Ray Kurzweil - How to Create a Mind - The Secret of Human Thought Revealed

The way that the massively redundant structures in the brain differentiate is through learning and experience. The current state of the art in AI does in fact enable systems to also learn from their own experience. The Google self-driving cars learn from their own driving experience as well as from data from Google cars driven by human drivers; Watson learned most of its knowledge by reading on its own. It is interesting to note that the methods deployed today in AI have evolved to be mathematically very similar to the mechanisms in the neocortex.

Another objection to the feasibility of “strong AI” (artificial intelligence at human levels and beyond) that is often raised is that the human brain makes extensive use of analog computing, whereas digital methods inherently cannot replicate the gradations of value that analog representations can embody. It is true that one bit is either on or off, but multiple-bit words easily represent multiple gradations and can do so to any desired degree of accuracy. This is, of course, done all the time in digital computers. As it is, the accuracy of analog information in the brain (synaptic strength, for example) is only about one level within 256 levels that can be represented by eight bits.

In chapter 9 I cited Roger Penrose and Stuart Hameroff’s objection, which concerned microtubules and quantum computing. Recall that they claim that the microtubule structures in neurons are doing quantum computing, and since it is not possible to achieve that in computers, the human brain is fundamentally different and presumably better. As I argued earlier, there is no evidence that neuronal microtubules are carrying out quantum computation. Humans in fact do a very poor job of solving the kinds of problems that a quantum computer would excel at (such as factoring large numbers). And if any of this proved to be true, there would be nothing barring quantum computing from also being used in our computers.

John Searle is famous for introducing a thought experiment he calls “the Chinese room,” an argument I discuss in detail in The Singularity Is Near . 9 In short, it involves a man who takes in written questions in Chinese and then answers them. In order to do this, he uses an elaborate rulebook. Searle claims that the man has no true understanding of Chinese and is not “conscious” of the language (as he does not understand the questions or the answers) despite his apparent ability to answer questions in Chinese. Searle compares this to a computer and concludes that a computer that could answer questions in Chinese (essentially passing a Chinese Turing test) would, like the man in the Chinese room, have no real understanding of the language and no consciousness of what it was doing.

There are a few philosophical sleights of hand in Searle’s argument. For one thing, the man in this thought experiment is comparable only to the central processing unit (CPU) of a computer. One could say that a CPU has no true understanding of what it is doing, but the CPU is only part of the structure. In Searle’s Chinese room, it is the man with his rulebook that constitutes the whole system. That system does have an understanding of Chinese; otherwise it would not be capable of convincingly answering questions in Chinese, which would violate Searle’s assumption for this thought experiment.

The attractiveness of Searle’s argument stems from the fact that it is difficult today to infer true understanding and consciousness in a computer program. The problem with his argument, however, is that you can apply his own line of reasoning to the human brain itself. Each neocortical pattern recognizer—indeed, each neuron and each neuronal component—is following an algorithm. (After all, these are molecular mechanisms that follow natural law.) If we conclude that following an algorithm is inconsistent with true understanding and consciousness, then we would have to also conclude that the human brain does not exhibit these qualities either. You can take John Searle’s Chinese room argument and simply substitute “manipulating interneuronal connections and synaptic strengths” for his words “manipulating symbols” and you will have a convincing argument to the effect that human brains cannot truly understand anything.

Another line of argument comes from the nature of nature, which has become a new sacred ground for many observers. For example, New Zealand biologist Michael Denton (born in 1943) sees a profound difference between the design principles of machines and those of biology. Denton writes that natural entities are “self-organizing,…self-referential,…self-replicating,…reciprocal,…self-formative, and…holistic.” 10 He claims that such biological forms can only be created through biological processes and that these forms are thereby “immutable,…impenetrable, and…fundamental” realities of existence, and are therefore basically a different philosophical category from machines.

The reality, as we have seen, is that machines can be designed using these same principles. Learning the specific design paradigms of nature’s most intelligent entity—the human brain—is precisely the purpose of the brain reverse-engineering project. It is also not true that biological systems are completely “holistic,” as Denton puts it, nor, conversely, do machines need to be completely modular. We have clearly identified hierarchies of units of functionality in natural systems, especially the brain, and AI systems are using comparable methods.

It appears to me that many critics will not be satisfied until computers routinely pass the Turing test, but even that threshold will not be clear-cut. Undoubtedly, there will be controversy as to whether claimed Turing tests that have been administered are valid. Indeed, I will probably be among those critics disparaging early claims along these lines. By the time the arguments about the validity of a computer passing the Turing test do settle down, computers will have long since surpassed unenhanced human intelligence.

My emphasis here is on the word “unenhanced,” because enhancement is precisely the reason that we are creating these “mind children,” as Hans Moravec calls them. 11 Combining human-level pattern recognition with the inherent speed and accuracy of computers will result in very powerful abilities. But this is not an alien invasion of intelligent machines from Mars—we are creating these tools to make ourselves smarter. I believe that most observers will agree with me that this is what is unique about the human species: We build these tools to extend our own reach.

Intelligence may be defined as the ability to solve problems with limited resources, in which a key such resource is time. Thus the ability to more quickly solve a problem like finding food or avoiding a predator reflects greater power of intellect. Intelligence evolved because it was useful for survival—a fact that may seem obvious, but one with which not everyone agrees. As practiced by our species, it has enabled us not only to dominate the planet but to steadily improve the quality of our lives. This latter point, too, is not apparent to everyone, given that there is a widespread perception today that life is only getting worse. For example, a Gallup poll released on May 4, 2011, revealed that only “44 percent of Americans believed that today’s youth will have a better life than their parents.” 1

If we look at the broad trends, not only has human life expectancy quadrupled over the last millennium (and more than doubled in the last two centuries), 2 but per capita GDP (in constant current dollars) has gone from hundreds of dollars in 1800 to thousands of dollars today, with even more pronounced trends in the developed world. 3 Only a handful of democracies existed a century ago, whereas they are the norm today. For a historical perspective on how far we have advanced, I suggest people read Thomas Hobbes’s Leviathan (1651), in which he describes the “life of man” as “solitary, poor, nasty, brutish, and short.” For a modern perspective, the recent book Abundance (2012), by X-Prize Foundation founder (and cofounder with me of Singularity University) Peter Diamandis and science writer Steven Kotler, documents the extraordinary ways in which life today has steadily improved in every dimension. Steven Pinker’s recent The Better Angels of Our Nature: Why Violence Has Declined (2011) painstakingly documents the steady rise of peaceful relations between people and peoples. American lawyer, entrepreneur, and author Martine Rothblatt (born in 1954) documents the steady improvement in civil rights, noting, for example, how in a couple of decades same-sex marriage went from being legally recognized nowhere in the world to being legally accepted in a rapidly growing number of jurisdictions. 4