latently

"On the other hand, Brooks doesn't show any indication he knows what Musk is talking about"

Not quite true: "Tell me, what behavior do you want to change, Elon?"

Consciousness has a specific meaning - https://en.wikipedia.org/wiki/Qualia

I like your comment. The real question is whether they are conscious.

The analogy between deep neural networks and the brain has proven to be very fruitful. Other analogies may as well. See our upcoming paper for more info.

https://grey.colorado.edu/mediawiki/sites/mingus/images/3/3a...

The brain is a dynamic system and (some) neural networks are also dynamic systems, and a three layer neural network can learn to approximate any function. Thus, a neural network can approximate brain function arbitrarily well given time and space. Whether that simulation is conscious is another story.

The Computational Cognitive Neuroscience Lab has been studying this topic for decades and has an online textbook here:

http://grey.colorado.edu/CompCogNeuro

The "emergent" deep learning simulator is focused on using these kinds of models to model the brain:

http://grey.colorado.edu/emergent

That's not how it works.

Latently | Deep Learning | Boulder, CO | REMOTE

Have some time on your hands and interested in implementing scientific papers for a stealth-mode deep learning startup? Contact brian@latent.ly More info: https://goo.gl/HhvxLO

An interesting technicality from the post and paper is that the measure of causal information (mutual information between the initial and final state) bears some resemblence to the Lyapunov exponent as it is used to measure whether a system is on the edge of chaos. When the exponent is 1 (IIRC) the system does not diverge exponentially when the initial conditions are changed slightly and the system is said to be on the edge of chaos and to have good generalization ability. Anywhere else and the system is either damped or chaotic and you don't expect "interesting" stuff to happen there, such as higher-order "causal" effects. (seriously though, why are people so obsessed with causality when it's clear that there is almost never one "causal" description. let it go!)

That's not deep enough for my tastes.

Is fitness emergent, or fundamental? If you didn't discuss this, you should add another year to your PhD!

The word explore is actually great in a data analysis context. The notions of exploratory vs confirmatory analysis are widely used, and exploratory means exactly what your students think it means. Just make sure they don't explore all of the data at once, otherwise they will have to go collect more so that they can confirm what they found when they were exploring.

Latently | Deep Learning Engineers | Boulder, Co | Remote

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In my opinion you are overconfident in the foundations of mathematics. Like deep learning models, math works. Why and how does it work? It's open to interpretation in both cases. In both cases, we don't have a complete understanding. It is that lack of complete understanding that makes it a black box.

"However, these models are largely big black-boxes. There are a lot of things we don’t understand about them."

This describes geometry as well as it describes deep learning.

You have NO idea what you're talking about man. Don't believe the hype. Deep learning has ALWAYS been popular and a very active field, since the mid 80s.

This is an excellent syllabus by Professor Dave Touretzky, a pioneer in deep learning. He started the Connectionists mailing list and was heavily involved in the early days of NIPS.

Sign up here: https://mailman.srv.cs.cmu.edu/mailman/listinfo/connectionis...

For anyone who thinks there was ever a pause in deep learning progress, the Connectionists archives beg otherwise!

Connectionists: https://mailman.srv.cs.cmu.edu/mailman/listinfo/connectionis...

Latently | Boulder, CO | REMOTE

Have some time on your hands and interested in implementing scientific papers for a stealth-mode deep learning startup? Contact brian@latent.ly

Probably being hosted by its hard drive.

Latently | Deep Learning Engineer | Boulder, Co | REMOTE

Latently is a stealth-mode pre-revenue startup that is looking for engineers who want to earn sweat equity alongside the founders. We have substantial hardware support through IBM's Global Entrepreneur program and you'll be training state-of-the-art recurrent neural networks on unstructured text on a sweet GPU cluster. If you have the luxury of time this is a great opportunity. Send resumes to brian@latent.ly

A better way to do this analysis would have been to create an extremely sparse matrix with one column for every possible endorsement category with the value being the number of endorsements (normalized). Then try to predict various aspects of coding performance.

Definitely wouldn't endorse the author for machine learning :)

For those interested in PDP, one of Jay McClellands students is Professor Randy O'Reilly at CU. He develops the emergent neural network simulator and is using biologically plausible deep learning to learn how the mind works. He's been doing it all along. There was never a gap!

The simulator: http://grey.colorado.edu/emergent The textbook: https://grey.colorado.edu/CompCogNeuro

Just because it wasn't wildly popular doesn't mean there was a gap of any sorts. I agree with Schmidhuber. http://grey.colorado.edu/emergent