cinjon

Right, that's one. And it's the only one that comes up when I search. Are there others you know of?

What are the companies you know of here?

At what age is this appropriate?

This short essay is roughly a synospsis of the theme behind Asimov's short story The Profession.

1% of sales for Walmart is ~3-4% of profit. That's huge.

Hi, I'm pretty interested in what you're doing here and have built similar tools for other domains. Ping me to talk offline? --> cinjon@nyu.edu.

Hi all, we have a new DFL guide, and we are especially proud of this one. James Allingham and his group made an especially superb curriculum for understanding WGAN. We highly recommend earnestly studying with this at hand. Check it out at http://www.depthfirstlearning.com/2019/WassersteinGAN.

To be clear, MIT very much does not pretend that. It's only recently hired people on that track and has only 4-5 professors in total in that regard.

Link to paper that cedes control of Borg?

This is in some respects what element.ai is aiming to supplement. And having spent a lot of time in the ML community, there's a strong urge from a proportion of the students to start their own companies after finishing their studies as well.

Who are the Econ people you're talking about on twitter?

GB (and Google more broadly) are very interested in this intersection. So yes, there are opportunities to do awesome work here, albeit actually working in a wet-lab is more difficult.

Here's a great blog from one of the residents - http://blog.otoro.net/

It's like any other (research) endeavor with lots of people involved. Some of us share research interests and so we coalesce around efforts towards accomplishing those goals. Out of the resident pool, I am currently working with Ryan and have had some discussions about collaborations with Denny and Justin.

Take a look at the published papers though and you can see some pairs that have flourished by working in the same vicinity.

This is a wonderful part of the program though, and it doesn't stop with the Residents. Brain is such a great place for ideas to meld and for people to come together to collaborate; it's designed to encourage that.

Given what you wrote to Hardmaru, I think you should just apply. The description you gave is a competitive one. Highlight it and then do well on the interview :).

They are. The pace of research is not limited to six months.

The wonderful Chris Olah teaches this, and in some ways he is by making them available through distill.pub. Check it out!

You should just apply. The people you're trying to impress are researchers and engineers, and they have a long history evaluating folks who have proven their merit outside of academia.

Off the cuff, and probably a lot updated since, you should think of it like the normal Google interview but geared towards figuring out how well you'd do in the research environment. Prepare for the former but understand the latter.

I think that's a great background for the program and matches a bunch of the current residents. You should definitely apply. I suspect it will only make your later PhD experience better.

Demonstrated interest. From peers in the program, I have found that to be the most consistent marker.

(MIT --> Startups --> Research --> GB)

Aye. True story.

Hi there! I'm Cinjon, a current resident in the Google Brain Residency. Feel free to ask any Qs and I'll answer them.

The current group doesn't end until at least June and so, except for those who just applied to grad schools, we're all continuing to do more awesome research here rather than starting to solidify what comes next.

That being said, this is an unparalleled experience in the field and a phenomenal road to travel regardless of what we decide to do afterward.

It is a very research-oriented experience, but you can definitely frame your work as more engineering-intensive. Quite a few of the residents have done some more really impressive engineering feats in the research domain.

Can you give more detail? I'm an alum ('10) who was majorly involved in student government but now have little insight into what's going on.

It seems that the local minima aren't actually a problem in these high dimensional spaces because most of them are either very close to the global minima or are saddle points, which can be escaped. There was a lot of work on this out of Bengio's lab. One such paper was Dauphin's "Identifying and attacking the saddle point..."

A translation service for large foreign documents (mostly PDFs). There's a first pass reproducing the PDF in html and a second that machine translates it into English. Users can then gist a document and select any section to get professionally translated. It's live at OneDossier.com.

It's a pretty great resource that's doing holiday pricing for only $39. (I'm not affiliated with the company.)

Aye, I just had a back and forth with a tech rep there. They advised me at the end to contact sales to 100% understand, but the gist I got was that the FineReader Engine is $100 and it can do anything the SDK can do, but you have limited functionality in how it can operate with other programs (on Mac, all you get is Automator) and it can't be scaled to many cores. If you don't need that, then it seems to be a pretty damn good deal. Will talk to the sales rep first though.

What kind of problem were you dealing with? Were you bumping up against any constraints?

Where do you see this? I'm actually thinking of buying the software right now and I explicitly asked what other charges are there besides the initial fee (~$100) and they said there wasn't any. Am I not asking the right question?