wookietrader

Do they still ask for your business idea when you register for their beta?

Okay, I should have worded that differently. There is also a paper of Salakhutdinov learning a kernel for Gaussian processes. That'd account for that as well.

My point is (I did not really write that above) that deep learning does not stand unchallenged in this domain. Its dominance is so far "only" apparent in vision and audio classification tasks.

Yes you are right.

Still, deep learning has done nothing more than classification right now.

What about predictive distributions, regression of complicated outputs (e.g. periodic data) and, most of all, heterogenous inputs? Right: nothing impressive has been done in that area, despite of huge amounts of practical problems.

Let's see if deep learning generalizes to those things. If it does (and I personally believe so) let's be happy. Before that, we still have to envy what Gaussian processes, Gradient boosting machines and random forests can do what DL so far cannot.

If an article says that Andrew Ng is "the man at the center of deep learning" it's just not right. Geoffrey Hinton's and Yoshua Bengio's impact were at least as high as his, if not much higher.

This has been said about neural nets two times already. Sadly, they did never deliver.

There are still applications where e.g. random forests beat the crap out of all kinds of deep learning algorithms in (a) training time (b) predictive quality (c) prediction time.

We should stop hyping this. I am a researcher working in deep learning myself, but the current deep learning hype is actually what makes me worry that I will have trouble getting a job because industry will be disappointed a third time.

Wrong.

Neural networks are affinity invariant. You can rotate, skew, translate the input data however you want, the optimum stays the same. Same for SVMs.

Here's why: In 95% of the cases you create a git branch, you check it out. In 95% of the cases you create a directory, you do not cd into it.

There is a machine learning that is not related to big data, you know. Many interesting problems in machine learning, and most of the hard ones, have a computational demand for which a single i7 and 16 GB of RAM are more than enough.

It's at least one way which has been advocated by leading researcher of the field. If you think differently, you should give references and explain what your AGI definition is.

With respect to Myo vs Leap.

Tracking is much precise than EMG based posture estimation. Unless these guys made a leap of at least one order of magnitude compared to current state of the art, expect the LEAP to be buch better in terms of gesture recognition.

Furthermore, this thing only allows the recognition of 20 gestures. That is about log 20 bit every few seconds. On the other hand, the LEAP will give you at least 10 finger positions plus rotations (thats more than 2^10 bits) every few milliseconds.

I think if people will get used to LEAP based HCI, they will not look at Myo, no matter if you can go to the kitchen with it.

To me it only looks like a limitation.

So yeah, you can write a custom class. But you will need to add a custom "save state" and "load state" in your class' __iter__ method. So all the benefits go away.

If they could only be pickled...

ATLAS is a very bad name to pick; it's already taken when it comes to numerical data processing.

Not sure why this being downvoted. Provably friendly strong AI is actually sth that researcher care about, yet it is difficult to do because "friendly" has to be defined.