This is part of IEEE Spectrum's SPECIAL REPORT: THE SINGULARITY
I think it's likely that with technology we can in the fairly near future create or become creatures of more than human intelligence. Such a technological singularity would revolutionize our world, ushering in a posthuman epoch. If it were to happen a million years from now, no big deal. So what do I mean by ”fairly near” future? In my 1993 essay, ”The Coming Technological Singularity,” I said I'd be surprised if the singularity had not happened by 2030. I'll stand by that claim, assuming we avoid the showstopping catastrophes--things like nuclear war, superplagues, climate crash--that we properly spend our anxiety upon.
In that event, I expect the singularity will come as some combination of the following:
The AI Scenario: We create superhuman artificial intelligence (AI) in computers.
The IA Scenario: We enhance human intelligence through human-to-computer interfaces--that is, we achieve intelligence amplification (IA).
The Biomedical Scenario: We directly increase our intelligence by improving the neurological operation of our brains.
The Internet Scenario: Humanity, its networks, computers, and databases become sufficiently effective to be considered a superhuman being.
The Digital Gaia Scenario: The network of embedded microprocessors becomes sufficiently effective to be considered a superhuman being.
The essays in this issue of IEEE Spectrum use similar definitions for the technological singularity but variously rate the notion from likely to totally bogus. I'm going to respond to arguments made in these essays and also mine them for signs of the oncoming singularity that we might track in the future.
Philosopher Alfred Nordmann criticizes the extrapolations used to argue for the singularity. Using trends for outright forecasting is asking for embarrassment. And yet there are a couple of trends that at least raise the possibility of the technological singularity. The first is a very long-term trend, namely Life's tendency, across aeons, toward greater complexity. Some people see this as unstoppable progress toward betterment. Alas, one of the great insights of 20th-century natural science is that Nature can be the harshest of masters. What we call progress can fail. Still, in the absence of a truly terminal event (say, a nearby gamma-ray burst or another collision such as made the moon), the trend has muddled along in the direction we call forward. From the beginning, Life has had the ability to adapt for survival via natural selection of heritable traits. That computational scheme brought Life a long way, resulting in creatures that could reason about survival problems. With the advent of humankind, Life had a means of solving many problems much faster than natural selection.
In the last few thousand years, humans have begun the next step, creating tools to support cognitive function. For example, writing is an off-loading of memory function. We're building tools--computers, networks, database systems--that can speed up the processes of problem solving and adaptation. It's not surprising that some technology enthusiasts have started talking about possible consequences. Depending on our inventiveness--and our artifacts' inventiveness--there is the possibility of a transformation comparable to the rise of human intelligence in the biological world. Even if the singularity does not happen, we are going to have to put up with singularity enthusiasms for a long time.
Get used to it.
In recent decades, the enthusiasts have been encouraged by an enabling trend: the exponential improvement in computer hardware as described by Moore's Law, according to which the number of transistors per integrated circuit doubles about every two years. At its heart, Moore's Law is about inventions that exploit one extremely durable trick: optical lithography to precisely and rapidly emplace enormous numbers of small components. If the economic demand for improved hardware continues, it looks like Moore's Law can continue for some time--though eventually we'll need novel component technology (perhaps carbon nanotubes) and some new method of high-speed emplacement (perhaps self-assembly). But what about that economic demand? Here is the remarkable thing about Moore's Law: it enables improvement in communications, embedded logic, information storage, planning, and design--that is, in areas that are directly or indirectly important to almost all enterprise. As long as the software people can successfully exploit Moore's Law, the demand for this progress should continue.
Roboticist Hans Moravec may have been the first to draw a numerical connection between computer hardware trends and artificial intelligence. Writing in 1988, Moravec took his estimate of the raw computational power of the brain together with the rate of improvement in computer power and projected that by 2010 computer hardware would be available to support roughly human levels of performance. There are a number of reasonable objections to this line of argument. One objection is that Moravec may have radically underestimated the computational power of neurons. But even if his estimate is a few orders of magnitude too low, that will only delay the transition by a decade or two--assuming that Moore's Law holds.
Another roboticist, Rodney Brooks, suggests in this issue that computation may not even be the right metaphor for what the brain does. If we are profoundly off the mark about the nature of thought, then this objection could be a showstopper. But research that might lead to the singularity covers a much broader range than formal computation. There is great variety even in the pursuit of pure AI. In the next decade, those who credit Moravec's timeline begin to expect results. Interestingly powerful computers will become cheap enough for a thousand research groups to bloom. Some of these researchers will pursue the classic computational tradition that Brooks is doubting--and they may still carry the day. Others will be working on their own abstractions of natural mind functions--for instance, the theory that Christof Koch and Giulio Tononi discuss in their article. Some (very likely Moravec and Brooks himself) will be experimenting with robots that cope with many of the same issues that, for animals, eventually resulted in minds that plan and feel. Finally, there will be pure neurological researchers, modeling increasingly larger parts of biological brains in silico . Much of this research will benefit from improvements in our tools for imaging brain function and manipulating small regions of the brain.