Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

AlphaFold: a solution to a 50-year-old grand challenge in biology

deepmind.com

Post reply on HN
Print view View original

611–620 of 683 posts

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#611

Post by hoseja »

mabbo wrote: post #416
treis wrote: post #143

Earlier quoted context omitted.

That is an impressive improvement, but I think you've missed the most important point: >a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods So DeepMind is to the point where it's a question of whether their generated model or the experimentally determined structure is closest to the actual physical structure.

Then we get the really fun question: if the experimentally determined structure is only 90% accurate, can machine learning actually reach 100%? Can you learn exact truth from inexact examples? Which gets into the concept of whether the ML model has actually learned some deeper conceptual ideas than we have, some deeper truth about how this works. If so, can we somehow extract that truth, or is it truly a black box th…

There is no 100%. Proteins are flexible. Curious how this deals with that even more fiendishly difficult fact.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#612

Post by ovi256 »

Seanambers wrote: post #250
tonfa wrote: post #226

Earlier quoted context omitted.

> So it wasn't out of reach for academia, pharmaceuticals, or others with a bit of resources. How much does hiring a deepmind-like team cost though? (massively more than the TPU resources?) Still within reach of pharmaceutical industry I guess, but maybe not so easy for academia.

From what I can gather, Google bought Deepmind for 500 million USD in 2014, they have outstanding debt to its parent company as of 2019 of 1.3 billion USD. And they had income around 100 million in 2019 but it's all against Google, so looks like a 2 billion +/- 0.5 operation so far, and who knows if they pay for compute. Other articles place the runrate at 500 million per year in 2019. Which means 500 million * 6 yea…

I appreciate this tremendous 3.5B subsidy that Google brought to basic ML research and R&D.

There is barely any multinational that has the freedom Google had of planning to spend 3.5B with no ROI. Their shareholders would sue and vote the managers out.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#613

Post by _0w8t »

sudosysgen wrote: post #540
drusepth wrote: post #535

Earlier quoted context omitted.

Well, this relies on the assumption that a God also inherently exists outside of time and space, which is debatable even among religious scholars.

I just had a discussion with a friend about this! It's indeed a very difficult question. We ended on the conclusion that God can't possibly exist outside of time and space in the Abrahamic tradition because he precedes the creation of the Universe, but I'm sure there's a twist we missed somewhere.

One cannot define the notion of “exist” without explicitly or implicitly referring to time. So the question of existence without time reduces to absurdity like can one exist without existing?

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#614

Post by stefanka »

Where will the development go on from now? We have been working on a geometrical approach that avoids the curse of dimensionality to solving the same problem for the last few months. Now, I wonder whether it makes sense to continue at all (we were and are clearly not ready to participate in the challenge yet). So What remains unresolved? Exact position of side chains? Can their approach be used for protein-protein interaction too?

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#615

Post by jaredmoon »

ramraj07 wrote: post #236
mncharity wrote: post #64

Additional commentary in Science: https://www.sciencemag.org/news/2020/11/game-has-changed-ai-... (submitted by furcyd : https://news.ycombinator.com/item?id=25254888 ).

The most amazing part: > The organizers even worried DeepMind may have been cheating somehow. So Lupas set a special challenge: a membrane protein from a species of archaea, an ancient group of microbes. For 10 years, his research team tried every trick in the book to get an x-ray crystal structure of the protein. “We couldn’t solve it.” > But AlphaFold had no trouble. It returned a detailed image of a three-part pro…

That is amazing, very excited to see how this will effect the biotech industry.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#616

Post by sockpuppet_12 »

scoopertrooper wrote: post #547
mensetmanusman wrote: post #523

Earlier quoted context omitted.

That would require the AI to exist outside of time and space.

From the perspective of a human observer, an AI more or less does exist outside of space and time. It can travel at the speed of light through radio broadcast (with some caveats). It can spend the equivalent of a lifetime learning a topic in just a few days.

But if you unplug the machine its running on, its over.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#619

Post by pyhtel »

sockpuppet_12 wrote: post #616
scoopertrooper wrote: post #547

Earlier quoted context omitted.

From the perspective of a human observer, an AI more or less does exist outside of space and time. It can travel at the speed of light through radio broadcast (with some caveats). It can spend the equivalent of a lifetime learning a topic in just a few days.

But if you unplug the machine its running on, its over.

Assuming the AI isn't a distributed system, sure

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#620

Post by knuthsat »

garmaine wrote: post #411
whimsicalism wrote: post #350

Earlier quoted context omitted.

Rude. I would appreciate substantive criticism, especially when I'm linking papers in Nature starting to do exactly what I'm talking about.

I cannot give constructive feedback to something which is incomprehensible. "the genome is a sequence that can be language modeled/auto-regressed for depth of understanding by the network" The genome is not a sequence so much as a discrete set of genes which are themselves sequences which specify construction plans for proteins. That distinction is important. Language modeling in the context of machine learning typic…

That statement you quote is completely understandable.

Let's say you have discrete sequences that are a product of a particular distribution.

Unsupervised methods are able, by just reading these sequences, to construct a compact representation of that distribution. The model has managed to untangle the sequences into a compact representation (weights in a neural network) that allows you to use it for other, higher level supervised tasks.

For example, the transformer model in NLP allowed us to not have to do part-of-speech tagging, dependency parsing, named entity recognition or entity relationship extraction for a successful language-pair translation system. The compact transformer model managed to remap the sequences into a representation that allows direct translation (people have inspected these models and figured out the internal workings of it and realized it does have latent information about a parse tree of a sentence or part-of-speech of a word).

Another interesting note is that designers of the transformer architecture did not incorporate any prior linguistic knowledge when they were designing it (meaning that the model is not designed to model language but just a discrete sequence).

Post reply on HN