251–260 of 270 posts
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It's not name “collision”, they took a biological name that somehow felt apt for what they where doing. To continue oblios's analogy, when you use the “hibernation mode” of your OS, it only has superficial similarity with how manals hibernate during winter…
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> That egg has long since hatched. I imagine there's entire companies in existence now, whose entire value proposition is clean human-generated data. At this point, the Internet as a data source is entirely and irrevokably polluted by large amounts of ducks and various other waterfowl from the Anseriformes order.
What an astonishing eudystopia this implies, after the soft-takeoff singularity Eliezer has predicted 300 of the last [0, 1) of...
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Those terms sound similar to biological concepts but they’re very different. Neural networks are not like brains. They don’t grow new neurons. A “neuron” in an artificial neural net is represented with a single floating point number. Sometimes even quantized down to a 4 bit int. Their degrees of freedom are highly limited compared to a brain. Most importantly, the brain does not do back propagation like an ANN does.…
Neural networks are explicitly modeled on brains. I don't know where this idea that "the things haves similar names but they're unrelated" trope is coming from. But it's not from people who know what they're talking about. Like I said, go back and read the research. Look at where it was done. Look at the title of Marvin Minksy's thesis. Look at the research on connectionism from the 40s. I would wager that every majo…
Just because a plane is named a F/A-18 Hornet doesn’t mean it shares flight mechanisms with an insect.
Artificial neural nets are very different from brains but in practice are very different, for the reasons I mentioned above, but also for the reason that no one is trying to build a brain, they are trying to predict clicks or recommend videos etc.
There is software which does attempt to model brains explicitly. So far we haven’t simulated anything more complex than a fly.
"Irrelevant" facts about cats are the most interesting part of a math problem, because they don't belong there. The math problem was also "irrelevant" to the information about cats, but at least its purpose was obvious because it was shaped like a math problem (except for the interesting barnacle attached to its rear.) Any person encountering any of these questions worded this way on a test would find the psychology…
LLMs seem to "think like a movie script"; if something is included, it's expected that it will be important later. It's a good thing to keep in mind when prompting them; it's generally a good idea to never go on tangents unless you're going to delete that tangent from the context once finished.
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I generally will respond to stuff like this with "people do this, too", but this result given their specific examples is genuinely surprising to me, and doesn't match at all my experience with using LLMs in practice, where it does frequently ignore irrelevant data in providing a helpful response. I do think that people think far too much about 'happy path' deployments of AI when there are so many ways it can go wrong…
> I generally will respond to stuff like this with "people do this, too" But why? You're making the assumption that everyone using these things is trying to replace "average human". If you're just trying to solve an engineering problem, then "humans do this too" is not very helpful -- e.g. humans leak secrets all the time, but it would be quite strange to point that out in the comments on a paper outlining a new Spec…
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The problem with your low-effort retort is that, for example, the brain can wield language without having to scan anywhere near hundreds of terabytes of text. People acquire language from vastly fewer examples, and are able to infer/postulate rules, and articulate the rules. We don't know how. While there may be activity going on in the brain interpretable as high-dimensional functions mapping inputs to outputs, you…
The problem with your answer is that you make affirmations using logical fallacies. We both don't know how LLMs, and brains works to produce output. Any affirmation toward that without proof is affirming things without any basis. For example in this response: > the brain can wield language without having to scan anywhere near hundreds of terabytes of text. The amount of text we need to train an LLM only goes down, ev…
Such a function is not inherently mysterious due to the size alone. For instance, if we fit a billion numeric points to a polynomial curve having a billion coefficients, we would not be mystified as to how the polynomial interpolates between the points.
Be that as it may, the trained neural network function does have mysterious properties, that is true.
But that doesn't mean we don't know how it works. We invented it and produced it by training.
To say that we completely don't understand it is like saying we don't understand thermodynamic because the laws of thermodynamic don't allow us to predict the path of a particle of gas in, and so we must remain mystified as to how the gas can take on the shape of the container.
Say we train a neural network to recognize digit characters. Of course we know why it produces the answer 3 when given any one of our training images of 3: we iterated on bumping the weights until it did that. When we give it a an image of 3 not in our training set and it produces some answer (either correctly 3 or something disappointing) we are less sure. We don't exactly know the exact properties of the multi-dimensional function which encode the "threeness" of the image.
Sure; so what? It's a heck of a lot more than we know about how a person recognizes a 3, where we had no design input, and don't even know the complete details of the architecture. We don't have a complete model of just one neuron, whereas we do have a complete model of a floating-point number.
Gas in a container is a kind of brain which figures out how to mimic the shape of the container using a function of a vast number of parameters governing the motion of particles. Should we be mystified and declare that we don't understand the thermodynamic laws we came up with because they don't track the path taken by a particle of gas, and don't explain how every particle "knows" where it is supposed to be so that the gas takes on the shape of the cylinder, and has equal pressure everywhere?