.
49 "Search and Rescue Robots," Associated Press, September
3, 2004, http://www.smh.com.au/articles/2004/09/02/1093939058792.html?oneclick=true.
50 "From Factoids to Facts," The Economist, August
26, 2004, http://www.economist.com/science/displayStory.cfm?story_id=3127462.
51 Joe McCool, "Voice Recognition, It Pays to Talk," May
2003, http://www.bcs.org/BCS/Products/Publications/JournalsAndMagazines/ComputerBulletin/OnlineArchive/
may03/voicerecognition.htm.
52 John Gartner, "Finally a Car That Talks Back," Wired
News, September 2, 2004, http://www.wired.com/news/autotech/0,2554,64809,00.html?tw=wn_14techhead.
53 "Computer Language Translation System Romances the Rosetta
Stone," Information Sciences Institute, USC School of Engineering
(July 24, 2003), http://www.usc.edu/isinews/stories/102.html.
54 Torsten Reil quoted in Steven Johnson, "Darwin in a Box,"
Discover Magazine 24.8 (August 2003), http://www.discover.com/issues/aug-03/departments/feattech/
55 "Let Software Catch the Game for You," July 3, 2004,
http://www.newscientist.com/news/news.jsp?id=ns99996097.
56 Michelle Delio, "Breeding Race Cars to Win," Wired
News, June 18, 2004, http://www.wired.com/news/autotech/0,2554,63900,00.html.
57 Marvin Minsky, The Society of Mind (New York:
Simon & Schuster, 1988).
58 Hans Moravec, "When will computer hardware match the
human brain?" Journal of Evolution and Technology, 1998.
Volume 1.
59 Ray Kurzweil, The Age of Spiritual Machines (New
York: Viking, 1999), p. 156.
60 See Chapter 2 endnotes 22 and 23 on the International
Technology Roadmap for Semiconductors.
61 "The First Turing Test," http://www.loebner.net/Prizef/loebner-prize.html.
62 Douglas R. Hofstadter, "A Coffeehouse Conversation on
the Turing Test," May 1981, included in Ray Kurzweil, The Age
of Intelligent Machines (Cambridge, Mass.: MIT Press, 1990),
pp. 80–102, http://www.kurzweilai.net/meme/frame.html?main=/articles/art0318.html.
63 Ray Kurzweil "Why I Think I Will Win." And Mitch Kapor,
"Why I Think I Will Win." Rules: http://www.kurzweilai.net/meme/frame.html?main=/articles/art0373.html;
Kapor: http://www.kurzweilai.net/meme/frame.html?main=/articles/art0412.html;
Kurzweil: http://www.kurzweilai.net/meme/frame.html?main=/articles/art0374.html;
Kurzweil "final word": http://www.kurzweilai.net/meme/frame.html?main=/articles/art0413.html.
64 Edward A. Feigenbaum, "Some Challenges and Grand Challenges
for Computational Intelligence," Journal of the Association for
Computing Machinery 50 (January 2003): 32–40.
© 2006 Ray Kurzweil
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Mind·X Discussion About This Article:
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Re: Why We Can Be Confident of Turing Test Capability Within a Quarter Century
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If we were to build a very powerful AI machine using the tools listed in the AI Toolkit; expert systems, Bayesian nets, Markov models, neural nets, genetic algorithms, and recursive searching, we might have a machine that could fool a turning test, but it would not have any self awareness, creative intuition, or the ability to think outside the box. (pun intended!)
I think it is time to take a closer look at the work of Harold Cohen and other generative artists who might have ideas about finding creativity in a machine. Artistic creativity does not come from a paradigm that narrows down knowledge with some end result in mind, it comes from a random thought that is captured and developed. The AI Toolkit can be applied to developing creative ideas but the spark has to come from unpredictable events.
I believe that research in this area could lead to a machine intelligence that will someday be self aware and have a free will. Then a machine will pass the Turing test with honor instead of being a programmed imitation of intelligence.
John Clavin
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Spiritual test of intelligence
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I would be interested in Ray's view of the spiritual capacities of the spiritual machines (strong AI) he is talking about.
For example, deep states of meditation are states in which the human mind is tacidly aware of its own presence, or even tacidly aware of its direct environment (similar to athletes experiencing "flow"), while all thought-processes have stopped.
The mind is still, in abeyance. But consciousness is not in abeyance, on the contrary, there's a strong sense of being, of precence.
I do not know how this state could be objectively tested, but maybe someone will be able to devise a Spiritual Test, not just a mental test like the Turing and Feigenbaum tests. These are tests that assess the development of mind, our level of thinking.
A machine can respond to and even interact intelligently with its environment, but will it be able to answer not just intellectually, the questions Who am I or What is consciousness?
It seems to me that a truly intelligent and spiritual machine will inately have a longing to explore these questions and have them answered.
Will machines, once they awaken to true intelligence (strong AI), have a natural desire to answer these spiritual questions?
Will a new generation of (machine-)philosophers or rather, (machine-)Buddhists be born?
My Spiritual Test for strong AI would be simply to see if these - for humans quite natural - spiritual questions 'spontanuously' arise in our intelligent machines.
Are they even interested in spirituality, are they even interested in ethics, humanist thought, or practicing compassion?
