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Is it just me, or is the OpenAI hype finally dying down?
The hype may be dying down, but I don't think the mission of the organization is to generate hype.
I presume we'll see hype when OpenAI starts posting :results: rather than generic plans.
They have plenty of results. All their work on GANs[1][2] has been great, and the OpenAI gym has proven to be pretty popular for reinforcement learning research already.

I'm not sure how "hype" is measured - perhaps people thought they were going to invent some kind of AI entity we would have to persuade not to shoot us all by now or something.

I know that all the papers coming out of OpenAI get hyped just as much as anything from DeepMind, Google Brain or FAIR, so there seems to be decent amount of hype still? But then usually that hype is justified, in which case maybe it is better characterised as "excitement"?

[1] http://arxiv.org/pdf/1606.03657.pdf

[2] http://arxiv.org/pdf/1606.03498.pdf

Scott Gray not heading to Intel. Interesting...
Nice! I read Dario Amodei's paper on Concrete Problems in AI Safety a few months ago, and I'm excited that he and OpenAI are going to be working on them. There's a lot of important research waiting to be done!
Is this useful in some way? In an admittedly brief look at their site, the only thing I found was the "gym," and it seemed to require that you already be using other software.

How would we use this to solve real-world problems?

I wish OpenAI would work harder to hire more women on their research staff specifically.
I'm sorry. I'm not PC, I'm not Millennial, I'm Gen-X.

I was brought up to think about the concept: the best person for the job. The BEST. If I were to hire someone now, I couldn't care less where they were born, what language they spoke, what colour they were, what gender they were, what hobbies they like or gasp political leanings.

Does anything of what I mentioned contribute to how they could perform? NO!

What does? How effectively they are able to actually perform in the job.

I will NOT be pressured by society or anyone else to fill quotas.

I think someone said it best in Twitter. Diversity does not mean lowering the bar.

If you want more of a certain group of people to be in a certain profession. Then make it attractive to them to be interested in it ALSO make them work hard to have the skills so that an employer would hire them on the SPOT!

Let me tell you something. I have worked with some amazing female developers. In my book? I'd hire them over a guy any day, especially when it's coupled with analytical skills.

Seriously. I wish this forced diversity thing would just die. No one company should work hard, it's the other way around. Want to get into a company, work hard and get into it yourself!

Is there anything the OP said that implied lowering the bar?

Indeed, I doubt OpenAI has a strong head-count limit, so hiring a highly qualified man shouldn't mean that hiring an equally qualified women isn't possible at the same time.

There are plenty of women who are more than qualified for OpenAI[1], and yet I'm not aware of any that they have hired in research positions.

(They don't seem to have a team list, so it is hard to know for sure)

[1] Start here: https://sites.google.com/site/wimllist/

Given OpenAI's specific mission, diversity is an important part of the job... if their goal was simply to create the best AI possible, your argument would carry more weight (though I'd still disagree with the conclusion), but when your goal is to ensure that AI advances in the way that most benefits humanity as a whole, it's a real flaw to have a team consisting largely of one group of people.
You seem very angry about my comment. I'm not sure why. Roughly 14% of NIPS attendees are women and OpenAI seems to be doing worse than that very low threshold for its research staff. If they want the best candidates, they need to work hard to erode systemic biases and patriarchal power structures that turn away good candidates. The longer this goes on, the harder it will be to fix since the senior people in the organization will mostly be early hires and candidates will start to find the problem more and more visible.