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“By 2029 no computer – or « machine intelligence » – will have passed the Turing Test.” Detailed Terms »

(Bet 1 Duration 27 years (02002-02029))


Mitchell Kapor

Ray Kurzweil

STAKES $20,000

will go to The Electronic Frontier Foundation if Kapor wins,
or The Kurzweil Foundation if Kurzweil wins.

Kapor’s Argument

The essence of the Turing Test revolves around whether a computer can successfully impersonate a human. The test is to be put into practice under a set of detailed conditions which rely on human judges being connected with test subjects (a computer and a person) solely via an instant messaging system or its equivalent. That is, the only information which will pass between the parties is text.

To pass the test, a computer would have to be capable of communicating via this medium at least as competently as a person. There is no restriction on the subject matter; anything within the scope of human experience in reality or imagination is fair game. This is a very broad canvas encompassing all of the possibilities of discussion about art, science, personal history, and social relationships. Exploring linkages between the realms is also fair game, allowing for unusual but illustrative analogies and metaphors. It is such a broad canvas, in my view, that it is impossible to foresee when, or even if, a machine intelligence will be able to paint a picture which can fool a human judge.

While it is possible to imagine a machine obtaining a perfect score on the SAT or winning Jeopardy–since these rely on retained facts and the ability to recall them–it seems far less possible that a machine can weave things together in new ways or to have true imagination in a way that matches everything people can do, especially if we have a full appreciation of the creativity people are capable of. This is often overlooked by those computer scientists who correctly point out that it is not impossible for computers to demonstrate creativity. Not impossible, yes. Likely enough to warrant belief in a computer can pass the Turing Test? In my opinion, no. Computers look relatively smarter in theory when those making the estimate judge people to be dumber and more limited than they are.

As humans:

  • We are embodied creatures; our physicality grounds us and defines our existence in a myriad of ways.
  • We are all intimately connected to and with the environment around us; perception of and interaction with the environment is the equal partner of cognition in shaping experience.
  • Emotion is as or more basic than cognition; feelings, gross and subtle, bound and shape the envelope of what is thinkable.
  • We are conscious beings, capable of reflection and self-awareness; the realm of the spiritual or transpersonal (to pick a less loaded word) is something we can be part of and which is part of us.

When I contemplate human beings in this way, it becomes extremely difficult even to imagine what it would mean for a computer to perform a successful impersonation, much less to believe that its achievement is within our lifespan. Computers don’t have anything resembling a human body, sense organs, feelings, or awareness after all. Without these, it cannot have human experiences, especially of the ones which reflect our fullest nature, as above. Each of knows what it is like to be in a physical environment; we know what things look, sound, smell, taste, and feel like. Such experiences form the basis of agency, memory and identity. We can and do speak of all this in a multitude of meaningful ways to each other. Without human experiences, a computer cannot fool a smart judge bent on exposing it by probing its ability to communicate about the quintessentially human.

Additionally, part of the burden of proof for supporters of intelligent machines is to develop an adequate account of how a computer would acquire the knowledge it would be required to have to pass the test. Ray Kurzweil’s approach relies on an automated process of knowledge acquisition via input of scanned books and other printed matter. However, I assert that the fundamental mode of learning of human beings is experiential. Book learning is a layer on top of that. Most knowledge, especially that having to do with physical, perceptual, and emotional experience is not explicit, never written down. It is tacit. We cannot say all we know in words or how we know it. But if human knowledge, especially knowledge about human experience, is largely tacit, i.e., never directly and explicitly expressed, it will not be found in books, and the Kurzweil approach to knowledge acquisition will fail. It might be possible to produce a kind of machine as idiot savant by scanning a library, but a judge would not have any more trouble distinguishing one from an ordinary human as she would with distinguishing a human idiot savant from a person not similarly afflicted. It is not in what the computer knows but what the computer does not know and cannot know wherein the problem resides.

Given these considerations, a skeptic about machine intelligence could fairly ask how and why the Turing Test was transformed from its origins as a provocative thought experiment by Alan Turing to a challenge seriously sought. The answer is to be found in the origins of the branch of computer science its practitioners have called Artificial Intelligence (AI).