It seems to me that a truly intelligent machine should demonstrate a natural concern for these things. Otherwise "intelligence" would mean cleverness, rather than intelligence.
Timothy Schoorel |
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Re: Why we need a Turing Test
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The logic of runaway AI is valid, but we still need to consider the timing. Achieving human levels in a machine will not immediately cause a runaway phenomenon. Consider that a human level of intelligence has limitations. We have examples of this today'about six billion of them. Consider a scenario in which you took one hundred humans from, say, a shopping mall. This group would constitute examples of reasonably well educated humans. Yet if this group was presented with the task of improving human intelligence, it wouldn't get very far, even if provided with the templates of human intelligence. It would probably have a hard time creating a simple computer. Speeding up the thinking and expanding the memory capacities of these one hundred humans would not immediately solve this problem.
depends how SAI is built.
If it's built to be a copy of human brain, then i agree it will not immediately runaway.
but suppose it's built from simple accalerating automata?
This is evolutionary comput8ng. genetic algorithms.
It self-modifies UP to human intelligence.
AND THEN CARRIES ON.
there's nothing fundamental about the limit of human intelligence.
It stopped evolving because it could master the environment, especially predation, without having to modify, and could keep repeating the blueprint for 100,000 years, though the body modified viz a viz germs.
but runaway machine intelligence is not like that.
Intelligence is a product of memory and speed..as you will see if you walk round a zoo.
We use it to solve the problems of survival through reproduction, and lately of ataempting personal indefinate survival.
Here the species is getting more intelligent, partly by numbers, mainly by tooling up and techniques.
But evolutionary intelligence is 'adding on more skills' - nothing more.
I also dont see a limit to intelligence as it should be able to solve any possible problem capable of solution.
But I do see a danger of runaway, and I doubt we will have anytime from launching a pathogen like software that germinates intelligence to when it has soared past a Singularity and metabolised the universe.
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Re: Why we need a Turing Test
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Why do you presume that machines won't have mental states?
If you are a materialist and think that the universe is made up out of matter, than there is really not much difference between a human being and a machine! Everything, including the human brain, is made up out of atoms or some even smaller building blocks of matter. So from a materialist's point of view, human beings and machines are indeed 'made of the same stuff'.
If you are a 'spiritualist' and think that everything is an expression or modification of consciousness itself, than the same logic says that there is really no fundamental difference between human beings and machines and that therefor there's no reason why machines couldn't become truly conscious and intelligent, like human beings.
The 3rd option is believing, like Descartes did, that there is a fundamental divide between matter and mind. But then, what is the mechanism by which the mind influences the body? This question has, as far as I know, never been answered in a philosophically satisfactory way. Also, there are so many indications now that mind originates from the brain.
For me, 'Cogito ergo sum' (I think, therefor I am) is not all that meaningful. There's no denying that machines can think. The question is whether machines can think consciously, can be conscious, that's the real question!
More meaningful than 'Cogito ergo sum' I think would be 'Cognito ergo sum': I am conscious, therefor I am.
The whole point of the effort towards Strong AI is that we somehow manage to transfer our intelligence and consciousness to machines, to a different platform from biology. A platform that is not as vulnerable and doesn't have all these biological limitations.
The question that needs to be answered is how we would know if we have succeeded! The Turing Test is one ingenious way to test an AI.
But maybe the AI itself will come up with an even more convincing test!
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Re: Why we need a Turing Test
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"People have tried to find a physical substance called consciousness for ages, but have failed miserably. "
Speak for yourself, zombie. I wake up every morning.I suspect you do too.
"Now you are saying: consciousness is not material, but none the less physical, like the surface tension of water. "
Correct. You got there.
"But I don't see what's so very special about brain cells that would create this consciousness. "
You don't ? Try shooting yourself in the head. See how conscious you feel afterwards.
I think you are falling into a common trap - "the emperor wears no clothes' trap of AI.
Don't get caught in this silly trap of pretending consciousness doesn't exist. Just accept it does. Subjective mental states are one of, if not the most, exciting objective facts in the universe.
It can seem difficult to get your head round the fact of 'proving'that consciosuness exists.
But philosophically it's no problem. If I'm conscious ( and I KNOW I am) then unless I think I'm very special, and I believe other people exist, then I can believe that other people are conscious too.
All knowledge is based on belied, don't forget. You can't see that matter is made of atoms with your senses but it would be strange, given the evidence, if atomic theory was not true.
An atomic radius can be measured, but only if we assume that atomic theory is actually correct. To measure something in science, you need a theory. We just don't have a full theory yet, and so 'measurement' is a problem.
Although in practice, anaesthetists measure consciosness already, in a rather blunt way. But measure it they do.
"why your idea is rather arcane to me, because you present no theory other than your assertion that consciousness has a physical causality. "
Stop listening to silly AI theorists and start listening to common sense.
There is NOTHING ARCANE about physics. That's just a silly thing to say isn't it.
If my theory is that mental phenomena are caused by physical forces, that's all I have to say !
There is no neuroscience to show exactly how. My belief is caused by a KNOWLEDGE of the EXACT way that computers work.
Saying that I don't know exactly how the brain works (nobody does) is not the same thing as admitting the computer is a brain.
On the contrary, unlike brains, computers are understood 100%. And computers are mathematical systems incapable of creating physical phenomena and semantic thought characteristics such as the experience of colours such as blue.
