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I'm quite sure that those 10k hours are not with a single version but they are already being updated all the time.

A human driver on the other hand similarly also updates his driving behavior throughout his life.

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Software and humans are completely different. Humans are, in a way, very fault tolerant. In software, one erroneous line of code can literally crash the car. In a human, one stray neuron will probably don't do much harm.

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Note: This is about unsupervised learning and mostly about RBMs/DBNs. Most of the Deep Learning success is all about supervised learning. In the past, RBMs have been used for unsupervised pretraining of the model, however, nowadays, everyone uses supervised pretraining.

And the famous DeepMind works (Atari games etc) is mostly about Reinforcement learning, which is again different.

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I will address the supervised vs unsupervised issue in my next post. Here, I believe the analogy would be that when a field is applied to a spin glass, it does not exhibit a glass transition to a non-self-averaging (highly non-convex) ground state.

As to supervised vs reinforcement learning, its not that different. See how Vowpal Wabbit incoporates both the 2 ideas in how the SGD update is formulated.

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Well, if I understood correctly, the RL DeepMind implementation is basically making a RL algorithm work with a supervised model.

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This has been done since the 90s. The Deepmind paper is about a few more tricks.

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I can really recommend to go to this event. It covers a wide area of topics, not just technical but also art, politics, etc. And it's all very much fun. There are many interesting people there who all want to show their projects and just want to chat. It's also very international.

Some more info:

https://events.ccc.de/

https://en.wikipedia.org/wiki/Chaos_Communication_Camp

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Qt is much more cross-platform than GTK. GTK doesn't work that well on MacOSX and I'm not sure about mobile platforms.

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Does Electron run on mobile platforms?

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This is from 2002, right?

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It is indeed - I'd suggest the title is modified to add year as it provides context for how long LSTMs have been established before their recent popularity boom.

https://scholar.google.com/scholar?hl=en&q=A+First+Look+at+M...

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LSTMs came from the same group (Schmidhuber), from Hochreiter. They were introduced in 1997.

It's somewhat interesting to see that only recently they become really widely used in certain Deep Learning communities, e.g. speech recognition.

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Unfortunately, I can no longer modify the title.

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Support Vector Machines was one of the hot topics.

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And Bengio and some other well-known people from the Deep Learning community. Interesting.

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Examples: https://github.com/overviewer/Minecraft-Overviewer/wiki/Map-...

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The method is not unique. See some of the work of Schmidhuber's group. They are doing a lot of reinforcement learning for recurrent nets (LSTMs) and also via evolutionary algorithms. Eg see Evolino.

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I stand corrected, Schmidhuber in AMA http://www.reddit.com/r/MachineLearning/comments/2xcyrl/i_am... has an evolved Atari agent before Mnih

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Otoro states he is using CNE Conventional Neural Evolution, but combining it with ideas about recurrence from the Atari paper.

http://blog.otoro.net/2015/01/27/neuroevolution-algorithms/

He outlines the evolution of his thinking and slime volleyball in this post which cites John Gomez's thesis as the inception of CNE.

Certainly parallel ideas to Schmidhuber but the implementation details are somewhat different in the U of Texas Neuro-Evolution models.

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None of the ideas are new.

http://people.idsia.ch/~juergen/evolino.html

Yet Schmidhuber's nets are much more complex and certainly different.

I still think Otoro's very simple RNN feedback nets are unique - especially when coupled with training by self-play.

Does Schmidhuber have any game playing agents ?

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I was very impressed with this handwriting demo (and the accompaning paper):

http://www.cs.toronto.edu/~graves/handwriting.html

Using a recurrent architecture.

To be honest I haven't played with NNs, but it puzzles me as to why the non-recurrent approach is so prevalent for complex tasks. I mean, it's the basic combinatorial circuit vs sequential circuits, which we all know are much more suited for complex or large outputs. Where's everything we learned from synchronous logic synthesis?

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[reply to darkmighty comment below, thread depth limitation]

Indeed this is exactly it, evolution is a global method, learning is local.

I am reading John Gomez's thesis where he compares and combines learning and evolution

http://www.cs.utexas.edu/users/nn/downloads/papers/gomez.phd...

Otoro's post on the evolution of his slime volleyball thinking is well worth a read.

http://blog.otoro.net/2015/01/27/neuroevolution-algorithms/

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I will check those out, thanks. This is indeed a fascinating topic. I guess every scientist wants to understand learning.

The connection comes up in david mckay's Information Theory book too, a reading I definitively recommend, although I haven't been through it properly myself.

http://www.inference.phy.cam.ac.uk/itprnn/book.pdf

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McKays Information Theory is a brilliant read so far, many thanks.

Having learning couched in Information Theory terms brings it all right back to Claude Shannon's early work on Reinforcement Learning Chess programs and Alan Turing's ideas about evolving efficient machine code by bitmask genetic recombination.

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Grave's Handwriting Net was trained using Backpropagation - whereby the error between the net's estimate and a training target is sent backward through the net - so the net's estimates gradually become closer to the targets.

Backpropagation takes longer the deeper the net - Recurrent Neural Nets are deep in time so Back Propagation can become intractable or unstable.

Otoro's Slimeball demo evolves a Recurrent Net rather than training it - this appears to be a very efficient method, less likely to get stuck in local minima.

The slimes evolve through self-play which is a trial and error method and reinforcement methods seem to do better on control tasks than passive learning.

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Ah I see. But as far as training goes the difference between the two methods ("evolution" and backprop) is a matter of locality, no? The backprop modifies weights loosely based on local gradiet towards fitness, and evolution goes in sparse random directions. In this view backprop is indeed vulnerable to local maxima if your optimization method isn't very good, but isn't it just a matter of choosing good optimization methods? In other words, combining local backprop optimization with global evolutionary methods should be the role of robust optimization algos, no?

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That handwriting demo is awesome! Thanks for the pointers - I want learn more about how that works.

I wish I stayed at U of T for a few more years...

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What's the state about https://luvit.io/ ? Or are there other similar projects?

I think Nodejs gave JS a big popularity boost.

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> Or are there other similar projects?

There are at least half a dozen somewhat related projects: nginx_lua, tir2, luajit.io, luanode, tarantool, lusty, plus a bunch of libuv bindings.

And yeah, a single lua 'killer app' could do a lot for the language's popularity (elua comes close, but is a bit niche).

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