In the 1950’s a series of computer programs were written which first demonstrated the ability of the computer to carry out symbolic manipulations in software in ways which the performance (not the actual process) began to approach human level on tasks such as playing checkers and proving theorems in geometry. These results fueled the dreams of computer scientists to create machines which were endowed with intelligence. Those dreams, however, repeatedly failed to be realized. Early successes were not followed with more success, but with failure. A pattern of over-optimism was first seen which has persisted to this day. Let me be clear I am not referring to most computer scientists in the field of AI, but to those who take an extreme position.

For instance, there were claims in the 1980’s that expert systems would come be of great significance, in which computer would perform as well or better than human experts in a wide variety of disciplines. This belief triggered a boom in investment in AI-based startups in the 1980’s, followed by a bust when audacious predictions of success failed to be met and the companies premised on those claims also failed.

In practice, expert systems proved to be fragile creatures, capable at best of dealing with facts in narrow, rigid domains, in ways which were very much unlike the adaptable, protean nature of intelligence demonstrated by human experts. As we call them today, knowledge-based systems do play useful roles in a variety of ways, but there is broad consensus that the knowledge of these knowledge-based systems is a very small and non-generalizable part of overall human intelligence.

Ray Kurzweil’s arguments seek to go further. To get a computer to perform like a person with a brain, a computer should be built to work the way a brain works. This is an interesting, intellectually challenging idea.

He assumes this can be accomplished by using as yet undeveloped nano-scale technology (or not — he seems to want to have it both ways) to scan the brain in order to reverse engineer what he refers to as the massively parallel digital controlled analog algorithms that characterize information processing in each region. These then are presumably what control the self-organizing hierarchy of networks he thinks constitute the working mechanism of the brain itself. Perhaps.

But we don’t really know whether « carrying out algorithms operating on these networks » is really sufficient to characterize what we do when we are conscious. That’s an assumption, not a result. The brain’s actual architecture and the intimacy of its interaction, for instance, with the endocrine system, which controls the flow of hormones, and so regulates emotion (which in turn has an extremely important role in regulating cognition) is still virtually unknown. In other words, we really don’t know whether in the end, it’s all about the bits and just the bits. Therefore Kurzweil doesn’t know, but can only assume, that the information processing he wants to rely on in his artificial intelligence is a sufficiently accurate and comprehensive building block to characterize human mental activity.

The metaphor of brain-as-computer is tempting and to a limited degree fruitful, but we should not rely on its distant extrapolation. In the past, scientists have sought to employ metaphors of their age to characterize mysteries of human functioning, e.g., the heart as pump, the brain as telephone switchboard (you could look this up). Properly used, metaphors are a step on the way to development of scientific theory. Stretched beyond their bounds, the metaphors lose utility and have to be abandoned by science if it is not to be led astray. My prediction is that contemporary metaphors of brain-as-computer and mental activity-as-information processing will in time also be superceded and will not prove to be a basis on which to build human-level intelligent machines (if indeed any such basis ever exists).

Ray Kurzweil is to be congratulated on his vision and passion, regardless of who wins or loses the bet. In the end, I think Ray is smarter and more capable than any machine is going to be, as his vision and passion reflect qualities of the human condition no machine is going to successfully emulate over the term of the bet. I look forward to comparing notes with him in 2029.

Kurzweil’s Argument

The Significance of the Turing Test. The implicit, and in my view brilliant, insight in Turing’s eponymous test is the ability of written human language to represent human-level thinking. The basis of the Turing test is that if the human Turing test judge is competent, then an entity requires human-level intelligence in order to pass the test. The human judge is free to probe each candidate with regard to their understanding of basic human knowledge, current events, aspects of the candidate’s personal history and experiences, as well as their subjective experiences, all expressed through written language. As humans jump from one concept and one domain to the next, it is possible to quickly touch upon all human knowledge, on all aspects of human, well, humanness.