In short, as I've said before, if you believe that computers think then it's up to YOU to justify it. And that starts by accounting for how a syntactical system generates semantic.
And you know what ? You won't do it. I wrote to Marvin Minsky a few times. We had a nice chat until I asked him that question and funnily enough - the emails stopped !
"But it is really the level of complexity that determines intelligence and therefor the possibility of human consciousness. "
Another common AI misconception. If you cant show what 'complexity' is, or demonstrate how a 'more complicated' program can iteratively increase its causal physical powers, then the argument is a total red herring.
Complexity is not causal. In any case no program is anything other, I repeat, than a changing series of 0's and 1's in memory. It's difficult to arge that any program is more complicated than another EXCEPT from the programmer's perspective, which is irrelevant.
"But I am interested to hear what physical property of the brain you think produces consciousness and how that works. If you're really onto something, it would be a Nobel-prize contribution! "
The physical property of the brain that causes consciousness is most likely the property that causes consciousness. Dont forget the universe is not a system : its a semantic block that does what it does. Physics is a system. If physics can't handle consciousness (and I dont see that it can't to be frank) then physics has to change to accomodate it.
Or , like computer scientists, they can still insist the emperor wears no clothes.
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Re: Why we need a Turing Test
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The fact that you or I wake up in the morning is by itself no proof that consciousness is a physical property.
We thank our existence to complexity as much as to physics. Sure, we are carbon-based life, but carbon itself is not alive, intelligent or conscious like human beings are.
If carbon itself is not inherently conscious, or even alive, then how can a human suddenly become alive, intelligent and conscious?
Complexity seems to be the clear answer, rather than some unexplained and unknown physical property of carbon or matter in a more general sense.
If it is really complexity that accounts for our human life, intelligence and consciousness, then a similar level of complexity in a machine may cause it to be alive, intelligent and conscious as well.
If complexity is the key ingredient for intelligence and consciousness, then it is irrelevant that a computer is in essence a logical system. Because consciousness probably is not a consequence of physics per se but a consequence of complexity.
In this way computers could really become subjectively conscious, think and have a mind, like human beings.
This is not the same as saying that consciousness does not exist or isn't a real phenomenon, quite the contrary, but it emerges as a consequence of complexity rather than physics.
Anaesthetists can only measure so-called neural correlates of consciousness, but not consciousness itself. They are simply measuring processes in the brain.
It is just your presumption that they must be measuring consciousness because you first of all presume that consciousness is a physical property of matter.
AI research is showing that intelligence is a function of complexity and that it has nothing to do with some unknown property of matter. If machines achieve much higher levels of intelligence and as a consequence show all the signs of being self-aware, we can not insist that they are not conscious.
Unless maybe some new physical theory can prove that consciousness really is a property of physics. But to date there is no theory of physics that accounts for consciousness.
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Re: Why we need a Turing Test
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"The fact that you or I wake up in the morning is by itself no proof that consciousness is a physical property. "
It's certainly no proof it's a mathematical or computing property either, but consciousnes is highly inconsistent with computation.
"We thank our existence to complexity as much as to physics."
the last time i looked "complexity" was neither a) a causal force b) a property of anything or c) defined. You define it : you quantify it : stop talking vaguely. The onus is on YOU.
"Sure, we are carbon-based life, but carbon itself is not alive, intelligent or conscious like human beings are. "
I have gone over this point before. A water molecule is not "wet" but a large number of them have the property of "wetness". The physical world is full of aggregate properties such as solidity, liquidity etc.
"Complexity seems to be the clear answer,"
Clear ? What on earth is clear about what you are saying ? Handwavey references to 'complexity' ? A carbon atom is very complicated. If you don't believe me study quantum and particle physics for a while. So according to you, a carbon atom IS conscious as it's so 'complex'.
"If it is really complexity that accounts for our human life, intelligence and consciousness, then a similar level of complexity in a machine may cause it to be alive, intelligent and conscious as well. "
Come on. This is drivel.
"but it emerges as a consequence of complexity rather than physics. "
How ? explain an iterative mechanism. Say we have a relatively simple logical system X. It is beneath conciousness. We know how computers work : we know everythig about them. So you should be able to tell me what the next step is to turn X into a conscious system, Y. Give me an idea.
"Anaesthetists can only measure so-called neural correlates of consciousness, but not consciousness itself. "
Physicists generally only measure 'correlates' of anything they ever observe. An atomic radius is not measured with a ruler. It is measured with highly derived system of wave mechanics based upon the assumption that atomic theory is correct. Most non-sense data measurement is 'correlated' and rarely is anything ever measured 'directly'. That is science.
"AI research is showing that intelligence is a function of complexity "
What has intelligence got to do with consciousness ? Explain the link.
"But to date there is no theory of physics that accounts for consciousness. "
But physics is compatible with consciosness nonetheless. Computation is 100% incompatible with phenomenological, semantic mental events. I asked you before to show how semantic arises from syntax. Make that your next challenge.
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Re: Why we need a Turing Test
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"...The whole point is that the mind does not have a relationship to the brain in the same way that a program does to a computer."
You are absolutely correct!
However, your selectively singling out software instructions as the basis for the implausibility of a "computerized" brain is an unfortunate oversight. If that were all to consider in a fair argument then I would agree with you. But it's not all to consider, so ...
