I think the short of it is that it seems to me that you are confusing a system having very good heuristics for having a solid understanding of principles of reality. Heuristics, without an understanding of principles, is what I mean by rote pattern matching. But heuristic break down, particularly in edge cases. Yes, humans also rely heavily on heuristics because we generally seek to use the least effort possible. But we also can mitigate against those shortcomings by reasoning about basic principles. This shortcoming is why I think both humans and AI can make seemingly stupid mistakes. The difference is, I don't think you've provided evidence that AI can have a principled understanding while we can show that humans can. Having a principled understanding is important to move from simple "cause-effect" relationships to understanding "why". This is important because the "why" can transfer to many unrelated domains or novel scenarios.
E.g., racism/sexism/...most -'isms' appear to be a general heuristics that help us make quick judgements. But we can also our decision-making process by reverting to basic principles, like the idea that humans have equal moral worth regardless of skin tone or gender. AI can even mimic these mitigations, but you haven't convinced me that it can fundamentally change away from it's training set based on an understanding of basic principles.
As for the Go example, a novice would be able to identify that somebody is drawing a circle around it's pieces; your link even states this. But you recharacterizing this as a specific strategy is weird when that strategy causes you to lose the game. It misses the entire meaning of strategy. We see the limitations of AI in it's reliance to training data from autonomous vehicles to healthcare. They range from the serious (cancer detection) to the humorous (Marines overtaking robots by hiding in boxes like in Metal Gear). The paper you referenced similarly shows it is reliant on proximity to the training set, rather than actually understanding the underlying principles.
>Did you read the paper? The authors admit it is only narrowly learning and cannot transfer it's knowledge to unknown areas. From the article: "we do not expect our language model to generate proteins that belong to a completely different distribution or domain"
Good thing they don't make sweeping declarations or say anything about that meaning narrow learning without transfer. Jumping the shark yet again.
>We find that without prior knowledge, information emerges in the learned representations on fundamental properties of proteins such as secondary structure, contacts, and biological activity. We show the learned representations are useful across benchmarks for remote homology detection, prediction of secondary structure, long-range residue–residue contacts, and mutational effect.
From the sequences of just the proteins alone, Language Models learn underlying properties that transfer to a wide variety of use cases. So yes, they understand proteins in any definition that has any meaning.
Wrong comment to respond to; if you can’t wait to reply, that might indicate it’s time to take a step back.
>Good thing they don't make sweeping declarations or say anything about that meaning narrow learning without transfer.
That's exactly what that previous quote means. Did you read the methodology? They train on a universal training set and then have to tune it using a closely related training set for it to work. In other words, the first step is not good enough to be transferrable and needs to be fine tuned. In that context, the quote implies the fine tuning pushes the model away from a generalizable one into a narrow model that no longer works outside that specific application. Apropos to this entire discussion, it means it doesn't perform well in novel domains. If it could truly "understand proteins in any definition", it wouldn't need to be retrained for each application. The word you used ('any') literally means "without specification"; the model needs to be specifically tuned to the protein family of interest.
You are quoting an entirely different publication in your response. You should use the paper from which I quoted to refute my statement, otherwise this is the definition of cherry picking. Can you explain why the two studies came to different conclusions? It sure seems like you're not reading the work to learn and instead just grasping at straws to be "right." I have zero interest in having a conversation where someone just jumps from one abstract to another just to argue rather than adding anything of substance.
>I think the short of it is that it seems to me that you are confusing a system having very good heuristics for having a solid understanding of principles of reality.
Humans don’t have a grasp of the “principles of reasoning” and as such are incapable of distinguishing “true”, "different" or “heuristic” assuming such a distinction is even meaningful. Where you are convinced of “faulty shortcut”, I simply think “different”. Multiple ways to skin a cat. a plane's flight is as "true" as any bird. There's no "faulty shortcut" even when it fails in ways a bird will not.
You say humans are "true" and LLMs are not but you base it on factors that can be probed in humans as well so to me, your argument simply falls apart. This is where our divide stems from.
>I don't think you've provided evidence that AI can have a principled understanding while we can show that humans can.
What would be evidence to you? Let’s leave conjecture and assumptions. What evaluation exist that demonstrate this “principled understanding” in humans? and how would we create an equitable test in LLMs?
>a novice would be able to identify that somebody is drawing a circle around it's pieces; your link even states this. But you recharacterizing this as a specific strategy is weird when that strategy causes you to lose the game.
You misunderstand. I did not characterize this as a specific “strategy”. Not only do modern Go systems not learn like humans, but they also don’t learn from human data at all. KataGo didn’t create a heuristic to play like a human because it didn’t even see humans play.
>The paper you referenced similarly shows it is reliant on proximity to the training set, rather than actually understanding the underlying principles.
Even the authors make it clear this isn’t necessarily the bridge to take so it’s odd to see you die on this hill.
The counterfactual of syntax is
Finding the main subject and verb of something like “Think are the best LMs they.” in verb-obj-subj order (they, think) instead of “They think LMs are the best.” in subj-verb-obj order (they, think). LLMs are not being trained on text like the former to any significant degree if at all yet the performance is fairly close. So what, it doesn’t “underlying principles of syntax” but still manages that ?
The problem is that you take a fairly reasonable conclusion from these experiments. I.e LLMs can/often also rely on narrow, non-transferable procedures for task-solving and proceed to jump the shark from there.
>but you haven't convinced me that it can fundamentally change away from it's training set based on an understanding of basic principles.
We see language models create novel functioning protein structure after training, no folding necessary.
Did you read the paper? The authors admit it is only narrowly learning and cannot transfer it's knowledge to unknown areas. From the article:
"we do not expect our language model to generate proteins that belong to a completely different distribution or domain"
So, no, I do not think it displays a fundamental understanding.
>What would be evidence to you?
We've already discussed this ad nauseum. Like all science, there is no definitive answer. However, when the data shows evidence that something like proximity to training data is predictive of performance, it's seems more like evidence of learning heuristics and not underlying principles.
Now, I'm open to the idea that humans just have a deeper level of heuristics rather than principled understanding. If that's the case, it's just a difference of degree rather than type. But I don't think that's a fruitful discussion because it may not be testable/provable so I would classify it as philosophy more than anything else and certainly not worthy of the confidence that you're speaking with.
E.g., racism/sexism/...most -'isms' appear to be a general heuristics that help us make quick judgements. But we can also our decision-making process by reverting to basic principles, like the idea that humans have equal moral worth regardless of skin tone or gender. AI can even mimic these mitigations, but you haven't convinced me that it can fundamentally change away from it's training set based on an understanding of basic principles.
As for the Go example, a novice would be able to identify that somebody is drawing a circle around it's pieces; your link even states this. But you recharacterizing this as a specific strategy is weird when that strategy causes you to lose the game. It misses the entire meaning of strategy. We see the limitations of AI in it's reliance to training data from autonomous vehicles to healthcare. They range from the serious (cancer detection) to the humorous (Marines overtaking robots by hiding in boxes like in Metal Gear). The paper you referenced similarly shows it is reliant on proximity to the training set, rather than actually understanding the underlying principles.