To the extent that the « AI » chooses to reveal its « history » during the interview with the Turing Test judge (note that none of the contestants are required to reveal their histories), the AI will need to use a fictional human history because « it » will not be in a position to be honest about its origins as a machine intelligence and pass the test. (By the way, I put the word « it » in quotes because it is my view that once an AI does indeed pass the Turing Test, we may very well consider « it » to be a « he » or a « she. ») This makes the task of the machines somewhat more difficult than that of the human foils because the humans can use their own history. As fiction writers will attest, presenting a totally convincing human history that is credible and tracks coherently is a challenging task that most humans are unable to accomplish successfully. However, some humans are capable of doing this, and it will be a necessary task for a machine to pass the Turing test.

There are many contemporary examples of computers passing « narrow » forms of the Turing test, that is, demonstrating human-level intelligence in specific domains. For example, Gary Kasparov, clearly a qualified judge of human chess intelligence, declared that he found Deep Blue’s playing skill to be indistinguishable from that of a human chess master during the famous tournament in which he was defeated by Deep Blue. Computers are now displaying human-level intelligence in a growing array of domains, including medical diagnosis, financial investment decisions, the design of products such as jet engines, and a myriad of other tasks that previously required humans to accomplish. We can say that such « narrow AI » is the threshold that the field of AI has currently achieved. However, the subtle and supple skills required to pass the broad Turing test as originally described by Turing is far more difficult than any narrow Turing Test. In my view, there is no set of tricks or simpler algorithms (i.e., methods simpler than those underlying human level intelligence) that would enable a machine to pass a properly designed Turing test without actually possessing intelligence at a fully human level.

There has been a great deal of philosophical discussion and speculation concerning the issue of consciousness, and whether or not we should consider a machine that passed the Turing test to be conscious. Clearly, the Turing test is not an explicit test for consciousness. Rather, it is a test of human-level performance. My own view is that inherently there is no objective test for subjective experience (i.e., consciousness) that does not have philosophical assumptions built into it. The reason for this has to do with the difference between the concepts of objective and subjective experience. However, it is also my view that once nonbiological intelligence does achieve a fully human level of intelligence, such that it can pass the Turing test, humans will treat such entities as if they were conscious. After all, they (the machines) will get mad at us if we don’t. However, this is a political prediction rather than a philosophical position.

It is also important to note that once a computer does achieve a human level of intelligence, it will necessarily soar past it. Electronic circuits are already at least 10 million times faster than the electrochemical information processing in our interneuronal connections. Machines can share knowledge instantly, whereas we biological humans do not have quick downloading ports on our neurotransmitter concentration levels, interneuronal connection patterns, nor any other biological bases of our memory and skill. Language-capable machines will be able to access vast and accurate knowledge bases, including reading and mastering all the literature and sources of information available to our human-machine civilization. Thus « Turing Test level » machines will be able to combine human level intelligence with the powerful ways in which machines already excel. In addition, machines will continue to grow exponentially in their capacity and knowledge. It will be a formidable combination.

Why I Think I Will Win. In considering the question of when machine (i.e., nonbiological) intelligence will match the subtle and supple powers of human biological intelligence, we need to consider two interrelated but distinct questions: when will machines have the hardware capacity to match human information processing, and when will our technology have mastered the methods, i.e., the software of human intelligence. Without the latter, we would end up with extremely fast calculators, and would not achieve the endearing qualities that characterize human discernment (nor the deep knowledge and command of language necessary to pass a full Turing test!).

Both the hardware and software sides of this question are deeply influenced by the exponential nature of information-based technologies. The exponential growth that we see manifest in « Moore’s Law » is far more pervasive than commonly understood. Our first observation is that the shrinking of transistors on an integrated circuit, which is the principle of Moore’s Law, was not the first but the fifth paradigm to provide exponential growth to computing (after electromechanical calculators, relay-based computers, vacuum tube-based computing, and discrete transistors). Each time one approach begins to run out of steam, research efforts intensify to find the next source of renewed exponential growth (e.g., vacuum tubes were made smaller until it was no longer feasible to maintain a vacuum, which led to transistors). Thus the power and price-performance of technologies, particularly information-based technologies, grow as a cascade of S-curves: exponential growth leading to an asymptote, leading to paradigm shift (i.e., innovation), and another S-curve. Moreover, the underlying theory of the exponential growth of information-based technologies, which I call the law of accelerating returns, as well as a detailed examination of the underlying data, show that there is a second level of exponential growth, i.e., the rate of exponential growth is itself growing exponentially.

