Graphs don’t prove the Singularity
A short reply, drafted for the SL4 mailing list, to a common objection: that the case for an approaching artificial-intelligence transition rests on extrapolated trend lines — Moore’s Law, Kurzweil’s graphs — and that extrapolation isn’t evidence. Alyssa argues that the case doesn’t actually depend on graph-fitting; it depends on a handful of independent technical trajectories converging.
Certainly, there is no certain evidence that AI will be developed in the near future. However, an increase in processing power, combined with improved brain-scanning methods, seems likely to produce artificial intelligence in the near future. Molecular nanotechnology, in particular, will enable massive amounts of processing power, as well as a thorough mapping of the brain. Even if it didn’t become available, more conventional techniques are also making fast progress: by some estimates, the top supercomputers of today already have enough processing power to match the human brain, and machines of comparable potential are expected to become cheaply and commonly available within a few decades. Projects to build brain simulations are currently underway, with one team having run a second’s worth of a simulation as complex as half a mouse brain, and IBM’s Blue Brain project seeking to simulate the whole human brain.
Progress made toward reverse-engineering the brain will also help AI research by making researchers themselves more intelligent: for instance, IQ tests seem to measure working memory capacity (Oberauer et al., 2005). As the neural basis for different working memory capacities becomes clear, it might become possible for us to increase our own intelligence directly. Even if this isn’t possible, algorithms extracted from the brain can be applied to traditional computer systems, making them more effective at helping us conduct research.
Even if we exclude the possibility of artificial intelligence by brain reverse-engineering, increasing amounts of processing power are likely to make it easier to create AIs by evolutionary programming. The human mind was never designed by anyone — it evolved through genetic drift and selection pressures. It might not be strictly necessary for us to understand how a mind works, as long as we can build a system that has enough computing power to simulate evolution and produce an artificial mind optimized to the conditions we want it to perform in. Combined with advances in cognitive science and traditional artificial intelligence techniques, the case is strong.
While nothing is ever certain, these factors are heavy enough to make the issue worth our attention. The case for believing that AI may be near does not depend on Ray Kurzweil’s predictions; it stands on the underlying technical trajectories, and would stand even if every one of his graphs turned out to be wrong.