571–580 of 683 posts
Earlier quoted context omitted.
That would require the AI to exist outside of time and space.
Well, this relies on the assumption that a God also inherently exists outside of time and space, which is debatable even among religious scholars.
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.
Earlier quoted context omitted.
> I don’t think we would do ourselves a service by not recognizing that what just happened presents a serious indictment of academic science. Much like other fields, I do begin to question the academic structure to making advances. It appears something is rotten in the state of academia. Oddly it's academia doing incremental improvements to existing methods but industry making novel leaps and bounds... The other majo…
> The other major case in point being NLP Speaking of which, Google Translate was published in 2006, but when did the "learning from data" approach became an accepted idea in machine translation? I think the earlier attempts at machine translation were more about trying to codify grammar rules in software, than doing statistical learning from large text corpuses? I remember in 2002, the approach of leaning protein su…
When we discuss Google's input in NLP, the most important contribution is certainly the "Attention is all you Need" paper, which paved the way for BERT and GTP (Alphafold also uses Attention networks, btw)
Earlier quoted context omitted.
What does that Å mean? Never seen our letter been used in a scientific context.
0.1nm - approximately a size of an atom - used in organic chemistry often.
Earlier quoted context omitted.
> The other major case in point being NLP Speaking of which, Google Translate was published in 2006, but when did the "learning from data" approach became an accepted idea in machine translation? I think the earlier attempts at machine translation were more about trying to codify grammar rules in software, than doing statistical learning from large text corpuses? I remember in 2002, the approach of leaning protein su…
Not really. Using a statistical approach to text modelling, specifically using Markov Chains, was proposed by Shannon in 1948. But yeah, there's a point in the 2000s where generative grammar/ symbolic approaches were pretty much left behind by NN methods. When we discuss Google's input in NLP, the most important contribution is certainly the "Attention is all you Need" paper, which paved the way for BERT and GTP (Alp…
Which is the same thing as hand-engineered feature stacks being left behind in vision problems, really. The story in every field is more or less "you're not clever enough to engineer good features"; "you might be clever enough to define good symmetries for the feature space in which the features live... maybe" (convolutional neural networks in image problems); "... but maybe not even that" (attention mechanisms).
Earlier quoted context omitted.
We don't know too much about the exact model they made but it looks sufficiently generalizable to be able to give a candidate protein structure for any given sequence. It doesn't automatically cure cancer and inject the drug but that by itself is an amazing tool that if available to everyone will revolutionize biology experimentation. I will definitely blame the protein structure field in multiple levels though. It w…
The reason for your second paragraph is pretty straightforward. There has been an immense amount of support for proteins as "the workhorses of the cell" for hundred+ years. I call it the "protein bias". We've seen in many times- for example when it was first hypothesized and then proved that DNA, rather than protein, is the heredity-encoding material, and seen many times, for example in the denial that RNA could act…
Earlier quoted context omitted.
I have a related question about this. If experimental methods produce results around a score of 90, what is the baseline we are comparing the DeepMind results against? If the experimental error is equal to the observed DeepMind error, how can we say which one is actually more erroneous?
Excellent question. At somepoint, I think the only answer is, "have a bunch of different people run a bunch of experiments on the same protein." The threshold for "real" in particle physics is +5 sigma. Which takes a lot of data.
Earlier quoted context omitted.
Academia is for generating problem solvers. Teams are small and made of people who will be there for around 5 years. A better comparison would be to national labs, but they are tasked with projects that make no sense for industry to tackle. The system is working as intended, all players are needed. The team at Alphafold busted their chops in academia and went on to working on problems they could spend decades on.
People seem to forget that you need a system like academia that's allowed to fail. Most companies aren't allowed to fail when they need to have quarterly returns. Of course academia has become more and more competitive. But tbh I think the answer is that the funding hasn't increased equally with the number of quality people who could stay in academia. But who knows.
We've also seen a rise of VC-backed research startups (like DeepMind but many others) whose value proposition (to the VC) only makes sense if the goal is to demonstrate a research capacity to get them bought out by a big company, or as a moonshot to out-compete them on an actual product made possible by the research. Investing in these little research startups themselves is also giving companies a way to push research without having to deal with having the researchers as direct employees, and I'm sure makes some of the startup employees feel a bit safer since there's a separation of money and operation influence. One similarity with modern academia is it selects for those who can do good work but who are also good bureaucrats (write grant proposals well, advising politicians, etc), startups have a selection for good work + good at courting VCs. But the startup just needs a few of them, then they can hire people who just want to do good work.
Another thing that makes corporate even better is they can occasionally spin off research developments into products, they can have some nice advances that only come when you try to productize, and among other reasons by not having to bother with external publishing (which takes time + fights with lawyers and business people) they can routinely be 10+ years ahead of whatever the state of the art in academia is.