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actually see things, we essentially hallucinate them in detail from
what we see from these low resolution cues. Past the early phases
of the visual cortex, detail doesn’t reach the brain.
“We are getting
exponentially
more knowledge. We can get de-
tailed scans of neurons working
in vivo,
and are beginning to un-
derstand the chaotic algorithms underlying human intelligence.
In some cases, we are getting comparable performance of brain
regions in simulation. These tools will continue to grow in detail
and sophistication.
“We can have confidence of reverse-engineering the brain in
twenty years or so. The reason that brain reverse-engineering has
not contributed much to artificial intelligence is that up until re-
cently we didn’t have the right tools. If I gave you a computer and
a few magnetic sensors and asked you to reverse-engineer it, you
might figure out that there’s a magnetic device spinning when a
file is saved, but you’d never get at the instruction set. Once you
reverse-engineer the computer fully, however, you can express its
principles of operation in just a few dozen pages.
“Now there are new tools that let us see the interneuronal con-
nections and their signaling,
in vivo,
and in real-time. We’re just
now getting these tools and there’s very rapid application of the
tools to obtain the data.
“Twenty years from now we will have realistic simulations and
models of all the regions of the brain and [we will] understand
how they work. We won’t blindly or mindlessly copy those meth-
ods, we will understand them and use them to improve our AI
toolkit. So we’ll learn how the brain works and then apply the
sophisticated tools that we will obtain, as we discover how the
brain works.
“Once we understand a subtle science principle, we can isolate,
amplify, and expand it. Air goes faster over a curved surface: from
that insight we isolated, amplified, and expanded the idea and
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