You seem to have stopped short in your thinking process about thinking processes. A software program can be likened to a desire. Is your mind composed of and manifest by desires alone? That is basically what you are trying to state for the case of a would-be computer mind. C'mon now, let's play fair.
Consider that there is not only the program instructions. In the case of an AI, the program instructions would simply be the "virtual engine" that "virtually reshapes" the computer hardware that runs it. Well, this virtual engine does nothing on it's own. Just as in the real world, computer programs don't just sit and spin. They work on data of some sort (memory). The computer also has various inputs that feed into the process. There are also outputs that feed back into the system as well as outside of the system.
"Thinking is a physical act. Think of a thought as a blue ball made of a solid. You would never thinkl of a solid ball as a program, as a program is mathematical and has no physical existence."
Ah, but a whole computer system running the program? You see, a computer program is nothing without hardware to execute it much as your memories are useless if not stored in your neurons and acted upon by your fuller mind which is manifest by your collective brain neurons (hardware), other experiences, hopes, and dreams (software and memory stored in the connections of neurons).
The whole process manifests something emergent beyond the parts: It is not just software instructions, nor processor, nor hardware memory, nor data, nor input, nor output; it is a working whole. Take away one piece and you lose the whole.
All of these things together are more of what a "computerized mind" should be likened to if you were presenting your argument fairly. Singling out software instructions as the sole basis for a computerized mind is easy to debunk. Quite a bit more difficult when all the true components are taken as a whole.
Certainly today's level and form of computing is not what it needs to be to pass a Turing Test. And I agree that a Turing Test tells us nothing certain and only allows us to assume this or that regarding the AI's ability to relate as a human. But, just as a complete operating "computer system" is more than the sum of its parts, pry open your skull, grab a chunk, toss it in the air and see if you remain "you". If not that, try bashing yourself several times in the head to induce amnesia and see if you can make sense of your current living trajectory.
"THinking is physical without being material. Thought acts, epistemelogically spealking, have no relationship to mathematics, hence their absolute forms (the experience of the colour blue, for example)."
You, personally, are born pre-wired to experience blue in a certain way. Your experiences may modify that somewhat, if for instance, someone hits you square in the face with a blue baseball bat. You are confusing genetics with thinking. A child that is not colorblind can't call something red or blue but they certainly know and experience the distintion. However, a child doesn't know or experience the blue sky except through experience. Similarly, an AI can be built to have defacto (hardwired) experiences of many things and only come to "know" others through data or experience.
"The objects of thought acts CAN have relationships to logical entities. But thoughts acts themselves are as cosmologically real and substantive as footballs."
Ummmm, so are computer outputs that cause other computer actions, i.e. updating memory with new data, outputing stuff to the screen, causing a speaker to vibrate to produce a waveform of what we might call a "beep". All of these things are as cosmologically real and substantive as footballs, wouldn't you agree?
In summary "You" is manifest from your physical brain no less than a computerized mind would be manifest from its physical brain. Look beyond thinking of an AI built from a Radio Shack TRS-80. Look beyond thinking of an AI built from technology that is 100 times faster than what you are working on today. Look beyond thinking of an AI built with von Neuman architecture. Look beyond. Just look beyond. You are trapping and stifling your own creativity and reasoning otherwise. |
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Re: Why we need a Turing Test
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Alright. Let me explain in simple sentences.
A computer is a logical, mathematical construct. Computational theory does not emanate from the physical sciences : it emanates from the mathemtical sciences.
A computer can be made of anything : old tins and bits of string , or from water running through valve systems.The only thing necessary is to map a physical token (i,e a voltage, or the strength of water flow in a valve system, ir indeed absolutely anything) to a symbol such as a 0 or 1. That is all computers need.
We could take a program of a human mind (a memory map of 0's and 1's and map it to, for instance, the quantum states of atoms in a wall to every sequence of that program.
Thus the wall in my bedroom is now 'running a program of the human mind'. Why ? Because I have decided to map the program to the wall.
I can look at a cup and decide that the cup represents '1'. The cup is 'running a program'. Not a very interesting one admittedly, as it just says '1'.
In other words, it's me, as the observer, who decides what the program is. The answer to your example is that of course the machines are running a program when people go out the room. They are running one imposed on the physical structure of that machine by the machine's designers.
They are also running an infinity of other programs at the same time. I could decide that the voltage levels that currently map to 0's and 1's be inverted. Now the machine is running a program of my choice, not the designers.
Are you getting the picture ? It's up to me what the program is as I decide how to map the symbol to the physical characteristic.
Imagine a martian came to earth and saw your computer and imagine the human race was wiped out. There would be no knowledge that the a voltage of -.1 V mapped to a '1' and voltage of +.1V mapped to a '0'.
From their epistemic perpective then no, the computer would not be running a program, or at least not the one your designers had constructed. That is because hey lack your mapping rules.
In other words, the computer as a physical machine remains the same and does the same things, but without an observer WITH MAPPING RULES there is no program.
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Re: Why we need a Turing Test
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You are getting there, although you are asking a slightly different question.
The requirement for the "two brains" requirement is, frankly, the most ridiculous consequence of AI beliefs, although some do argue it.
