361–370 of 622 posts
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Yep. falcor84: you’re thinking of the so-called ‘multilayer perceptron’ which is basically an archaic name for a (densely connected?) neural network. I was referring to traditional perceptrons.
While ReLU is relatively new, AI researchers have been aware of the need for nonlinear activation functions and building multilayer perceptrons with them since the late 1960s, so I had assumed that's what you meant.
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I thought maybe they were on the right track until I read Attention Is All You Need. Nah, at best we found a way to make one part of a collection of systems that will, together, do something like thinking. Thinking isn’t part of what this current approach does. What’s most surprising about modern LLMs is that it turns out there is so much information statistically encoded in the structure of our writing that we can u…
I see takes like this all the time and its so confusing. Why does knowing how things work under the hood make you think its not on the path towards AGI? What was lacking in the Attention paper that tells you AGI won't be built on LLMs? If its the supposed statistical nature of LLMs (itself a questionable claim), why does statistics seem so deflating to you?
But how do you know a magician that knows how to do card tricks isn't going to arrive at real magic? Shakes head.
Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs? I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go. GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential…
I don't think we've even started to get the most value out of current gen LLMs. For starters very few people are even looking at sampling which is a major part of the model performance. The theory behind these models so aggressively lags the engineering that I suspect there are many major improvements to be found just by understanding a bit more about what these models are really doing and making re-designs based on…
"While the model was initially expected to significantly surpass previous versions of the technology behind ChatGPT, it fell short in key areas, particularly in answering coding questions outside its training data." Right. If you generate some code with ChatGPT, and then try to find similar code on the web, you usually will. Search for unusual phrases in comments and for variable names. Often, something from Stack Ov…
> Right. If you generate some code with ChatGPT, and then try to find similar code on the web, you usually will. People who "follow" AI, as the latest fad they want to comment on and appear intelligent about, repeat things like this constantly, even though they're not actually true for anything but the most trivial hello-world types of problems. I write code all day every day. I use Copilot and the like all day every…
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> "let's only allow the LLM to do things we know it is rock-solid at." Even this is insanely hard in my opinion. The one thing that you would assume LLM to excel at is spelling and grammar checking for the English language, but even the top model (GPT-4o) can be insanely stupid/unpredictable at times. Take the following example from my tool: https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samples... 5 models are…
I was using an LLM to help spot passive voice in my documents and it told me "We're making" was passive and I should change it to "we are making" to make it active. Leaving aside "we're" and "we are" are the same, it is absolutely active voice
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Novel as in requiring novel reasoning to sort out. One of the classic ways to expose the issue is to take a common puzzle and introduce irrelevant details and perhaps trivialize the solution. LLMs pattern match on the general form of the puzzle and then wander down the garden path to an incorrect solution that no human would fall for. The sort of generalization these things can do seems to mostly be the trivial sort:…
Why is your criteria for "on the path towards AGI" so absolutist? For it to be on the path towards AGI and not simply AGI it has to be deficient in some way. Why does the current failure modes tell you its on the wrong path? Yes, it has some interesting failure modes. The failure mode you mention is in fact very similar to human failure modes. We very much are prone to substituting the expected pattern when presented…
They've simply run out of data to use to fabricate legitimate-looking guesses. They can't create anything that doesn't already exist.
> They can't create anything that doesn't already exist. Just increase the temperature.
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Right, but its behavior didn't change after you learned more about it. Why should that cause you to update in the negative? Why does learning how it work not update you in the direction of "so that's how thinking works!" rather than, "clearly its not doing any thinking"? Why do you have a preconception of how thinking works such that learning about the internals of LLMs updates you against it thinking?
If you didn’t know what an airplane was, and saw one for the first time, you might wonder why it doesn’t flap its wings. Is it just not very good at being a bird yet? Is it trying to flap, but cannot? Why, there’s a guy over there with a company called OpenBird and he is saying all kinds of stuff about how bird-like they are. Where’s the flapping? I don’t see any pecking at seed, either. Maybe the engineers just have…