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superfx

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www.biorxiv.org 3y ago

OpenFold: Retraining AlphaFold gives insights into how it learns and generalizes

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www.geekwire.com 4y ago

Biotech startups join AWS and others in open-source project to design proteins

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twitter.com 5y ago

DeepMind plans to open source AlphaFold

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www.wsj.com 5y ago

Daniel Humm’s New Eleven Madison Park Menu Will Be Meat-Free

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www.nature.com 6y ago

A watershed moment for protein structure prediction

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www.nature.com 6y ago

International evaluation of an AI system for breast cancer screening

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www.biorxiv.org 7y ago

Rational protein engineering with sequence-only deep representation learning

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rxivist.org 7y ago

Most downloaded bioRxiv preprints of 2018

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www.documentcloud.org 7y ago

Texas Judge Overturns Affordable Care Act

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arxiv.org 7y ago

Gradient Descent Finds Global Minima of Deep Neural Networks

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www.nytimes.com 7y ago

M.I.T. Plans College for Artificial Intelligence, Backed by $1B

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www.youtube.com 7y ago

Nvidia: Video-to-video synthesis

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www.quora.com 8y ago

The story behind Elon Musk's involvement with the Thai cave rescue effort

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www.wired.com 8y ago

Inside Palmer Luckey’s Bid to Build a Border Wall

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moalquraishi.wordpress.com 8y ago

Protein Linguistics

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stanfordmlgroup.github.io 8y ago

Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

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www.youtube.com 8y ago

Progressive Growing of GANs for Improved Quality, Stability, and Variation

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shanghaiist.com 8y ago

iPhone 8 Plus allegedly 'explodes' while charging in Taiwan

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www.wbur.org 8y ago

Mass. Becomes First State to Have Half Its Labor Force Hold Bachelor's Degrees

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www.nytimes.com 9y ago

Yearning for New Physics at CERN, in a Post-Higgs Way

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www.sfchronicle.com 9y ago

Bay Area slips in startup rankings

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www.technologyreview.com 9y ago

Andrew Ng Is Leaving Baidu in Search of a Big New AI Mission

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www.evolvingai.org 9y ago

Plug and Play Generative Networks

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www.theguardian.com 9y ago

Trump to scrap NASA climate research in crackdown on ‘politicized science’

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blog.wolfram.com 9y ago

Wolfram Player for iOS

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medium.com 9y ago

Apple Strategy 2017. Very important change to iPhone coming

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www.youtube.com 9y ago

The Evolution of Bacteria on a “Mega-Plate” Petri Dish

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fivethirtyeight.com 9y ago

Airbnb Probably Isn’t Driving Rents Up Much, at Least Not Yet

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fivethirtyeight.com 9y ago

Religious Diversity May Be Making America Less Religious

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www.wired.com 10y ago

Step into the Huge Factory Forging America’s Fancy New Trains

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This strikes me as the quintessential problem with autocracies. Sometimes one gets extremely efficient governments in the short term, when the autocrat is competent and not entirely corrupt, but in the long run whatever short term gains were had are squandered by corruption and greed. Democracy is inefficient in the short term but efficient in the long run.

"E-scooters aren't a reliable way to get anywhere yet, and who knows if they'll ever be, not to mention that they are not for everyone. My grandmother is not going to ride one -- nor my wife, for that matter, nor should the kids. But the Subway is a common denominator."

I used to think so, but some European cities really do offer counterexamples. I'm thinking of places like Munich, Vienna, and Copenhagen. It's not uncommon to see people there who, by American stereotypes, wouldn't be expected to ride scooters: moms with kids, men in suits, etc. Perhaps the urban cultural gap is so vast that what you're saying is indeed true of the US, but I wouldn't take it as a given.

Learning Dexterity 8 years ago

It looks that way because they're moving rapidly from one face configuration to another. But there's no way that's happening by random. I would guess that even just holding the cube constant in a dynamic grip is quite difficult.

I would say the biggest thing is obviously the architecture, coupling LSTMs with the geometric units that spit out the actual 3D structure that can then be directly optimized via the dRMSD loss function. That's the biggest point of distinction from everything else out there (no contact map prediction, etc.) So it really is about end-to-end differentiability IMO, which hasn't been done before.

As for why it took so long, it is and it is not fine-tuning. Getting RGNs to train _at all_ was a rather difficult process, and required a lot of finicking around. But since I got them working, I haven't actually spent all that much time fine-tuning them, and so I expect there to be a lot of low-hanging fruit in terms of optimizing performance (starting from the baseline I found.)

I do think however that protein folding is very much understudied in the ML community, relative to say the big three of vision, NLP, and speech. The lack of standardized data sets and benchmarks, not to mention the need for domain knowledge, have made it difficult to get into the field

Hi! I’m the author of the paper. Not sure why you say Rosetta isn’t mentioned? It’s extensively referenced throughout the paper, discussed in the discussion section, and is one of the top 5 CASP servers compared to in the results section.

Also as for how it’s different from what’s described in the paper, that’s the topic of the introduction of the paper. Rosetta uses both fragment assembly and co-evolution methods.

I took CS221 from Andrew in 2006 (or was it 2007?) Even more has changed since then ;-) It was my second ML course, after taking Daphne Koller's punishing CS229. Right then though I knew ML will sweep the world pretty soon.

Funny thing is that the population of Texas is a little less than half that of France's (27 mil vs 66 mil), so the density differential isn't _that_ large, yet your point about the implausibility of 450 trains in Texas stands.

This is awesome! I was a pretty serious (classic) minesweeper player and was even ranked pretty highly internationally. Curious: did other people have their intuition translate "incorrectly" at first? I kept on blowing up cells because I hadn't yet fully internalized the changes that occur in 3D (I know the rules are the same, but the "pattern recognition rules" are not quite the same.) It was really interesting having to step back and rework out the implications of things in 3D. In some vague sense I feel like this is a bit like how some mathematical theorems hold in certain number of dimensions but not others (e.g. random walks). Cool game!

Yeah I can see that, but they're running into the other problem now where 12GB is just not that much more than 11GB and certainly not worth the 100% price increase. At 16GB they would at least be offering ~50% more memory.

Still no collapsable hierarchy of cells. This is the one feature I miss most from Mathematica notebooks.