But after that, I actually had my own lab in the computational neuroscience department, and I wasn't doing any neuroscience.

Neuroscience is hopeless.

The computational power is amazing.

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There is evidence from neuroscience.

Graphical display of computational chemistry output

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It emerges from our understanding of basic neuroscience.

This works in physics. This works in geology. But if this is normal science, what does neuroscience look like? This is what neuroscience looks like.

So this is a session on frontiers in neuroscience.

There's a new field in brain science, social neuroscience.

It also brings up new issues, though, like computational irreducibility.

And that's what we're doing at the Redwood Neuroscience Institute.

You have data, and data in neuroscience is sensory input.

So that's essentially the computational equivalent of two light switches.

Now we know from neuroscience that compassion has some very extraordinary qualities.

Modeling on the right hand side, there's a very complex computational model.

But it's going to work. It's more effective than just computational aids.

This is the result of computational geometry, gesture recognition, and machine learning.

You can ask about what causes it biochemically neuroscience, serotonin, all that stuff.

Also, genetics and neuroscience are increasingly showing that the brain is intricately structured.

In a sense, we can use the computational universe to get mass customized creativity.

And in neuroscience, we have to deal with many difficult questions about the brain.

But where I think computational speed is most applicable is in that of imaging.

So, the Holy Grail for neuroscience is really to understand the design of the neocoritical column and it's not just for neuroscience it's perhaps to understand perception, to understand reality, and perhaps to even also understand physical reality.

But I wanted to look not at the astronomical universe, but at the computational universe.

Here's the problem Any serious candidate for our universe is inevitably full of computational irreducibility.

Andrew Ng So in large scale machine learning, we'd like to come up with computational ...

Most of you know me from my Palm and Handspring days, but I also run a nonprofit scientific research institute called the Redwood Neuroscience Institute in Menlo Park, and we study theoretical neuroscience, and we study how the neocortex works.

We use it in the engineering and neuroscience sense meaning a random noise corrupting a signal.

This fascination led me to submerge myself in art, study neuroscience and later to become a psychotherapist.

And the new thinking about compassion from social neuroscience is that our default wiring is to help.

Well, it turns out we can do very much the same kind of thing in the computational universe.

So, the job of my field of cognitive neuroscience is to stand with these ideas, one in each hand.

I would posit that one frontier in neuroscience is to figure out how the brain of that thing works.

We've all, I think, become, sort of computational chauvinists, and think that computation is everything, but the mechanics still matter.

And when one does that, one really changes the economics of delivering computational things, whether it's on the web or elsewhere.

And, for example, Wolfram Alpha and Mathematica are actually now full of algorithms that we discovered by searching the computational universe.

But in the computational universe, we've now seen how rules that are incredibly simple can produce incredibly rich and complex behavior.

The modern tools of neuroscience are demonstrating to us that what's going on up there is nothing short of rocket science.

So what that means is, one could use computational methods to read all of the books in a click of a button.

There were 28,000 people who went to the neuroscience conference this year, and every one of them is doing research in brains.