Some good comments on related work in https://www.reddit.com/r/MachineLearning/comments/2kth6d/googles_secretive_deepmind_startup_unveils_a/
(After reading the paper, I still don't really get how you wire some RAM into a neural network. Maybe it makes more sense to others.)
It's not really RAM, but rather a tape. (like a doubly linked list) The LSTM controller can't specify any location in logarithmic space / time. They add multiple tape readers at one point though.
What is going on with those renewable funding and performance curves?
The performance curves database site only has data submitted from '09 to '10. None of the data sets are up to date. There's also not much context to the data, so it can be misinterpreted easily.
Do you have a way of tweaking the AIXI or AIXI(tl) equation so that that could be accomplished?
It'd just model a world where if the machine it sees in the mirror turns off, it can no longer influence what happens.
When the function it uses to model the world becomes detailed enough, it can predict only being able to do certain things if some objects in the world survive, like the program running on that computer over there.
If parents had strong embryo selection available to them, how would the world be different, other than via increased intelligence?
Gattaca, except everyone is actually superhuman and nobody cares about whether you'll have a heart attack at thirty except your doctor.
I have started reading Qualia the Purple, a manga strongly recommended by a few LWers, such as Eliezer and Gwern. In his recommendation, Eliezer wrote: "The manga Qualia the Purple has updated with Ch. 14-15. This is what it looks like to “actually try” at something."
Does anyone else know good examples of "actually trying" in other media? Over the LW IRC channel, Gwern linked to this page (Warning, TV Tropes), and specifically recommended Monster. Any other suggestions?
It seems like Qualia the Purple is a manga where after a certain point, the author introduced magic and started giving philosophic explanations for how the main character can do magic, turn into other people, go back in time, and generally do whatever the fuck she wants except save one person. What does "actually try" mean?
What did you find most interesting in this week's reading?
Iterated embryo selection was pretty interesting. I wonder if there is anything viable about inserting new / activating the growth of neurons / synapses into the human brain, particularly into specifically targeted areas, like the section(s) where people do math.
Neuromorphic AI/low-fidelity whole-brain emulation, is likely to precede hi-fidelity whole-brain emulation.
Therefore, if we want to understand possible pathways to AI, I think it pays to focus attention on technologies which are inspired by neuroscience but either emulate the human mind in an inexact way or use somewhat neuron-like computing elements in new ways.
The level of detail to choose for simulated neurons seems very controversial.
The Blue Brain Project models neurons with a lot of complicated internal state, while the DARPA Synapse Project is going for very simple neurons.
http://www.eecs.berkeley.edu/~phj/cs267/hw0/
http://www.artificialbrains.com/darpa-synapse-program
The projects have different goals- DARPA wants to create devices for applications, while The European Brain Initiative is motivated more by trying to understand biological phenomena.
At 256 Million synapses per chip, the DARPA project could string together less than one million processors to reach the number of synapses in the brain. That goal is within reach, but how much can their simple neurons really do?
If simpler neuron-like components are sufficient, then far less computing capacity is required to build some kinds of neuromorphic AI.
Is there literature I could read on the differences between the performance of the neurons DARPA uses and the neurons Blue Brain uses?
Are there examples in the different octants suggested by this? In particular, is there an example of something automatic, but slow and effortful?
I wrote a 140 character lambda calculus interpreter and a bigger and more complete (static name resolution + renaming + repl) version of it.
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CFAR should post more stats about success rates of any kind and maintain these stats throughout all cohorts.
Also stats about how many people asked for their money back.