no hard takeoff, steps to strong AI
i was just reading william hertling’s article on predicting the future of technological progress:
How To Predict The Future
essentially, he makes similar arguments as kurzweil…mapping the exponential progress of computing power and suggesting that the new hardware enables the invention of the next technology. he feels one can largely ignore software and other factors and provides a few examples to support that perspective (rise of you tube, napster and others).
all this is the lead up to kurzweil’s same prediction of strong AI emerging when computing power allows emulation of an entire brain…with varying estimates of the computing power necessary, given our evolving understanding of exactly how the brain works. a range of 10^14 FLOPS to 10^20. that is 6 orders of magnitude difference (big range), but with our exponential progress, that maps out to a difference of only 24 years…less if you can strap of few machines together. this got me thinking as i was washing the lunch dishes. i often make the mental leap from the state of the world today to the post-singularity world of tomorrow….when computers exceed human intelligence. this (not surprisingly) is always a bit dislocating…and because it seems so drastically different from current reality, i tend to discount it. this has caused me to ping back and forth from having confidence in a radically different future and thinking that it is too different, and thus unlikely. what i tend to forget, and what kevin kelly knows (below), is that things progress in steps…and we have plenty of time to make those steps. even techno-optimists like kurzweil allow many years to move from the initial emulation of a brain to the singularity. it won’t happen overnight (we all hope).
this recognition reminds me of kelly’s book, “what technology wants.” here, kelly anticipates elements of hertling’s work and outlines the inevitable nature of technological invention. he elegantly describes how each invention builds on those that preceded it, giving technology an almost palpable “want” of the next thing. this thought stream brings me back to the pathway to human level AI (strong AI).
many people criticize these arguments for the singularity saying that we don’t or cannot understand the brain, therefore we cannot build strong AI. a not unreasonable observation, but i don’t think that will prevent us from getting there. we have 17 years before the hardware will really be ready (commodity hardware that is, current supercomputers are already there). those 17 years will be spent building on neuroscience’s understanding of the brain’s wiring and structure. we may not have to understand the brain holistically, we may just have to know how it is wired and what the wires (neurons, axons, synapses) do.
digression: the IBM sequoia machine just debuted at lawrence livermore lab at 16 petaflops (1.6 x 10^16), clearly in the range of brain emulation, though it’ll be used to model nuclear explosions. this level of processing power should be available to the consumer in 12-15 years*. it is hertling’s perspective (and mine) that strong AI progress will really begin to move when the processing power to roughly emulate a brain is in the hands of many people.
so…instead of imagining a post-singularity world, let’s just imagine a world where computers understand us, or understand our speech well enough to execute directions. this seems eminently reasonable. indeed, apple’s siri is the beginning of that trend. does it not seem reasonable that in 4-5 years computers should be able to understand a fairly broad array of directions? how long after that occurs will it be before neuroscience labs can automate more of their processes?** mapping the trillions of connections in a brain is currently impossible…but seems entirely possible with a bit better instrumentation and a roomful of dumb, but effective, robotic technicians (this bit deserves greater treatment, but one can certainly imagine this capability in several different ways…in other words, very likely).***
it is not a large leap then, to envision commodity hardware capable of high petaflop performance, in the hands of many researchers (and enthusiasts) creating and refining forms of AI that may not necessarily be human-like, but will be astonishingly capable:
- highly effective voice interfaces
- vision (visible spectrum, infrared, other?)
- reasoning and problem solving
- mated with robotic bodies that have evolved from today to 2029 (what might that look like?)
at that point (again, not hard to imagine) we are clearly on the way to that dislocated future that makes me a bit uncomfortable.
* based on previous experience of moore’s law. yes, some say moore’s law will end soon, but even most of them admit we can fairly clearly see moore’s law going until 2022 (the current semi-conductor roadmap goes that far and is based on known science). we only have to squeeze a bit more out of silicon or find useable one of many new technologies (spintronics, optical, quantum, memristor, others) to extend our capabilities for a few more years.
this also ignores the emerging tech of neuromorphics which, i believe, is the true path to strong AI. more on that, next posting.
**robotic patch-clamping (technique for mapping neurons) is already possible and in use: http://medgadget.com/2012/05/robot-for-whole-cell-patch-clamp-electrophysiology-of-neurons-in-vivo.html
***please see henry markram’s blue brain project.