My Halloween lecture has been uploaded to youtube. The basic outline is:

* what is intelligence?
* Solomonoff induction
* Hutter’s AIXI
* Monte Carlo AIXI (here’s the missing video of it playing pac-man)
* universal intelligence measure
* what neuroscience can teach us about AGI design
* early 2020′s: the Halloween scenario

You can get the slides here. I talked for 2 hours, so it’s broken up into many parts on youtube: Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8 Part 9 Part 10 Part 11 Part 12

Thanks to David Wood at ExtroBritannian for organising this, and all the people who attended — especially those who travelled from other cities and countries, the intelligent questions during my talk, and all the positive feedback I’ve received since. Thanks also to Anders Sandberg for the picture of me speaking that I stole from his flicker stream.

So what does the brain do? For over a decade it has been known that temporal difference learning, a type of model-free RL algorithm, appears to explain the activity of dopamine neurons and their dorsolateral striatal projections. It has also been observed that parts of the prefrontal cortex appear to implement some kind of model-based RL algorithm. Mammalian brains, then, appear to get the best of worlds by having model-free and model-based RL algorithms and then choosing which to use on the fly.  Pretty clever huh?

A key question then is how this choice is made. I recently read Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control from Nature Neuroscience, by Nathaniel Daw, Yael Niv and Peter Dayan. They suggest that the brain may be using some kind of Bayesian principle based on the uncertainty estimates generated by each system. They implement such a system and show how it can explain a range of experimental data from animal studies.

This research is now four years old and there is plenty of significant research in the area that follows it — an “embarrassment of riches” as Dayan recently described it.  Nevertheless, I think it’s a good example of the kind of crossover between machine learning and neuroscience that is starting to take place.  Indeed, the more neuroscientists learn about the brain, the more it starts to look like the rough outline of an AGI design.

Now, try to imagine 20 times this scale.  A street with 100 people on it now has 2,000 people.  Regular two story family homes are now 40 story apartment blocks.  Big skyscrapers now have 2,000 floors and are 10 km high, taller than Mt. Everest.  Or if you prefer to go out, rather than up, take New York and drop another 19 of them around the United States.  Then do the same for Los Angeles… and so on for every city and every town, and continue this way across the whole world.  The planet would be burried under a seething mass of humanity.

That’s 120 billion people, approximately the number of neurons in your brain.

Now give each of these 120 billion people a cell phone, and load each one up with something like 5,000 phone numbers, mostly of people who live in the individual’s area.  Get everybody on this planet to start sending messages to each other.  Some only slowly send messages, others are busy sending 200 messages a second to all 5,000 people that they know.  Now let this system start to adapt in order to control which messages go where and when…

That’s about the scale of your brain.

When people say they can’t believe that the human brain is “just a machine”, I suspect they are suffering from a lack of imagination — have they seriously tried to wrap their mind around how unbelievably profoundly gigantic this machine actually is?

The next generation of Nvidia GPUs will support enhancements such as double precision floating point in order to make them more suitable for general purpose highly parallel computation. There will also be cards with no graphics interface and greater maximum RAM designed specifically for low cost supercomputing applications.

But what does this mean? Nothing really. Like in the brains of people with a cut corpus callosum, who’s to say that there isn’t a conscious experience of what is taking place in these parts of cortex that aren’t being reported as conscious experience? It’s just that this conscious experience is not integrated with the parts of my conscious experience that are able to report. There could be a number of other “conscious awarenesses” residing in my own head that “I” don’t experience as “I” (the part of my brain that is able to report) am not sufficiently integrated with them.

That’s a pretty weird thought, and yet it seems reasonable. A touch Freudian even.