We recently had a decade of the brain.
Now, why not a decade of the liver or some other organ?
Only the brain transcends biochemistry to produce intelligence.
We want to understand how the brain computes and to use that knowledge
to augment and extend intelligence in machines and networks.
Now, we just heard the brain is a million miles of wire
but our science can’t just start there.
To see why I think about this machine, what does it do, how does it work?
It’s computer made out of wooden Tinkertoys, it plays Tic-tac-toe,
but you never guess that or how it works just by looking at the wires.
And, likewise, we can’t just look at the connect on the wiring of the brain.
We have to understand the problem the machine is solving.
Our minds are learning machines.
And what I’m gonna talk about today are two theories of learning.
So, the first, the standard theory,
views learning as statistics on a grand scale finding clusters, correlations,
finding principal components in masses of data to predict what will come next.
These statistics can be computed in artificial neural networks or real neurons
by adjusting the connections, the synapses between units.
The same kind of math, the math of clustering
or finding principle components, underlies learning technology
that we take for granted like face detection now in digital cameras
or online systems for recommending new books, new friends, new movies,
or Google’s PageRank algorithm which ranks web search hits based on
the principle component of the web very much like the best models we have
how memory search in human brains.
So we have a great unifying theory.
It’s elegant and it’s useful, but it’s missing something big.
Human learning isn’t just masses of data but sometimes quite the opposite,
connecting a few sparse fragments of evidence, a child learns a new concept
from just a few examples like you can do here.
Given these tufas, just a few examples, you can say which other ones are tufas,
but no artificial neural network can do that. It needs hundreds of examples.
Or consider Google spelling correction,
amazing stuff based on finding words as clusters of frequently occurring letters.
But take something like this.
You can read this easily while Google fails and the difference is because you
appreciate words as having meaning and sentences expressing coherent thoughts.
You use your knowledge of language to put together the piece of this puzzle
anywhere in language.
Or maybe take the deepest challenges for us in scientific discovery.
Ask, how did Darwin come to natural selection
by looking at finches in Galapagos?
How did Newton derive his law of gravitation
from the motions of the planets?
How did Mendel glimpse genetics in pea pods?
Not by finding clusters or correlations in massive online data sets
but by positing colossal processes that best explain the data they observed.
So we’re developing a new theory of learning based on colossal models,
more powerful than clusters or correlations.
The idea is to express any colossal model as a computer program
that can generate different data sets like rolling differently weighted dice.
Then we use this theorem from probability to figure out
which cause of model is most likely to have produced the data we observed.
So in this case study here even though you’ve never seen these objects before,
our learning algorithm is able to figure out the colossal process
most likely to have generated them, something like an evolutionary tree.
And then once you have the best tree,
the few examples that you’ve seen of tufas cluster in one region
and, bingo, you know what’s a tufa and what isn’t.
Many challenges in society have the same flavor.
We need to connect the dots, connect sparse fragments of evidence
to figure out, for example, who’s a real terror threat
or which drug is really a promising cancer cure.
Current approaches to connecting the dots
missed too many targets that they shouldn’t.
So we and others are working on new computational approaches
to connecting the dots by trying to learn from how the mind
solves these kinds of problems so well.
Or in another project we’re working with scientists from Shell
to build systems that learn where the oil is
from very sparse seismic data like this.
If you just try to use simple statistical approaches, clusters correlations,
it’s hopeless.
A human expert solves this problem because they have in their head
a colossal model of how geological process like river formation
unfolds over time and we can now put that kind of colossal approach
into our learning systems.
For most of us the hardest colossal challenge
is figuring out what other people are thinking.
So here it’s irresistible just from a sparse pattern of motion,
you don’t just see blue and red, you see blue chasing red.
You don’t just see two triangles in a circle.
You see a big guy bullying, shoving a little guy.
You see that circle hiding.
You see now the big guy coming and threatening, pursuing the other guy,
or taking intelligence analyst who’s tracking people moving around a city
and gets a feeling that something bad is happening or who’s working with whom.
In all these cases our minds junk from a few sparse fragments of motion
to the most likely causes, the goals, the intentions, the social ties that
generate the motion we see.
And we can now put those same kinds of colossal models in our learning systems
to augment and extend people’s natural social??? capacities.
Increasingly, our social interactions are taking place online
and we need our systems to work there.
There’s a lot of intriguing applications.
So instead of just looking at simple clusters based on current recommendation
systems, we can use our colossal models to make subtler distinctions like,
is he really looking to buy or just browsing
or is she stalking on Facebook or does she just have a bad crash?
In some our minds are for modeling the world but that goes beyond
just finding clusters or feeding curves to an ocean of data.
It means building deeper theories, appreciating causality not just correlation,
explaining not just predicting to connect the dots rapidly and accurately
across fragments of evidence.
We now know how to take this colossal approach and put it
in computational models and to use that to build smarter, more human-like
cognitive technologies that can make a real difference in our society.
Thanks.