It is, of course, a nonsense. It means that in your brain you have to have an additional 'person'(or sub-person) in your head who understands the brain's mappings.
The main reason that we can conclude that mental events are not computational is that they have a form which is absolute. The experience of blue is absolute , not quantitative. It dosn't feel like ' a bit of red', for instance, or 'similar to sexual arousal' or 'has facets of the feeling of depression'. It feels like it feels and it feels like nothing else. That is incompatible with computation, which is syntactical and mathematical.
It is the same as the difference between a piece of solid matter and the number 1 : semantic and syntax.
The requirement to have a second brain in the head gets us nowhere, of course.
We simply ask "what is this sub-person who understands the brain's mappings?". If it is a computer program then we are back to square one, namely we have a computer program ( the sub person ) that refers to the rest of the brain as a gigantic digital memory. And who understands how the "sub-person" is mapped ? Who 'runs' the sub-person ?
May be we need three brains then, an additional brain to understand how the second person's mappings are set up. Or maybe not. |
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Re: Why we need a Turing Test
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"From our perspective, everything is relative. "
What is blue relative to ?
"Au contraire, people _do_ describe partial and hybrid experiences. Ever heard of "bitter-sweet"? Or "mixed feelings"? "
I repeat then, what is blue relative to ?
People don't describe hybrid experiences, they describe the simultaneous experience of different feelings. As they say in chemistry, a mixture is not a compound, it is a mixture.
"1. Is a human psyche a system of non-random transformations and/or movements of matter and/or energy in a human brain? "
What is a 'psyche' - do you mean a 'mind'?
The answer is the mind is (probably - we don't know what brains do of course, unlike computers) the physical and mental phenomena associated like an aggregate property of a group of brain cells. What the significance of 'randomness' in your scheme is I don't know.
There are other examples of physical phenomena that are aggregated - for instance, the surface tension of a liquid. We don't speak of a water molecule as being 'wet', but we think of a body of water as being 'wet'.
Similarly, no one neurone is 'conscious', but a group of them can be.
But this is all speculation. The neuroscience isn't there yet. Computers on the other hand are not the subject of behavioural speculation at all.
"2. Is a computer program a system of non-random transformations and/or movements of matter and/or energy in a (silicon) processor?"
No. A computer program is a time based development of a series of 0's and 1'.s
I like the Markov definition of a computer :-
"a string rewriting system that uses grammar-like rules to operate on strings of symbols. "
see http://en.wikipedia.org/wiki/Theory_of_computation
I think your obsession with silicon indicates you need to read up on what a computer actually is, my confused chum.
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Re: Why we need a Turing Test
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"What is blue relative to ?"
The observer's eyesight quality - some people don't distinguish tones, others don't even distinguish different fundamental colours. Probably no two people experience blue in exactly the same way. When they talk about "blue", they can only be said to _approximately_ understand eachother.
"People don't describe hybrid experiences, they describe the simultaneous experience of different feelings."
Your proof of this being...?
"What is a 'psyche' - do you mean a 'mind'?"
Yes, I think I meant approximately ;) the same thing you mean when you say "mind".
"The answer is the mind is... the physical and mental phenomena" etc.
1. This definition is circular: you're using "mental" to define "mind".
2. This can't be the answer to my "yes"/"no" question. I'm still waiting.
"What the significance of 'randomness' in your scheme is I don't know."
3. A system of transformations like in the above definitions is "non-random" if the majority of its elementary tranformations have non-uniform probability distributions for their possible outcomes. (Think of a mechanism of multiple flipping coins - if, say, 60% of those coins are loaded and have heads/tails probabilities other than 50/50, you have a non-random system of transformations.) Complete this with the property that all transformations in the system are connected (so it's not just a collection of independent coins) and re-read my first question.
"No. A computer program is a time based development of a series of 0's and 1'.s"
Fair enough. Second question rephrased:
2. Is [the physical process implementing what humans call "a computer program"] a system of non-random interconnected transformations and/or movements of matter and/or energy in a (silicon) processor?
"I think your obsession with silicon indicates you need" etc.
No obsession involved. I'm trying to keep things clear, to have a grip on what we're talking about. I may be using a particular example of computing hardware, but I'm still talking about computing in general terms, so my statements should remain as true for silicon transistor arrays as they are for any other hardware. |
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Re: Why we need a Turing Test
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Jeez, dont fly off the handle brother. It's not THAT important.
Firstly it is perfectly possible to have questions that are not suited to yes or no answers. 'Yes' or 'No' imply that the question has an unambiguos status given the surrounding context. Not replying 'Yes' or 'No' is simply a way of clarifying context. Ask a lawyer. Ask me, I'm one.
Secondly, demanding the terms in which a question can be answered is a particularly unsubtle rhetorical trick which I refuse to accommodate unless permitted to establish context first.
Thirdly, despite the fact that I am unwlling to put up with a determination of terms, I consider my answers to your questions unambiguous in the extreme. I dont think a computer program is physical in any way, and your rather unsibtle efforts to prove that they do will amount to nothing. In any case, establishing what a computer is, or amounts no, has nothing whatsoever to do with establishing what brains do, another pointless linkage on your part.
