Academics Need to Wake Up on AI(popularbydesign.org) |
Academics Need to Wake Up on AI(popularbydesign.org) |
If all scientists suddnenly do nothing all day but play with AI --- all research grinds to a halt!
So it is able to process and act upon summaries and concepts. In other words, apply synthesis. What it can't do is understand what a useful result looks like without direction. So it could synthesize a billion pointless claims from source material, but we still need a human to know which ones matter (without a specialized framework to comprehend this). If you provide LLMs with an objective and source materials it is certainly capable of following threads of logic or building an argument backed by sources.
I understand the concerns about AI, but it is a powerful tool for discovery and synthesis.
> But the AI doesn't even have "own words"; they are the training data's words.
If the AI understands those words, in what sense aren't they its 'own words'? Are you arguing that nothing but neologisms count?
Absolutely true haha, even outside of academia.
Software developers wax poetic about the value of 'handcrafted human-made software', but really they just don't want to lose their cushy $300k WFH job.
They really don't; devs are the highest consumers of AI by far. Designers, on the other hand, have already been obsoleted and exist basically as makework generators. They can put plenty of useless bullshit in figma and have zero ability to execute.
We can't since it is a vapid, unsourced, AI mania fueled piece that could have been written by AI.
I suppose the associate professor wants AI funding.
"Oh! That explains it!"
"Uh..."
"I didn't want to say anything rude, but the whole time I was like 'yikes, how did this idiot become a professor at Notre Dame?'"
"Actually -"
"Heh! You got me good! Of course it was written with AI. Duh. These ideas are so vacuous and shallow, there's no way a fancy professor like you-"
"Actually, I asked the AI to write an essay summarizing my arguments from social media."
"...oh."
"..."
"...hey you should log off BlueSky, it's not healthy there."
But it is the same transformer architecture, and it is able to generate novel proteins in the same way that an LLM is able to generate novel sentences. AlphaFold 3 is a diffusion model, so it's most similar to the AI art generators.
Protein folding is a kind of syntax. If you train on protein folding and then use it to obtain protein folding results, you are using a screwdriver to drive a screw: that checks out.
Nobody should be arguing along lines analogous to the claim that a good neural net trained on handwritten digits is not suitable for classifying handwritten digits.
At most, RL can be described as injecting information from a secondary source. It is not extending a model's programming to do anything other than what it was already doing, probability-based token prediction. It simply alters the probabilities.
With sufficient RL sampling/training, there's no reason an LLM couldn't similarly develop entirely new skills, especially in verifiable domains like math and code.
> It simply alters the probabilities.
Yes? What else would a learning system do besides alter its behavior? (and you can just sample with argmax or pseudo-randomly of you think probabilities are a problem)
Similarly, people often object to using words like “reasoning” and “understanding” in relation to models, but again, functionally, models observably demonstrate both of those qualities - you can test for them and measure their proficiency.
The fact that this discovery, training, and understanding is implemented in terms of a statistical model isn’t really relevant. If it were, you could similarly argue that humans don’t discover, reason, or understand, we just process chemical and electrical signals through our biological neural network.