Second, this phenomenon of ongoing exponential growth through a cascade of S-curves is far broader than computation. We see the same double exponential growth in a wide range of technologies, including communication technologies (wired and wireless), biological technologies (e.g., DNA base-pair sequencing), miniaturization, and of particular importance to the software of intelligence, brain reverse engineering (e.g., brain scanning, neuronal and brain region modeling).

Within the next approximately fifteen years, the current computational paradigm of Moore’s Law will come to an end because by that time the key transistor features will only be a few atoms in width. However, there are already at least two dozen projects devoted to the next (i.e., the sixth) paradigm, which is to compute in three-dimensions. Integrated circuits are dense but flat. We live in a three-dimensional world, our brains are organized in three dimensions, and we will soon be computing in three dimensions. The feasibility of three-dimensional computing has already been demonstrated in several landmark projects, including the particularly powerful approach of nanotube-based electronics. However, for those who are (irrationally) skeptical of the potential for three-dimensional computing, it should be pointed out that achieving even a conservatively high estimate of the information processing capacity of the human brain (i.e., one hundred billion neurons times a thousand connections per neuron times 200 digitally controlled analog « transactions » per second, or about 20 million billion operations per second) will be achieved by conventional silicon circuits prior to 2020.

It is correct to point out that achieving the « software » of human intelligence is the more salient, and more difficult, challenge. On multiple levels, we are being guided in this effort by a grand project to reverse engineer (i.e., understand the principles of operation of) the human brain itself. Just as the human genome project accelerated (with the bulk of the genome being sequenced in the last year of the project), the effort to reverse engineer the human brain is also growing exponentially, and is further along than most people realize. We already have highly detailed mathematical models of several dozen of the several hundred types of neurons found in the brain. The resolution, bandwidth, and price-performance of human brain scanning is also growing exponentially. By combining the neuron modeling and interconnection data obtained from scanning, scientists have already reverse engineered two dozen of the several hundred regions of the brain. Implementations of these reverse engineered models using contemporary computation matches the performance of the biological regions that were recreated in significant detail. Already, we are in a early stage of being able to replace small regions of the brain that have been damaged from disease or disability using neural implants (e.g., ventral posterior nucleus, subthalmic nucleus, and ventral lateral thalamus neural implants to counteract Parkinson’s Disease and tremors from other neurological disorders, cochlear implants, emerging retinal implants, and others).

If we combine the exponential trends in computation, communications, and miniaturization, it is a conservative expectation that we will within 20 to 25 years be able to send tiny scanners the size of blood cells into the brain through the capillaries to observe interneuronal connection data and even neurotransmitter levels from up close. Even without such capillary-based scanning, the contemporary experience of the brain reverse engineering scientists, (e.g., Lloyd Watts, who has modeled over a dozen regions of the human auditory system), is that the connections in a particular region follow distinct patterns, and that it is not necessary to see every connection in order to understand the massively parallel, digital controlled analog algorithms that characterize information processing in each region. The work of Watts and others has demonstrated another important insight, that once the methods in a brain region are understood and implemented using contemporary technology, the computational requirements for the machine implementation requires on the order of a thousand times less computation than the theoretical potential of the biological neurons being simulated.

A careful analysis of the requisite trends shows that we will understand the principles of operation of the human brain and be in a position to recreate its powers in synthetic substrates well within thirty years. The brain is self-organizing, which means that it is created with relatively little innate knowledge. Most of its complexity comes from its own interaction with a complex world. Thus it will be necessary to provide an artificial intelligence with an education just as we do with a natural intelligence. But here the powers of machine intelligence can be brought to bear. Once we are able to master a process in a machine, it can perform its operations at a much faster speed than biological systems. As I mentioned, contemporary electronics is already more than ten million times faster than the human nervous system’s electrochemical information processing. Once an AI masters human basic language skills, it will be in a position to expand its language skills and general knowledge by rapidly reading all human literature and by absorbing the knowledge contained on millions of web sites. Also of great significance will be the ability of machines to share their knowledge instantly.

