Excerpt from /r/machinelearning AMA with JuergenSchmidhuber

While a problem solver is interacting with the world, it should store the entire raw history of actions and sensory observations including reward signals. The data is ‘holy’ as it is the only basis of all that can be known about the world. If you can store the data, do not throw it away! Brains may have enough storage capacity to store 100 years of lifetime at reasonable resolution [1].

As we interact with the world to achieve goals, we are constructing internal models of the world, predicting and thus partially compressing the data history we are observing. If the predictor/compressor is a biological or artificial recurrent neural network (RNN), it will automatically create feature hierarchies, lower level neurons corresponding to simple feature detectors similar to those found in human brains, higher layer neurons typically corresponding to more abstract features, but fine-grained where necessary. Like any good compressor, the RNN will learn to identify shared regularities among different already existing internal data structures, and generate prototype encodings (across neuron populations) or symbols for frequently occurring observation sub-sequences, to shrink the storage space needed for the whole (we see this in our artificial RNNs all the time). Self-symbols may be viewed as a by-product of this, since there is one thing that is involved in all actions and sensory inputs of the agent, namely, the agent itself. To efficiently encode the entire data history through predictive coding, it will profit from creating some sort of internal prototype symbol or code (e. g. a neural activity pattern) representing itself [1,2]. Whenever this representation becomes activated above a certain threshold, say, by activating the corresponding neurons through new incoming sensory inputs or an internal ‘search light’ or otherwise, the agent could be called self-aware. No need to see this as a mysterious process — it is just a natural by-product of partially compressing the observation history by efficiently encoding frequent observations.

[1] Schmidhuber, J. (2009a) Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes. SICE Journal of the Society of Instrument and Control Engineers, 48 (1), pp. 21–32.

[2] J. Schmidhuber. Philosophers & Futurists, Catch Up! Response to The Singularity. Journal of Consciousness Studies, Volume 19, Numbers 1-2, pp. 173-182(10), 2012.

Quote from To Dream The Possible Dream Raj Reddy

The basic unit of a digital library is an electronic book. An electronic book provides the same information as a real book. One can read and use the information just as we can in a real book. However, it is difficult to lie in bed and read an electronic book. With expected technological advances, it is conceivable a subnotebook computer will weigh less than 12 ounces and have a 6¨ x 8¨ high resolution color screen, making it look and feel like a book that you might read in bed. However, the analogy stops there. An electronic book cannot be used as part of your rare book collection, nor can it be used to light a fire on a cold night to keep you warm. You can probably throw it at someone, but it would be expensive. On the other hand, using an electronic book, you can process, index, and search for information; open the right page;  highlight information; change font size if you don’t have your reading glasses; and so on. The point is, an electronic book is not the same as a real book. It is both more and less.

. . .

Similarly, AI is both more and less than human intelligence. There will be certain human capabilities that might be impossible for an AI system to reach. The boundary of what can or cannot be done will continue to change with time. More important, however, it is clear that some AI systems will have super human capabilities that would extend the reach and functionality of individuals and communities. Those who possess these tools will make the rest of us look like primitive tribes. By the way, this has been true of every artifact created by the human species, such as the airplane. It just so happens that AI is about creating artifacts that enhance the mental capabilities of the human being.

Connectome, The Project of Mapping out the Brain’s Wiring Using Citizen Science

“You’re the grandiose one, you think you’re going to understand how the brain works, all I want to do is map it.”- Sebastian Seung
Tonight, RSA uploaded a video of a talk on YouTube where Seung talks about Eyewire.  EyeWire is a massively multiplayer “game” developed by researchers at the Computation Science Lab at MIT, where the objective is to correct for an artificial intelligence’s hesitations/errors in coloring neurons. The artificial intelligence is programmed to stop when it is has less certainty about how to color the area and relies on the player’s discernment to complete the coloring. These corrected colorings will eventually be useful in mapping the “connectome”, or the wiring system formed by neurons in the brain.
The machine he shows a photo of in the talk used to cut slices of brain one thousand times thinner than air is amazing.
Do you find the four color map shading game fun?  Do you like Science Fairs?  Then you will like playing Eyewire.  “The EyeWire Games are 7 days of team competition between Facebook, Reddit, Twitter, Google+ and Team X (Veterans). The team that maps the most 3D neuron volume in one week receives the ultimate reward: neuron naming rights.Your team is the social network where you discovered EyeWire.”  I am on Team Reddit because I found out about it via /r/simulate.
After signing up, you do a tutorial. Completing the tutorial gets you your first points.  You get points according to how much neuron you map.  I’ve only done a couple neurons so I have about 169 points and that ranks me in the 300s.  The leader has earned 140000 points. So, not only is it cool to participate, but you still have a chance to maybe be in the top 100 if you start now.
This is exciting research.  Crowdsourcing may suffer from being this age’s hype, but this project is an exemplery and inspiring example of the kinds of problems it can solve.