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How does the next machine/LLM know what’s valid or not? I don’t really understand the idea behind layers of hallucinating LLMs.
by yunohn 2y ago
How does the next machine/LLM know what’s valid or not? I don’t really understand the idea behind layers of hallucinating LLMs.
- ben_w 2y agoBy comparison with reality. The initial LLMs had "reality" be "a training set of text", when ChatGPT came out everyone rapidly expanded into RLFH (reinforcement learning from human feedback), and now there's vision and text models the training and feedback is grounded on a much broader aspect of reality than just text.
- yunohn 2y agoCould you link to a paper or working POC that shows how this “turtles all the way down“ solution works?
- ben_w 2y agoI don't understand your question. This isn't turtles all the way down, it's grounded in real world data, and increasingly large varieties of it.
- croes 2y agoHow does the AI know it’s reality and not a fake image or text fed to the system?
- exe34 2y agoDoes the AI need to know or the curator of the dataset? If the curator took a camera and walked outside (or let a drone wander around for a while), do you believe this problem would still arise?
- ben_w 2y agoI refer you to Wachowski & Wachowski (1999)*, building on previous work including Descartes and A. J. Ayer. To whit: humans can't either, so that's an unreasonable question. More formally, the tripartite definition of knowledge is flawed, and everything you think you know has a Munchausen trilemma. * Genuinely part of my A-level in philosophy
- yunohn 2y agoI wasn’t expecting your response to be “the truth is unknowable”, but was hoping for something of more substance to discuss.
- ben_w 2y agoThen you need a more precisely framed question. 1. AI can do what we can do, in much the same way we can do it, because it's biologically inspired. Not a perfect copy, but close enough for the general case of this argument. 2. AI can't ever be perfect because of the same reasons we can't ever be perfect: it's impossible to become certain of anything in finite time and with finite examples. 3. AI can still reach higher performance in specific things than us — not everything, not yet — because the information processing speedup going from synapses to transistors is of the same order of magnitude as walking is to continental drift, so when there exists sufficient training data to overcome the inefficiency of the model, we can make models absorb approximately all of that information.
- croes 2y agoSo we get the same flaws as before with a higher power consumption. And because it’s fast and easy we now get more fakes, scams and disinformation. That makes AI a lose-lose not to mention further negative consequences.
- ben_w 2y agoNot if you source your training data from reality. Are you treating "the internet" as "reality" with this line of questions? The internet is the map, don't mistake the map for the territory — it's fine as a bootstrap but not the final result, just like it's OK for a human to research a topic by reading on Wikipedia but not to use it as the only source.
- croes 2y agoGiven that there are more and more AI generated texts and pictures that ground will be pretty unreliable.
- ben_w 2y agoPerhaps. But CCTV cameras and smartphones are huge sources of raw content of the real world. Unless you want to take the argument of Morpheus in The Marix and ask "what is real?"
- croes 2y agoSo let’s crank up total surveillance for better auto descriptions of a picture. We aren’t exchanging freedom for security anymore, what could be reasonable under certain conditions, we just get convenience. Bad deal.
- ben_w 2y agoThat's one way to do it, but overkill for this specific thing — self-driving cars or robotics, or natural use of smart-[phone|watch|glass|doorbell|fridge], likely sufficient. Total surveillance may be necessary for other reasons, like making sure organised crime can't blackmail anyone because the state already knows it all, but it's overkill for AI.