Epistemelogically brains and computers are in completely different classes : chalk and cheese. A computer is DEFINED by function. Therefore its modus operandi is 100% utterly unambiguous.A brain is a naturally occuring physiological organ whose 'functions' (inasmuch as that word is appropriate) can only be discerned by scientific enquiry. The way it works CAN ONLY BE ASCERTAINED BY SCIENTIFIC ENQUIRY. It cannot be done by simply assuming it to be a computer. That is patently silly and ridiculously presumptive.
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Re: Why we need a Turing Test
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jack_d:
I didn't quite know which post to reply to as I followed all of the discussions with Timothy and donjoe.
Your arguments are well taken. If I'm following you correctly you feel, as I do, that there's just something particularly interesting about the human brain that causes human consciousness to emerge. Further, I imagine that you'd agree that a single functioning neuron does not bring about this awareness and consciousness. If I am mistaken, please correct me.
As I understand your points, a computer making ones and zeros can not bring about consciousness. I'm not really sure about what the future holds when current processing models are computing in the terahertz, petahertz, exahertz, or zettahertz ( 10^-21 vs 10^-9 for gigahertz) when things go primarily optical, but for now I'd have to say that I agree with you. Current von Neuman computer systems working at their current speeds and architectures most likely have no (or very little) capacity for consciousness.
However, I'm curious about all of this synthetic intelligence and consciousness talk and I would appreciate your assistance in tearing down the most popular scenario of how its proponents say it will occur.
They (proponents of a conscious, super AI) speak of alternate forms of computing rather than the von Neuman type as the Holy Grail.
I imagine that in the advancement of computing technology, alternate forms of computing architecture is what research of natural systems is leading to. Given the frantic pace of discovery and advances in the fields of optical and quantum computing, nanotech, and micro-biomechanical and chemical systems it would be foolish to think that von Neuman computing will remain the standard for centuries or decades to come, wouldn't you agree?
I think we would all like to one day be operating our own personal palm-sized mega systems that outperform our current systems by a hundred thousand fold one day, I'm sure. The new multi-cores are great, but I personally don't think they'll get us there... but these new emerging technologies probably will.
The advent of current day synthetic retinas and other sensory nerve replacements are evidence to a technological trend that presages that ?someday? in the future replication of the function and structure of a single neuron with advanced nano, chemical, optical, quantum, and MEMS technology will be a reality. Further this replicate model could, by way of its design, emulate structurally, chemically, and electrically any particular neuron in a brain; axon, dedrites, nucleus, synapse, neural response patterns, etc. Essentially creating a versatile synthetic replica of any biological neuron. This includes all electro-chemical processes, including DNA. If you doubt this please share your reasoning.
Now, if this neuron were made to be completely compatible with your human biology, and I could replace just one of your neurons with its synthetic equivalent (structure, neural response pattern, etc.) such that it handled all of the inputs and outputs as would the true original and neighboring neurons behaved and communicated as normal, do you think you would lose yourself, become someone else, lose consciousness, or die? If so, please explain with scientific reasoning.
If I were to replace a second, a third, a fourth...? You have another 50 billion or so to go (depending of course on one's usual rate of alcohol consumption).
At what point would you estimate that your consciousness would break down or turn into something else? 100, 1000, 10,000, 1 million, 1 billion, 50 billion? If you believe that a breakdown in consciousness would occur at some point when your natural biological neurons are being replace with their compatible functional equivalents please explain when and why in absolute scientific terms.
If you can not scientifically explain a breakdown then why can't I then, instead of inserting my duplicates into your neural structure just simply create a new, totally duplicate---brain---your brain? Is their something in the universe that will not allow this? Is there something that would not allow multiple "you"s? Consciousness is a locale issue. "You" would not be in two places at the same time. However, there would be two divergent "you"s (consciousnesses) that are thinking identically until unshared events cause them to get more and more out of sync. The last scenario, however, is not the point.
If consciousness is not a neuron or all of the neurons, connections, and physical activity then what is it?
Perhaps it's an emergent phenomenon and I think you sad the same in other terms. Emerging from what you might ask. Well, firstly, I'd like to posit that emergence is as fundamental a property of the universe as any other property you can name. Big Bang to Earthly Nature.
We're talking emergent systems here. Imagine, if you were able to replace H2O with a synthetic equivalent that had all of H2O's properties, exactly, would Mother Nature lie down and call it a day, or would she continue her delicate equilibrium dance without skipping a beat?
All of the universe is emergent from quantum processes that build emergent multi-layered structure after emergent multi-layered structure. Replace the quantum processes with an equivalent and you still have the universe.
Consciousness, the mind, is emergent from the "processes" of the brain. Create a synthetic brain what do you think will happen? Nothing will emerge? If nothing, why? Are we too bio-human-centric to extrapolate otherwise?
A Turing Test will only allow us to ASSUME positively or negatively the consciousness of any entity synthetic or otherwise. That is the best we can do even with fellow humans WITH FULL FACULTIES: They act like me, walk like me, talk like me, speak like me, laugh like me, cry like me, and other ways respond like me... hmmm... they must think like me... they must be aware like me. We can relate. Super AI will go beyond human levels of awareness and we will no longer be able to relate on the same level, kinda like you to a worm. But because knowledge of what it is to be human is built in the SAI will be able to relate some things in its realm to us, but the far majority it will not. We will endeavor to encorporate it into ourselves, however. Problem solved.
