321–330 of 504 posts
I feel like it's an important threshold moment if this gets accepted into scientific use without the model being available - reproducibility of results becomes dependent on the good graces of a single commercial entity. I kind of hope that like OpenAI it just spurs creation of equivalent open models that then actually get used.
Probably worth mentioning that David Baker’s lab released a similar model (predicts protein structure along with bound DNA and ligands), just a couple of months ago, and it is open source [1]. It’s also worth remembering that it was David Baker who originally came up with the idea of extending AlphaFold from predicting just proteins to predicting ligands as well [2]. 1. https://github.com/baker-laboratory/RoseTTAFold…
TFA announces AlphaFold 3.
Post: "Unlike AlphaFold 3, which predicts only a small, preselected subset of ligands, RosettaFold All Atom predicts a much wider range of small molecules"
TFA: "AlphaFold 3...*models large biomolecules such as proteins, DNA and RNA*, as well as small molecules, also known as ligands"
Post: "they also use the structure predicting model as the denoising model in a diffusion process...Presumably, DeepMind is working on this problem as well."
TFA: "AlphaFold 3 assembles its predictions using a diffusion network, akin to those found in AI image generators."
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Any organization/country that has the ability to use a tool like this to create a bio weapon is already sophisticated enough to do bioterrorism today.
Alright, but now picture this: it's now open to the masses, meaning an individual could probably even do it.
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I'm in the following camp: It is wrong to think about the world or the models as "complex systems" that may or may not be understood by human intelligence. There is no meaning beyond that which is created by humans. There is no 'truth' that we can grasp in parts but not entirely. Being unable to understand these complex systems means that we have framed them in such a way (f.e. millions of matrix operations) that doe…
"There is no 'truth' that we can grasp in parts but not entirely." The value of pi is a simple counterexample.
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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…
Spoiler: "Interpretable ML" will optimize for output that either looks plausible to humans, reinforces our preconceptions, or appeals to our aesthetic instincts. It will not converge with reality.
> One strong theme is the prevalence of context features (e.g. DNA, base64) and token-in-context features (e.g. the in mathematics – A/0/341, > [...]
> The general the in mathematical prose feature (A/0/341) has highly generic mathematical tokens for its top positive logits (e.g. supporting the denominator, the remainder, the theorem), whereas the more finely split machine learning version (A/2/15021) has much more specific topical predictions (e.g. the dataset, the classifier). Likewise, our abstract algebra and topology feature (A/2/4878) supports the quotient and the subgroup, and the gravitation and field theory feature (A/2/2609) supports the gauge, the Lagrangian, and the spacetime
I don't think "hundreds of different ways to represent the word 'the', depending on the context" is a-priori plausible, in line with our preconceptions, or aesthetically pleasing. But it is what falls out of ML interpretation techniques, and it does do a quantitatively good job (as measured by fraction of log-likelihood loss recovered) as an explanation of what the examined model is doing.
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Discovering underlying principles and predicting outcomes is two sides of the same coin in that there is no way to confirm you have discovered underlying principles unless they have some predictive power. Some had tried to come up with other criteria to confirm you have discovered an underlying principle without predictive power, such as on aesthetics - but this is seen by the majority of scientists as basically a co…
If all you can do is predict an outcome without being able to explain how then what have you really discovered? Asking someone to just believe you can predict outcomes without any reasoning as to how, even if you're always right, sounds like the concept of faith in religion.
There is a car. We think it drives by burning petrol somehow.
How do we test this? We take petrol away and it stops driving.
Ok, so we know it has something to do with petrol. How does it burning the petrol make it drive?
We think it is caused by the burned petrol pushing the cylinders, which are attached to the wheels through some gearing. How do we test it? Take away the gearing and see if it drives.
Anyway, this never ends. You can keep asking questions, and as long as the hypothesis is something you can test, you are doing science.
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Any organization/country that has the ability to use a tool like this to create a bio weapon is already sophisticated enough to do bioterrorism today.
Alright, but now picture this: it's now open to the masses, meaning an individual could probably even do it.
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 other words I bring a hypothesis to AF3 and ask for it to refute or affirm.
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If you're a scientist who works in protein folding (or one of those other areas) and strongly believe that science's goal is to produce falsifiable hypotheses, these new approaches will be extremely depressing, especially if you aren't proficient enough with ML to reproduce this work in your own hands. If you're a scientist who accepts that probabilist models beat interpretable ones (articulated well here: https://no…
The goal of science has always been to discover underlying principles and not merely to predict the outcome of experiments. I don't see any way to classify an opaque ML model as a scientific artifact since by definition it can't reveal the underlying principles. Maybe one could claim the ML model itself is the scientist and everyone else is just feeding it data. I doubt human scientists would be comfortable with that…
Can we differentiate?
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But we do know why, it's just not simple. The atoms interact with one another because of a variety of fundamental forces, but since there can be hundreds of thousands of atoms in a single protein, it's plainly beyond human comprehension to explain why it folds the way it does, one fundamental force interaction at a time.
Fair. I guess the interesting thing for protein folding research then is that there appears to be a way to approximate/simplify the calculations required to predict folding patterns that doesn’t require the precision of existing folding models and software. In essence, AlphaFold is an existence proof that there should be a way to model protein folding more efficiently.