One challenge to our ability to master the apparent complexity of human intelligence in a machine is whether we are capable of building a system of this complexity without the brittleness that often characterizes very complex engineering systems. This a valid concern, but the answer lies in emulating the ways of nature. The initial design of the human brain is of a complexity that we can already manage. The human brain is characterized by a genome with only 23 million bytes of useful information (that’s what left of the 800 million byte genome when you eliminate all of the redundancies, e.g., the sequence called « ALU » which is repeated hundreds of thousands of times). 23 million bytes is smaller than Microsoft WORD. How is it, then, that the human brain with its 100 trillion connections can result from a genome that is so small? The interconnection data alone is a million times greater than the information in the genome. The answer is that the genome specifies a set of processes, each of which utilizes chaotic methods (i.e., initial randomness, then self-organization) to increase the amount of information represented. It is known, for example, that the wiring of the interconnections follows a plan that includes a great deal of randomness. As the individual person encounters her environment, the connections and the neurotransmitter level pattern self-organize to better represent the world, but the initial design is specified by a program that is not extreme in its complexity.
Thus we will not program human intelligence link by link as in some massive expert system. Nor is it the case that we will simply set up a single genetic (i.e., evolutionary) algorithm and have intelligence at human levels automatically evolve itself. Rather we will set up an intricate hierarchy of self-organizing systems, based largely on the reverse engineering of the human brain, and then provide for its education. However, this learning process can proceed hundreds if not thousands of times faster than the comparable process for humans.

Another challenge is that the human brain must incorporate some other kind of « stuff » that is inherently impossible to recreate in a machine. Penrose imagines that the intricate tubules in human neurons are capable of quantum based processes, although there is no evidence for this. I would point out that even if the tubules do exhibit quantum effects, there is nothing barring us from applying these same quantum effects in our machines. After all, we routinely use quantum methods in our machines today. The transistor, for example, is based on quantum tunneling. The human brain is made of the same small list of proteins that all biological systems are comprised of. We are rapidly recreating the powers of biological substances and systems, including neurological systems, so there is little basis to expect that the brain relies on some nonengineerable essence for its capabilities. In some theories, this special « stuff » is associated with the issue of consciousness, e.g., the idea of a human soul associated with each person. Although one may take this philosophical position, the effect is to separate consciousness from the performance of the human brain. Thus the absence of such a soul may in theory have a bearing on the issue of consciousness, but would not prevent a nonbiological entity from the performance abilities necessary to pass the Turing test.

Another challenge is that an AI must have a human or human-like body in order to display human-like responses. I agree that a body is important to provide a situated means to interact with the world. The requisite technologies to provide simulated or virtual bodies are also rapidly advancing. Indeed, we already have emerging replacements or augmentations for virtually every system in our body. Moreover, humans will be spending a great deal of time in full immersion virtual reality environments incorporating all of the senses by 2029, so a virtual body will do just as well. Fundamentally, emulating our bodies in real or virtual reality is a less complex task than emulating our brains.
Finally, we have the challenge of emotion, the idea that although machines may very well be able to master the more analytical cognitive abilities of humans, they inherently will never be able to master the decidedly illogical and much harder to characterize attributes of human emotion. A slightly broader way of characterizing this challenge is to pose it in terms of « qualia, » which refers essentially to the full range of subjective experiences. Keep in mind that the Turing test is assessing convincing reactions to emotions and to qualia. The apparent difficulty of responding appropriately to emotion and other qualia appears to be at least a significant part of Mitchell Kapor’s hesitation to accept the idea of a Turing-capable machine. It is my view that understanding and responding appropriately to human emotion is indeed the most complex thing that we do (with other types of qualia being if anything simpler to respond to). It is the cutting edge of human intelligence, and is precisely the heart of the Turing challenge. Although human emotional intelligence is complex, it nonetheless remains a capability of the human brain, with our endocrine system adding only a small measure of additional complexity (and operating at a relatively low bandwidth). All of my observations above pertain to the issue of emotion, because that is the heart of what we are reverse engineering. Thus, we can say that a side benefit of creating Turing-capable machines will be new levels of insight into ourselves.