It's nice being human. As far as we know right now, we and our fellow aware earth-bound creatures have brought intent and awareness to a universe that was once only possibility and probabilty.
Actually, and this is perhaps a topic for a different forum, I believe that one day we'll discover that multi-layered emergence entails multi-layered intellect and awareness of DIFFERENT KINDS and on different time scales. I stress "different kinds" to avoid the tendency to think in human terms and interpret that statement to imply human forms of thinking and awareness. Think of the complex structures of the universe and it's interactions of gas, electromagnetism (light, infrared, radio, x-ray, gamma-ray, etc), chemisty, and gravity. Something is emergent there beyond the parts (and there's a heck of a lot more parts and interactions than in the human brain) that we can see and feel. Galaxies, black holes, etc., may all have their own forms of emergent awareness and intelligence on physical scales and time scales far unlike our own. Now, go figure out a Turing Test for that ;0)
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Potential of Computers for Generating Consciousness
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Given that superintelligence will one day be technologically feasible... This begs the definition of intelligence. As I understand it, Turing Test looks at a system (from the outside) to see if that system behaves like a human. The minimum practical test is to get a computer to respond to typed text inputs with text responses which appear human in the eyes of a human.
http://cogsci.ucsd.edu/~asaygin/tt/ttest.html#intr o
Self-awareness is internal to a system, not observable from the outside. This is definitional. The fact it cannot be measured doesn't mean it's not part of human intelligence, unless you specifically define human intelligence to exclude self awareness.
From an external perspective, superintelligence might work at the text level someday. Even if superintelligence happens, the phenomenon of self awareness is not going to be a part of it anytime soon.
Just because a test cannot be devised for self awareness doesn't mean it's not a real thing. Human awareness obviously is real. If you ask me, it's pretty obvious my cats are self aware also. I don't think we are any closer to understanding physics self awareness today than we were 100,000 years ago.
if you cannot test for self awareness, you cannot say the intelligence is human, again, I'll remember to see where we stand with Turing machines for text in 2030 (25 years from now). Ramping up that technology into a fully functional humanLike machine can be made to work like an android, indistinguishasble from a real human unless you cut into it...or you replace the human brain with something electronic, so even the whole body functions as if it were a human inside, but the brain is not alive.... that system appears human in every way, thus meeting the Turing Test.
create machines that behave like humans in their language or other prcessing,
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Re: Potential of Computers for Generating Consciousness
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Yes, I am a computer scientist.
Personally, I think a fly probably has a simple kind of self-awareness, but I don't know. I feel just about certain my cats have some of what I experience as self-awareness, but I don't know. I consider myself self-aware, but I cannot prove it to anyone who doubts it.
There is a reason I cannot prove my self-awareness to anyone else. Self-awareness is observable only by the self, nothing/no one else. I am aware of my being and you are not. All you can know of me is my behavior from an external perspective. You cannot replace your awareness with mine, just like I cannot mine with yours. We can communicate as one being to another. The phenomenon of self awareness is outside the scope of the energy/mass system described by string theory, quantum mechanics, general relativity, Maxwell's equations, laws of motion, or any other system of math.
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Re: Try The Opposite
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If I am up to speed, the current hypothesis is I am not sentient. I am not aware. I am a computer.
The cultists must have seen this 'paradox' before. Surely, somewhere in the mountain of material gurged forth by Mr. Kurzweil, there must be a proof that I am sentient, or not, whichever I am. I know whether I am sentient or not, and whether I am a computer or not, but do they?
This must be the reality-paradox. That would explain it. You take reality, make it something else, then it's not real, even though it is reality. People who cannot see this might be aware, but at a lower level, because it is so obvious how this explains everything, if you think about it. It is the one scientific truth that cannot be proven by experiment. :-) |
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Re: Potential of Computers for Generating Consciousness
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Personally, I think a fly probably has a simple kind of self-awareness, but I don't know. I feel just about certain my cats have some of what I experience as self-awareness, but I don't know. I consider myself self-aware, but I cannot prove it to anyone who doubts it.
There is a reason I cannot prove my self-awareness to anyone else. Self-awareness is observable only by the self, nothing/no one else. I am aware of my being and you are not. All you can know of me is my behavior from an external perspective. You cannot replace your awareness with mine, just like I cannot mine with yours. We can communicate as one being to another. The phenomenon of self awareness is outside the scope of the energy/mass system described by string theory, quantum mechanics, general relativity, Maxwell's equations, laws of motion, or any other system of math.
The only reason you call another entity 'self aware' is because;
a) you call yourself 'self aware' and b) |
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Re: Potential of Computers for Generating Consciousness
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Personally, I think a fly probably has a simple kind of self-awareness, but I don't know. I feel just about certain my cats have some of what I experience as self-awareness, but I don't know. I consider myself self-aware, but I cannot prove it to anyone who doubts it.
There is a reason I cannot prove my self-awareness to anyone else. Self-awareness is observable only by the self, nothing/no one else. I am aware of my being and you are not. All you can know of me is my behavior from an external perspective. You cannot replace your awareness with mine, just like I cannot mine with yours. We can communicate as one being to another. The phenomenon of self awareness is outside the scope of the energy/mass system described by string theory, quantum mechanics, general relativity, Maxwell's equations, laws of motion, or any other system of math.
