[p2p-research] Tick, tock, tick, tock… BING

[Ryan]

Human level computational intelligence.

Sent to you by Ryan via Google Reader: Tick, tock, tick, tock… BING via
vetta project by Shane Legg on 12/7/09

Am I the only one who, upon hearing the year 2010, imagines some date
far off in the future? I think I felt the same way in the weeks before
2000, so I’m sure it will pass. Anyway, another year has gone, indeed
another decade, and it’s time for my annual review of predictions. You
can find my last annual post here.

It’s been an interesting year in which I’ve been exposed to far more
neuroscience than ever before. What I’ve learnt, plus other news I’ve
absorbed during the year, has helped to clarify my thinking on the
future of AI. First, let’s begin with computer power. I recently gave a
talk at the Gatsby Unit on the singularity in which I used the
following graph showing the estimated LINPACK scores of the fastest
computers over the last 50 years.




The first two points beyond 2010 are for some supercomputers that are
already partly constructed. In the past performance estimates for these
kinds of machines near to their delivery have been reasonably accurate
so I’ve put these on the graph. Rather more speculative is the 2019
data point for the first ExaFLOPS machine. IBM is in discussions about
how to put this machine together based on the technology used in the 20
PetaFLOPS machine due in a year and a bit. Based on articles on
supercomputer sites like top 500, it appears to be a fairly mainstream
opinion that this target should be achievable. Nevertheless, 9 years is
a while away so I’ve marked it in grey.

First observation: just like the people who told me in 1990 that
exponential growth in supercomputer power couldn’t continue for another
decade, the people who told me this in 2000 were again completely
wrong. Ha ha, told you so! So let me make another prediction: for the
next decade this pattern will once again roughly hold, taking us to
about 10^18 FLOPS by 2020.

Second observation: I’ve always been a bit sceptical of Kurzweil’s
claim that computer power growth was double exponential, but I’m now
thinking that there is some evidence for this having spent some time
putting together data for this graph and attempting to compensate for
changes in measurement etc. in the data. That said, I think it’s
unlikely to remain double exponential much longer.

Third observation: it looks like we’re heading towards 10^20 FLOPS
before 2030, even if things slow down a bit from 2020 onwards. That’s
just plain nuts. Let me try to explain just how nuts: 10^20 is about
the number of neurons in all human brains combined. It is also about
the estimated number of grains of sand on all the beaches in the world.
That’s a truly insane number of calculations in 1 second.

Desktop performance is also continuing this trend. I recently saw that
a PC with just two high end graphics cards is around 10^13 FLOPS of
SGEMM performance. I also read a paper recently showing that less
powerful versions of these cards lead to around 100x performance
increases over CPU computation when learning large deep belief networks.

By the way, in case you think the brain is doing weird quantum voodoo:
I had a chat to a quantum physicist here at UCL about the recent claims
that there is some evidence for this. He’d gone through the papers
making these claims with some interest as they touch on topics close to
his area of research. His conclusion was that it’s a lot of bull as
they make assumptions (not backed up with new evidence) in their
analysis that essentially everybody in the field believes to be false,
among other problems.

Conclusion: computer power is unlikely to be the issue anymore in terms
of AGI being possible. The main question is whether we can find the
right algorithms. Of course, with more computer power we have a more
powerful tool with which to hunt for the right algorithms and it also
allows any algorithms we find to be less efficient. Thus growth in
computer power will continue to be an important factor.

Having dealt with computation, now we get to the algorithm side of
things. One of the big things influencing me this year has been
learning about how much we understand about how the brain works, in
particular, how much we know that should be of interest to AGI
designers. I won’t get into it all here, but suffice to say that just a
brief outline of all this information would be a 20 page journal paper
(there is currently a suggestion that I write such a paper next year
with some Gatsby Unit neuroscientists, but for the time being I’ve got
too many other things to attend to). At a high level what we are seeing
in the brain is a fairly sensible looking AGI design. You’ve got
hierarchical temporal abstraction formed for perception and action
combined with more precise timing motor control, with an underlying
system for reinforcement learning. The reinforcement learning system is
essentially a type of temporal difference learning though unfortunately
at the moment there is evidence in favour of actor-critic, Q-learning
and also Sarsa type mechanisms — this picture should clear up in the
next year or so. The system contains a long list of features that you
might expect to see in a sophisticated reinforcement learner such as
pseudo rewards for informative queues, inverse reward computations,
uncertainty and environmental change modelling, dual model based and
model free modes of operation, things to monitor context, it even seems
to have mechanisms that reward the development of conceptual knowledge.
When I ask leading experts in the field whether we will understand
reinforcement learning in the human brain within ten years, the answer
I get back is “yes, in fact we already have a pretty good idea how it
works and our knowledge is developing rapidly.”

The really tough nut to crack will be how the cortical system works.
There is a lot of effort going into this, but based on what I’ve seen,
it’s hard to say just how much real progress is being made. From the
experimental neuroscience side of things we will soon have much more
detailed wiring information, though this information by itself is not
all that enlightening. What would be more useful is to be able to
observe the cortex in action and at the moment our ability to do this
is limited. Moreover, even if we could, we would still most likely have
a major challenge ahead of us to try to come up with a useful
conceptual understanding of what is going on. Thus I suspect that for
the next 5 years, and probably longer, neuroscientists working on
understanding cortex aren’t going to be of much use to AGI efforts. My
guess is that sometime in the next 10 years developments in deep belief
networks, temporal graphical models, liquid computation models, slow
feature analysis etc. will produce sufficiently powerful hierarchical
temporal generative models to essentially fill the role of cortex
within an AGI. I hope to spend most of next year looking at this so in
my next yearly update I should have a clearer picture of how things are
progressing in this area.

Right, so my prediction for the last 10 years has been for roughly
human level AGI in the year 2025 (though I also predict that sceptics
will deny that it’s happened when it does!) This year I’ve tried to
come up with something a bit more precise. In doing so what I’ve found
is that while my mode is about 2025, my expected value is actually a
bit higher at 2028. This is not because I’ve become more pessimistic
during the year, rather it’s because this time I’ve tried to quantify
my beliefs more systematically and found that the probability I assign
between 2030 and 2040 drags the expectation up. Perhaps more useful is
my 90% credibility region, which from my current belief distribution
comes out at 2018 to 2036.

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