101–110 of 126 posts
Earlier quoted context omitted.
There are already tons of systems (for example Google Translate) that combine rule-based reasoning with probabilistic reasoning. Looks to be working to me.
Interesting. Do you have any sources on Google Translate using rule-based reasoning?
Earlier quoted context omitted.
Why? Eg in the latter half of the 20th century the US integrated women into the workforce (almost doubling the population eligible for participation in the labourforce), without violence or uproar. There was also remarkably little uproar nor violence when the Czech Republic escaped the Iron Curtain and embraced capitalism.
Integrating women into the workforce is not even close to abolishing private capital and the need to work. The violence would be between billionaires with infinite automation making infinite money, and common folk with no way to eat.
Earlier quoted context omitted.
I think "high quality, well-prepared sources" would include blogs and articles, which are likely to become heavily influenced by AI (blogs and articles are high quality compared to Reddit posts for example, which were included in the past). In fact, there's no reason to think that academic papers won't start using language models to write better. Tainting your text with AI can be as simple as pasting a paragraph in a…
I honestly don’t understand why “tainting” is such a big deal. Can someone explain it to me? I see two possible reasons, but neither seems to be worth the purity concern. The first is that AI can be wrong, make stuff up, be confidently incorrect. Anyone who has been on the internet knows this isn’t exactly a game changer. Second is that we won’t be training AI to be like humans, but like humans + AI. Also doesn’t see…
LLMs weren't training AI to be like humans. They were training AI to be able to predict what humans (and other sources of common crawl data) will write next in their texts. This might seem like a small difference but it's not. Consider for example someone whose career is to research ant behavior. Their job in some sense is to be able to predict what an ant will do. Does this mean that in the course of their academic training and scientific research, this researcher is being trained to be like an ant?
Earlier quoted context omitted.
Sorry if I was unclear. I know that the model is incentivised to accurately predict the probability distribution of the next token. I mean that the model is not being incentivised to literally produce the output tokens corresponding to "I don't know" when asked a question where it is uncertain.
Yes, exactly. What I wanted to emphasize is that the training _does_ actually incentivize the model to say "I don't know" but on a lower level.
Earlier quoted context omitted.
It’s always interesting to think about the exact technological analog for our biological sensors, but I believe that our vision would be way less than that in terms of raw data. We have a super high-res area at the center of vision (the fovea), but the rest is extremely low resolution (but with high movement and light sensitivity). I think we could reasonably say that if an optical nerve has 1mm neurons on average, a…
I don't think you correctly calculate bandwidth in this case. You assume 1 bit per neuron per tick, but time when it fires within the tick also matters, and that information is missing from multipliers. Also, there's no reason to use data from optical nerves as input, as it is already precompressed. You should be counting optical receptors instead (120 000 000).
I don’t believe it is precompressed as it hasn’t been processed by the visual cortex yet, no? Aren’t the optical receptors simply an artifact of the “sensor design”? E.g. if the refractory period of an optical receptor is 100x that of the neuron (or you simply need to cover a certain area, as you probably have tons of receptors attached to a single neuron outside the fovea and a small number per neuron inside the fovea), you’d hook up 100 optical receptors per neuron to use its full capacity. I think this is less compression and more combining a bunch of low information channels into a higher information channel.
All we really care about here is the amount of information reaching the brain not what your physical eye is capable of receiving, so I think using the nerve makes the most sense. There’s an interesting direct analogy: we don’t really care about the number of CCD sensors in the camera that took the image, we only care about how much information is in the video coming from the camera.
Earlier quoted context omitted.
I honestly don’t understand why “tainting” is such a big deal. Can someone explain it to me? I see two possible reasons, but neither seems to be worth the purity concern. The first is that AI can be wrong, make stuff up, be confidently incorrect. Anyone who has been on the internet knows this isn’t exactly a game changer. Second is that we won’t be training AI to be like humans, but like humans + AI. Also doesn’t see…
> Second is that we won’t be training AI to be like humans, but like humans + AI. LLMs weren't training AI to be like humans. They were training AI to be able to predict what humans (and other sources of common crawl data) will write next in their texts. This might seem like a small difference but it's not. Consider for example someone whose career is to research ant behavior. Their job in some sense is to be able to…
If they act out these predictions and are rewarded based on their accuracy, then yes. They're being trained to be like ants. Not entirely like ants in every way, but like them in specific ways.
There's a big difference with your analogy. Predicting tokens is essentially the same as generating tokens. There's no meaningful objective difference between the activities (I'm ignoring philosophy and focusing on observables). They both lead to a stream of tokens.
For contrast, consider any sport, maybe baseball. I could predict the winner of a game but not be able to win it myself. I could predict the next pitch but not be able throw it or hit it. There's an execution aspect you can fail at. Being like an ant would also have this aspect. Token prediction doesn't have this, or if it does (maybe turning a vector into an API response?) it's a trivial part of the whole problem.
Maybe I'd be more clear to say "write like humans" instead of "be like humans", though.
Earlier quoted context omitted.
But we can devise generally applicable rules of reasoning from first principles. It's called logic. I am pretty sure the next step is to properly combine machine learning and logic properly.
Seems unlikely, that never worked in the past. And humans don't actually use logic (especially formal logic) to come up with anything. They just use it to justify what they came up with. Not even mathematicians think in terms of logic when trying to solve problems.
Earlier quoted context omitted.
Integrating women into the workforce is not even close to abolishing private capital and the need to work. The violence would be between billionaires with infinite automation making infinite money, and common folk with no way to eat.
What you're responding to doesn't propose the abolition of obligatory work or private capital, it proposes a decrease in labor hours commensurate with, or conservative in comparison to, an expected increase in productivity
Earlier quoted context omitted.
I think "high quality, well-prepared sources" would include blogs and articles, which are likely to become heavily influenced by AI (blogs and articles are high quality compared to Reddit posts for example, which were included in the past). In fact, there's no reason to think that academic papers won't start using language models to write better. Tainting your text with AI can be as simple as pasting a paragraph in a…
I honestly don’t understand why “tainting” is such a big deal. Can someone explain it to me? I see two possible reasons, but neither seems to be worth the purity concern. The first is that AI can be wrong, make stuff up, be confidently incorrect. Anyone who has been on the internet knows this isn’t exactly a game changer. Second is that we won’t be training AI to be like humans, but like humans + AI. Also doesn’t see…
In the same way, AI is trying to generate text that looks like its training data, but if its training data is AI generated text then it's simply being taught to be more like itself. It slowly starts to work less like a human and more like whatever its own idiosyncrasies are. It's a larger sort of version of the hallucinations it has today. If 50% of all the text on the internet becomes some part AI generated, then a huge part of the training for the next generation of AI will be the shortcomings of the current iteration of AI. And this will get worse as non-AI content moves to exclude itself from training.