The only reason you call another entity 'self aware' is because;
a) you call yourself 'self aware' and b) |
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Re: Potential of Computers for Generating Consciousness
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as an expert, I am saying to you, computers cannot be self-aware in the way my cats and I are. You cannot argue our awareness out of existence. At least not mine.
If you want to argue about whether my cats have awareness pretty much the same way people do, I am all ears (eyes I guess). My opinion: not only are cats self-aware, they are people, just the way African Homo Sapiens were people, even when they were not considered as such.
I think rocks are self aware, at a really faint level.
I think self awareness is outside the scope of energy and mass studied by mainstream physicists. Mainstream physicists are those focused on string theory, particle physics, relativtiy, and anything else that is provable by experiment.
Some reality has a nature that makes it unreproducable by experiment. Self awareness arises from that domain.
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Re: Potential of Computers for Generating Consciousness
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regarding the considerable portions of deft logic, mixed with simpering antagonistic arrogance and sheer dunderheadedness:
a) I believe you're both poor attempts to pass a Turing Test, so don't take this as a personal attack but rather, an attempt to describe your affect as artificial constructs.
b) Things we are not aware of don't exist, for that is how the awareness/reality interface works, with or withut paradox.
c) One artificial construct's assertion that awareness is (in so many words) 'that which touches but we cannot (yet) touch', is another man's phlogiston.
d) Another artificial construct's assertion that awareness is physical while the mathematical logic of a computer is metaphysical, places one's awareness in the curious position of noting that this thing that can't be awareness because it isn't physical (a computer program), only exists within human awareness which, I heard one artificial construct say, is physical.
That you may be able to connect the logical dots of these statements may suggest that you are truly intelligent and aware, but the fact that you may not be able to do so doesn't mean that you're not intelligent and aware.
There's no denying, though, that you're both rude and not in the Don Rickles manner which makes one laugh, but in the manner of a Turing-bot that hasn't yet learned how to...
For Christopher and extrasense, a perspective on the Turing test ideal:
Fooling humans is easy. Relatively speaking, that is. Fooling an AI to believe it's sentient is another matter. The Turing Test gives way to the Descartes Dilemma, which is passed by being unable to decide one way or another while remaining firmly convinced that one IS (sentient).
Not that we will be likely to tell.
One hand clapping and all that and how ironic that one has to be aware to doubt that one is aware?
Half a year late to the party,
theblueraj
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Re: Potential of Computers for Generating Consciousness
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Hello --
I would like some of this action. So, if I agree with you that a computer is nothing but a mathematical and logical construct - then tell me: why can not such a construct be a mind ? It seems that your explanation is that it takes an observer to make sense. Fine, but what if such is also true of us ? And who would this "Uber Observer" be ? God ? Hmmm...
Heinrich Hertz believed that even static, yet very complex mathematical equations possessed conciousness. Since this low tech forum does not allow me to see the thread as I write, I must paraphrase. But the one fellow did point out what I believe is the key, not just to mind - but to life as well. Complexity. Once a pattern of symbols becomes sufficiently complex, and especially when it becomes a pattern that adapts and changes itself - it begins more and more to exhibit the qualities of mind and life.
And in the end, no matter what you all argue - the outward signs of conciousness and life are all we have to go on to settle this argument.
Let us say for instance that we run across (as has been explored ad nuseum on sci-fi shows) a coherent and evolving complex organized energy form that outwardly exhibits the qualities of sentient life ? Now, these energy forms may appear to have no mechanism or explanation for HOW they are concious - they, like us - may not even be able to explain it. Nevertheless, they BEHAVE as living and thinking creatures. Are they ?
That is the ONLY test that can be applied to ANY coherent system with regard to sentience and life. That is, the test of common sense. If it acts alive and sentient, then it IS alive and sentient. This applies whether it's biological, mechanical, energy forms, or some as yet un-discovered life form that we can't even imagine.
YOURS -- Christopher Doyon
---------------------
Saint Stephen AI Project
www.SaintStephen.info
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Re: Why We Can Be Confident of Turing Test Capability Within a Quarter Century
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Because the definition of the Turing test will vary from person to person, Turing test-capable machines will not arrive on a single day, and there will be a period during which we will hear claims that machines have passed the threshold. Invariably, these early claims will be debunked by knowledgeable observers, probably including myself. By the time there is a broad consensus that the Turing test has been passed, the actual threshold will have long since been achieved.
A few brief thoughts about strong AI and the Turing test:
1. Capability (as Kurzweil implies) is a crucial, if not obvious element;
2. Possible reponse patterns--(a.)no reponse (unwilling, or unable to repond,(b.)reponse (unintelligle [to the interviewer], intelligent/rational [w/in the interviewer's personal cognitive capability, or other test parameters]).
3. Once a strong AI develops/evolves, the capability to successfully pass a Turing test doesn't mean it will be willing to participate;
4. If an AI doesn't answer, does that mean it isn't intelligent?
5. If it answers, but sounds neurotic/psychotic, does that indicate lack of intelligence, or do we seat them in the crazy-not-stupid section?
5. Maybe, the ability to play well with others (i.e., Humans) should be an important design element.
6. At first, an AI may not be able to respond then (as it evolves) it will want to respond then it may choose not to respond.
7. Where is strong AI at today, and how do we know?
Bon apetit - Nangenai
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