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AlphaFold 3 predicts the structure and interactions of life's molecules

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Re: AlphaFold 3 predicts the structure and interactions of life's molecules

#391

Post by xixixao »

kobenni wrote: post #382
stouset wrote: post #363

Earlier quoted context omitted.

> If you're the sort of person who believes that human brains are capable of understanding the "why" of how things work in all its true detail, you'll find this an interesting challenge- can we actually interpret these models, or are human brains too feeble to understand complex systems without sophisticated models? I think chess engines, weirdly enough, have disabused me of this notion. There are lots of factors a h…

I don't understand why you would draw this conclusion. The deep search you describe is an algorithm that humans can understand perfectly fine. Humans just can't solve it in their heads and need to let a computer handle the number crunching. Just like a scientist may understand the differential equations to describe a system perfectly fine, but require a computer to approximate the solution for an initial value proble…

The scientist can understand “how” the model works, how many layers there are, that each neuron has a weight, that some are connected… Parent comment and yours show that “understanding” is a fuzzy concept.

Re: AlphaFold 3 predicts the structure and interactions of life's molecules

#392

Post by tsimionescu »

imchillyb wrote: post #154
_yb2s wrote: post #120

Earlier quoted context omitted.

It means we now have an accurate surrogate model or "digital twin" that can be experimented on almost instantaneously. So we can massively accelerate the traditional process of developing mechanistic understanding through experiment, while also immediately be able to benefit from the ability to make accurate predictions, even without needing understanding. In reality, science has already pretty much gone this way lon…

Medicine can be explained fairly simply, and the why of how it works as it does is also explained by this: Imagine a very large room that has every surface covered by on-off switches. We cannot see inside of this room. We cannot see the switches. We cannot fit inside of this room, but a toddler fits through the tiny opening leading into the room. The toddler cannot reach the switches, so we equip the toddler with a p…

The more important point was that medications that do work still come in two forms: ones where we have a good idea of the mechanism of action that makes them work, and ones where we don't.

For example, we have a good idea of why certain antibiotics cure tuberculosis - we understand that tuberculosis is caused by certain bacteria, and we know how antibiotics affect the cellular chemistry of those bacteria to kill them. We also understand the dynamics of this, the fact that the body's immune system still has to be functioning well enough to kill many of the bacteria as well, etc. We don't fully understand all of the side-effects and possible interactions with other diseases or medications in every part of the body, but we understand the gist of it all.

Then there are drugs and diseases where we barely understand any of it. We don't have for example a clear understanding of what depression is, what the biochemistry of it is. We do know several classes of drugs that help with depression in certain individuals, but we know those drugs don't help with other individuals, and we have no way of predicting which is which. We know some of the biochemical effects of these drugs, but since we don't understand the underlying cause of depression, we don't actually know why the drugs help, or what's the difference in individuals where they don't help.

There are also widely used medications where we understand even less. Metamizole, a very widely used painkiller sold as Novalgin or Analgin and other names, discovered in 1922, has no firmly established mechanism of action.

Re: AlphaFold 3 predicts the structure and interactions of life's molecules

#393

Post by goodmachine »

moconnor wrote: post #32

Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-int…

In order for that not to happen (uninterpretable ML models) some research on symbolic distillation, aka symbolic regression

https://arxiv.org/abs/2006.11287

https://www.science.org/doi/10.1126/sciadv.aay2631

Re: AlphaFold 3 predicts the structure and interactions of life's molecules

#394

Post by d0mine »

throwaway4aday wrote: post #337
nsoonhui wrote: post #335

Here's something that bugs me about ML: all we have is prediction and no explanation how we come to that prediction, ie: no deeper understanding on the underlying principles. So despite that we got a good match this time, how can we be sure that the match will be equally good next time? And how to use ML to predict structure that we have no baseline to start with or experimental result to benchmark ? In the absence o…

Speaking of physics, we should borrow the quote "Shut up and calculate" to describe the situation: it works so use it now and worry about the explanations later.

Except the model is not open-source. You can't calculate anything.

Re: AlphaFold 3 predicts the structure and interactions of life's molecules

#395

Post by tsimionescu »

bluerooibos wrote: post #180
nybsjytm wrote: post #62

Important caveat: it's only about 70% accurate. Why doesn't the press release say this explicitly? It seems intentionally misleading to only report accuracy relative to existing methods, which apparently are just not so good (30%, 50% in various settings). https://www.fastcompany.com/91120456/deepmind-alphafold-3-dn...

That's pretty good. Based on the previous performance improvements of Alpha-- models, it'll be nearing 100% in the next couple of years.

Which specific AlphaX model evolved like that? Most of the ones that were in the press had essentially a single showing, typically very good, but didn't really improve after that.

Re: AlphaFold 3 predicts the structure and interactions of life's molecules

#396

Post by barrenko »

pen2l wrote: post #132
moconnor wrote: post #32

Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-int…

The most moneyed and well-coordinated organizations have honed a large hammer, and they are going to use it for everything, and so almost certainly future big findings in the areas you mention, probabilistically inclined models coming from ML will be the new gold standard. But yet the only thing that can save us from ML will be ML itself because it is ML that has the best chance to be able to extrapolate patterns fro…

Interpretable AI is the same scam as alignment.

Re: AlphaFold 3 predicts the structure and interactions of life's molecules

#397

Post by [deleted] »

moconnor wrote: post #32

Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-int…

[deleted]

Re: AlphaFold 3 predicts the structure and interactions of life's molecules

#398

Post by [deleted] »

moconnor wrote: post #32

Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-int…

[deleted]

Re: AlphaFold 3 predicts the structure and interactions of life's molecules

#399

Post by stouset »

kobenni wrote: post #382
stouset wrote: post #363

Earlier quoted context omitted.

> If you're the sort of person who believes that human brains are capable of understanding the "why" of how things work in all its true detail, you'll find this an interesting challenge- can we actually interpret these models, or are human brains too feeble to understand complex systems without sophisticated models? I think chess engines, weirdly enough, have disabused me of this notion. There are lots of factors a h…

I don't understand why you would draw this conclusion. The deep search you describe is an algorithm that humans can understand perfectly fine. Humans just can't solve it in their heads and need to let a computer handle the number crunching. Just like a scientist may understand the differential equations to describe a system perfectly fine, but require a computer to approximate the solution for an initial value proble…

“Knowing” that some line works to some ridiculous depth is different than understanding how and why.

And at some level the answer is simply “because every possible refutation fails” and there is no simpler pattern to match against nor intuition to be had. That is the how and why of it.

Re: AlphaFold 3 predicts the structure and interactions of life's molecules

#400

Post by drdeca »

d0mine wrote: post #385
drdeca wrote: post #361

Earlier quoted context omitted.

“calculate” in that phrase, refers to doing the math, and the understanding that that entails, not pressing the “=“ button on a calculator.

Why do you think systems of partial differential equations (common in physics) are somehow provide more understanding than the corresponding ML math (at the end of the day both can produce results using a lots of matrix multiplications).

... because people understand things about what is described when dealing with such systems in physics, and people don't understand how the weights in ML learned NNs produce the overall behavior? (For one thing, the number of parameters is much greater with the NNs)
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