Floating-point operations per second of different supercomputers.11
Transistors per chip for different Intel processors.12
Bits per dollar for dynamic random access memory chips.13
Bits per dollar for random access memory chips.14
The average price per transistor in dollars.15
The total number of bits of random access memory shipped each year.16
Bits per dollar (in constant 2000 dollars) for magnetic data storage.17
Even the predictions that were “wrong” were not all wrong. For example, I judged my prediction that we would have self-driving cars to be wrong, even though Google has demonstrated self-driving cars, and even though in October 2010 four driverless electric vans successfully concluded a 13,000-kilometer test drive from Italy to China.18 Experts in the field currently predict that these technologies will be routinely available to consumers by the end of this decade.
Exponentially expanding computational and communication technologies all contribute to the project to understand and re-create the methods of the human brain. This effort is not a single organized project but rather the result of a great many diverse projects, including detailed modeling of constituents of the brain ranging from individual neurons to the entire neocortex, the mapping of the “connectome” (the neural connections in the brain), simulations of brain regions, and many others. All of these have been scaling up exponentially. Much of the evidence presented in this book has only become available recently—for example, the 2012 Wedeen study discussed in chapter 4 that showed the very orderly and “simple” (to quote the researchers) gridlike pattern of the connections in the neocortex. The researchers in that study acknowledge that their insight (and images) only became feasible as the result of new high-resolution imaging technology.
Brain scanning technologies are improving in resolution, spatial and temporal, at an exponential rate. Different types of brain scanning methods being pursued range from completely noninvasive methods that can be used with humans to more invasive or destructive methods on animals.
MRI (magnetic resonance imaging), a noninvasive imaging technique with relatively high temporal resolution, has steadily improved at an exponential pace, to the point that spatial resolutions are now close to 100 microns (millionths of a meter).
A Venn diagram of brain imaging methods.19
Tools for imaging the brain.20
MRI spatial resolution in microns.21
Spatial resolution of destructive imaging techniques.22
Spatial resolution of nondestructive imaging techniques in animals.23
Destructive imaging, which is performed to collect the connectome (map of all interneuronal connections) in animal brains, has also improved at an exponential pace. Current maximum resolution is around four nanometers, which is sufficient to see individual connections.
Artificial intelligence technologies such as natural-language-understanding systems are not necessarily designed to emulate theorized principles of brain function, but rather for maximum effectiveness. Given this, it is notable that the techniques that have won out are consistent with the principles I have outlined in this book: self-organizing, hierarchical recognizers of invariant self-associative patterns with redundancy and up-and-down predictions. These systems are also scaling up exponentially, as Watson has demonstrated.
A primary purpose of understanding the brain is to expand our toolkit of techniques to create intelligent systems. Although many AI researchers may not fully appreciate this, they have already been deeply influenced by our knowledge of the principles of the operation of the brain. Understanding the brain also helps us to reverse brain dysfunctions of various kinds. There is, of course, another key goal of the project to reverse-engineer the brain: understanding who we are.
CHAPTER 11
OBJECTIONS
If a machine can prove indistinguishable from a human, we should award it the respect we would to a human—we should accept that it has a mind.
T he most significant source of objection to my thesis on the law of accelerating returns and its application to the amplification of human intelligence stems from the linear nature of human intuition. As I described earlier, each of the several hundred million pattern recognizers in the neocortex processes information sequentially. One of the implications of this organization is that we have linear expectations about the future, so critics apply their linear intuition to information phenomena that are fundamentally exponential.
I call objections along these lines “criticism from incredulity,” in that exponential projections seem incredible given our linear predilection, and they take a variety of forms. Microsoft cofounder Paul Allen (born in 1953) and his colleague Mark Greaves recently articulated several of them in an essay titled “The Singularity Isn’t Near” published in Technology Review magazine.1 While my response here is to Allen’s particular critiques, they represent a typical range of objections to the arguments I’ve made, especially with regard to the brain. Although Allen references The Singularity Is Near in the title of his essay, his only citation in the piece is to an essay I wrote in 2001 (“The Law of Accelerating Returns”). Moreover, his article does not acknowledge or respond to arguments I actually make in the book. Unfortunately, I find this often to be the case with critics of my work.
When The Age of Spiritual Machines was published in 1999, augmented later by the 2001 essay, it generated several lines of criticism, such as: Moore’s law will come to an end; hardware capability may be expanding exponentially but software is stuck in the mud; the brain is too complicated; there are capabilities in the brain that inherently cannot be replicated in software; and several others. One of the reasons I wrote The Singularity Is Near was to respond to those critiques.
I cannot say that Allen and similar critics would necessarily have been convinced by the arguments I made in that book, but at least he and others could have responded to what I actually wrote. Allen argues that “the Law of Accelerating Returns (LOAR)…is not a physical law.” I would point out that most scientific laws are not physical laws, but result from the emergent properties of a large number of events at a lower level. A classic example is the laws of thermodynamics (LOT). If you look at the mathematics underlying the LOT, it models each particle as following a random walk, so by definition we cannot predict where any particular particle will be at any future time. Yet the overall properties of the gas are quite predictable to a high degree of precision, according to the laws of thermodynamics. So it is with the law of accelerating returns: Each technology project and contributor is unpredictable, yet the overall trajectory, as quantified by basic measures of price/performance and capacity, nonetheless follows a remarkably predictable path.