Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid - welcome to the Stubsack, your first port of call for learning fresh Awful you’ll near-instantly regret.
Any awful.systems sub may be subsneered in this subthread, techtakes or no.
If your sneer seems higher quality than you thought, feel free to cut’n’paste it into its own post — there’s no quota for posting and the bar really isn’t that high.
The post Xitter web has spawned so many “esoteric” right wing freaks, but there’s no appropriate sneer-space for them. I’m talking redscare-ish, reality challenged “culture critics” who write about everything but understand nothing. I’m talking about reply-guys who make the same 6 tweets about the same 3 subjects. They’re inescapable at this point, yet I don’t see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldn’t be surgeons because they didn’t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I can’t escape them, I would love to sneer at them.
(Credit and/or blame to David Gerard - both for starting this, and for covering the previous week. We should get a bot to automate this...)
Google shut down the AlphaFold team, trying to reassign the people behind the super interesting problem of using ML to predict protein structure from sequence onto chatbot development. A whole bunch left the company.
https://www.engadget.com/2225849/google-shuts-down-alphafold/
The Business Idiots are truly running the asylum.
One of the defenses I see of LLMs on place like /r/singularity (although recently /r/singularity has started wising up and the committed true believers have shifted to /r/accelerate), is that they are going to cure cancer or some other incredibly valuable thing, conflating AI, ML in general, DNN, vs. LLMs specifically. Technology like AlphaFold was a lot more on the "cure cancer" track (although there is still a huge gap between better protein folding predictions and successful drug discovery) than anything LLM related.... this is sad.
This has always been a huge red herring. Llms are built on top of the transformer architecture which does text autocomplete (and yes we can combine text embeddings with other inputs like images). They have some interesting properties where they seem to be able to do text autocomplete in a bunch of different scenarios that they weren't explicitly trained for, but they were never designed for precise dna analysis. It is their architecture that prevents them from other long horizon tasks like playing chess and the way that they represent text is why they can never count the letters in strawberry (most have this specific question hard-coded in their training data now).
Anyone who believes that LLMs are going to solve cancer either has no idea how they work or has been one-shotted from talking to Claudia
I am continually amused at people not quite understanding what AlphaFold is actually doing, too.
Yes, a bunch of its performance comes from it learning rules about how proteins fold. But not a majority of its performance. MOST of its performance is it effectively acting as a translator of what evolution knows about protein folding into a form we can understand.
A key part of the system is not just cooking the sequence into a structure. A system running alphafold has a database of terabytes of curated sequence information from all over the tree of life. You put in the sequence you care about, and it first searches that database for anything with homology, and builds a "covariation matrix" - wherever theres anything with even vague sequence relatedness, build a matrix of every position in your sequence and the correlation between variation at position X and variation at position Y. This covariation matrix represents implicit information from the evolutionary process about what parts of a sequence are functionally connected to each other, which has a correlation to positional information, and these correlations are in turn learned by the ML system.
You put in de novo designed proteins or orphan proteins without homologs in the curated dataset and performance does not go away, but it drops precipitously. A bunch of what is going on is finding an evolutionary signal, and translating that evolutionary signal into structural information. So still, evolution knows much much more about protein folding than we do or any machine does, and once again a ML system is revealed to essentially be an information channel that takes in information from an interesting source on one end and turns it into a different form of information on the other.
Those things are barely related, no wonder the staff voted with their feet. It would be a bit like taking a team of seasoned architects who design amazing skyscrapers and telling them they have to spend all their time on low density suburban houses from now on... Come on guys it's still architecture. Where are you going?
Rather than rebelling against google, it seems like they where just poached by anthropic a short while before the project shut down:
Depends on what Anthropic wants to do with the poached people.
Oh... Well that's disappointing.
I was an academic in computer science in the last 10 years or so (keeping it vague to avoid doxxing myself) and it has been so depressing seeing so many of my colleagues selling out to OpenAI, Anthropic, Meta and even Google (for some reason the latter often gets a reputational free pass because people associate them with the golden days of big tech 10+ years ago)
I am actually in the middle of both trying to advance my career and a project about information theory in evolutionary biology making a bunch of explicit parallels to machine learning. Someone where I work suggested that given the connections I was making I should look to a 'frontier AI lab' as an employer.
He did not see the instant flashbacks to chasing these weirdos across the internet for almost two decades, watching in horror as the religious psychosis gained national prominence and great destructive power. All he got to hear was my instant intonation of "I'm sorry Dave, I'm afraid I can't do that."