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Architectures have evolved significantly since then. DeepSeek v4 =/= GPT-3. Even then, a great deal of complexity lies in everything surrounding the architect
by faurroar 4mo ago
Architectures have evolved significantly since then. DeepSeek v4 =/= GPT-3. Even then, a great deal of complexity lies in everything surrounding the architectures e.g. how do you implement them performantly on modern accelerators, how do you distribute the model across a set of accelerators, how do you post-train, etc. And pre-training itself is a dark art. If you legitimately think that frontier labs are doing something equivalent to whatever you wrote on your whiteboard, you’re clueless.
- jumploops 4mo agoThose are all just optimizations. We still don’t really know why they work, we just know how to build them.
- trollbridge 4mo agoWe don't really know why language works with humans, either. If you raise a baby from birth, you kind of observe how it is learning language, but the process is also rather mysterious. My eldest son's first word was to actually imitate a cow mooing, and then after that to imitate a motor noise of a tractor or truck. And then after that a meow. (His first complete sentence was "King Graham fell"...) My next child took a completely different path to language, including skipping all the non-verbal imitations. And then at some point, you just suddenly can two-way communicate with them when you couldn't before, and then after that, they can engage in reasoning.
- jumploops 4mo agoCompletely agree! It’s interesting to me how similar attempting to understand LLMs is to neuroscience. “When we turn this bit off, this other thing happens… if we change these weights the Eiffel Tower is now in Rome” We’re basically just probing around and trying to reverse engineer an emergent system. To your point, this system may be quite different from model to model (human to human) although some similarities likely occur. The comment I was responding to tried to belittle the OP’s understanding of transformers, by mentioning that running an LLM at scale is much harder than the simple white board diagram. My point was simply that we don’t know why they work, and all the extra optimizations isn’t the “thing” that makes it emergent. Simply scaling the “GPT” is good enough to see it, so the OP’s awe should stand. (On a side note, what other architectures can we scale to find similar emergent behavior?)
- trollbridge 4mo agoComputer vision ends up displaying emergent behaviour. It just "figures out" things.
- galaxyLogic 4mo agoIsn't the LLM simply predicting what should be the next sentences after user's input, using its algorithm and data it has exatrcted from existing texts on the internet. The algorithm that does that could have many different designs, some better some worse for the purpose of predicting what output makes most sense next? So what is it that we don't understand about why theyr work? The algorithm? We have the code. Why the specific algorithm makes such good predictions? I see it as a generalization of trying to predict who wins Kentucky Derby.
- ai_slop_hater 4mo agoHuman brain capabilities are truly amazing, imagine if people didn’t treat their children as if they are stupid and didn’t constantly lie to them, because kids are stupid right, they wouldn’t understand. What heights could be reached.
- baq 4mo agoWe don’t treat children like they’re stupid, we treat children like they’re children. A stupid adult is treated very differently than any child. Adults are expected to have their world models approximately correct in terms of physical environment so they won’t accidentally kill themselves by falling off a cliff; then there are the social norms which adults are expected to conform to so everyone is kinda predictable to everyone else so adults don’t kill each other too often over food or mates. Understanding of neither is expected from children.
- ai_slop_hater 4mo agoYou may have been raised properly since you don’t get what I mean. I really envy kids with “Chinese parents” that had them learn math early on and not some bullshit like that if you put your tooth under your pillow, then a tooth fairy will come.
- mejutoco 4mo agoI think those 2 are orthogonal. Math still works with Santa or the tooth fairy.
- ai_slop_hater 4mo agoMaybe math works but critical thinking doesn’t. There are people who have lived for many decades without ever questioning insane b.s. they were taught as kids.
- beezlewax 4mo agoIt is possible to have learned both things you know.
- slopinthebag 4mo agoHm, I wonder if it's more that we're shocked such a simple thing (relatively speaking) can work so well.
- malwrar 4mo agoIt was precisely that for me! Another commenter captures it well; “the bitter lesson” indeed.
- otabdeveloper4 4mo agoWe do know how they work. They predict the next statistically most likely token. The "bitter lesson" is that fake-it-till-you-make-it is a valid way of doing knowledge work. (Or not make it, then people will just claim you're holding the LLM wrong and it's not the AI's fault.)
- throw310822 4mo ago> statistically most likely token. Statistically most likely in what context, given which preconditions? Because each prompt sequence is unique so the probability of any token following it is unknown.
- skydhash 4mo agoIt’s not unknown because that’s what the model computes. It’s matrix multiplication just like shaders.
- throw310822 4mo agoAnd how do you know that the model computes it correctly?
- skydhash 4mo agoCorrectness is based on axioms and rules. You need to define your axioms and rules first before you can determine correctness. If you’re talking about matrix multiplication, I can use mathematical rules and axioms and proves formally that the multiplication is correct. For next token prediction, I can prove that the set of tokens is finite and that the next token is always part of that set. But things like grammar correctness, or semantic consistency over a few sentences are not hardcoded rules in the model. They’re emergent properties, mostly due to the amount and quality of data available for training. Quantization is mostly about how much we can shed without loosing a particular emergent properties (like dithering or psycho acoustic audio compression)
- klempner 4mo ago