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actually, are we sure they will improve, if there is emergent unpredicted behaviour in the SOTA models we see now, then how can we predict if what emerges from
by tudorw 3y ago
actually, are we sure they will improve, if there is emergent unpredicted behaviour in the SOTA models we see now, then how can we predict if what emerges from larger models will actually be better, it might have more detailed hallucinations, maybe it will develop its own version of cognitive biases or inattentional blindness...
- dcow 3y agoHow do we know the sun will rise tomorrow?
- muh_gradle 3y agoPoor comparison
- dcow 3y agoNo so! Either both the comments are meaningful, or both are meaningless.
- jchw 3y agoI don't understand why that is necessarily true.
- dcow 3y agoBecause they are both statements about the future. Either humans can inductively reason about future events in a meaningful way, or they can’t. So both statements are equally meaningful in a logical sense. (Hume) Models have been improving. By induction they’ll continue until we see them stop. There is no prevailing understanding of models that lets us predict a parameter and/or training set size after which they’ll plateau. So arguing “how do we know they’ll get better” is the same as arguing “how do we know the sun will rise tomorrow”… We don’t, technically, but experience shows it’s the likely outcome.
- jchw 3y agoIt's comparing the outcome that a thing that has never happened before will (no specified time frame), versus the outcome that a thing that has happened billions of times will suddenly not happen (tomorrow). The interesting thing is, we know for sure the sun will eventually die. We do not know at all that LLMs will ever stop hallucinating to a meaningful degree. It could very well be that the paradigm of LLMs just isn't enough.
- dcow 3y agoWhat? LLMs have been improving for years and years as we’ve been researching and iterating on them. “Obviously they’ll improve” does not require “solving the hallucination problem”. Humans hallucinate too, and we’re deemed good enough.
- jdiff 3y agoHumans hallucinate far less readily than any LLM. And "years and years" of improvement have made no change whatsoever to their hallucinatory habits. Inductively, I see no reason to believe why years and years of further improvements would make a dent in LLM hallucination, either.
- ripe 3y agoAs my boss used to say, "well, now you're being logical." The LLM true believers have decided that (a) hallucinations will eventually go away as these models improve, it's just a matter of time; and (b) people who complain about hallucinations are setting the bar too high and ignoring the fact that humans themselves hallucinate too, so their complaints are not to be taken seriously. In other words, logic is not going to win this argument. I don't know what will.
- dcow 3y agoI don’t know if it’s my fault or what but my “LLMs will obviously improve” comment is specifically not “llms will stop hallucinating”. I hate the AI fad (or maybe more annoyed with it) but I’ve seen enough to know these things are powerful and going to get better with all the money people are throwing at them. I mean you’d have to be willfully ignoring reality recently to not have been exposed to this stuff. What I think is actually happening is that some people innately have taken the stance that it’s impossible for an AI model to be useful if it ever hallucinates, and they probably always will hallucinate to some degree or under some conditions, ergo they will never be useful. End of story. I agree it’s stupid to try and inductively reason that AI models will stop hallucinating, but that was never actually my argument.
- zdragnar 3y agoWell, based on observations we know that the sun doesn't rise or set; the earth turns, and gravity and our position on the surface create the impression that the sun moves. There are two things that might change- the sun stops shining, or the earth stops moving. Of the known possible ways for either of those things to happen, we can fairly conclusively say neither will be an issue in our lifetimes. An asteroid coming out of the darkness of space and blowing a hole in the surface of the earth, kicking up such a dust cloud that we don't see the sun for years is a far more likely, if still statically improbable, scenario. LLMs, by design, create combinations of characters that are disconnected from the concept of True, False, Right or Wrong.
- pixl97 3y agoIs the function of human intelligence connected to true false right or wrong? These things are 'programmed' into you after you are born and from systematic steps.
- zdragnar 3y agoYes, actually. People may disagree on how to categorize things, but we are innately wired to develop these concepts. Erikson and Piaget are two examples of theorists in the field of child psychology who developed formalizations for emotional and mental stages of development. Understanding that a thing "is" is central to these developmental stages. A more classic example is Freud's deliniation between the id, ego and super-ego. Only the last is built upon imparted cultural mores; the id and ego are purely internal things. Disorders within the ego (excessive defense mechanisms) inhibit perception of what is true and false. Chatbots / llms don't consider any of these things; they consider only what is the most likely response to a given input?. The result may, by coincidence, happen to be true.
- seanhunter 3y agoAbsolutely not. One thing happens because of a set of physical laws that govern the universe. These laws were discovered due to a massive number of observations of multiple phenomena by a huge number of individuals over (literally) thousands of years, leading to a standard model that is broadly comprehensive and extremely robust in its predictions of millions or possibly even billions of seperate events daily. The other thing we have a small number of observations of happening over the last 50 or 60 years but mostly the last 5 years or so. We know some of the mathematical features of the phenomena we are observing but not all and there is a great deal going on that we don't understand (emergence in particular). The things we are seeing contradict most of the academic field of linquistics so we don't have a theoretical basis for them either outside of the maths. The maths (linear algebra) we understand well, but we don't really understand why this particular formulation works so well on language related problems. Probably the models will improve but we can't naively assume this will just continue. One very strong result we have seen time and time again is that there seems to be an exponential relationship between computation and trainingset size required and capability. So for every delta x increase we want in capability, we seem to pay (at least) x^n (n>1) in computation and training required. That says at some point increases in capability become infeasible unless much better architectures are discovered. It's not clear where that inflection point is.
- ysavir 3y agoOriginally: very few input toggles with little room for variation and with consistent results. These days: Modern technology allows us to monitor the location of the sun 24/7.
- TheMode 3y agoBecause it has been the case for billions of years, and we adapted our assumptions as such. We have no strong reason to believe that we will figure out ways to indefinitely improve these chat bots. It may, but it may also not, at that point you are just fantasizing.
- dcow 3y agoWe’ve seen models improve for years now too. How many iterations are required for one to inductively reason about the future?
- arcticbull 3y agoHow many days does it take before the turkey realizes it’s going to get its head cut off on its first thanksgiving? Less glibly I think models will follow the same sigmoid as everything else we’ve developed and at some point it’ll start to taper off and the amount of effort required to achieve better results becomes exponential. I look at these models as a lossy compression logarithm with elegant query and reconstruction. Think JPEG quality slider. The first 75% of the slider the quality is okay and the size barely changes, but small deltas yield big wins. And like an ML hallucination the JPEG decompressor doesn’t know what parts of the image it filled in vs got exactly right. But to get from 80% to 100% you basically need all the data from the input. There’s going to be a Shannon’s law type thing that quantifies this relationship in ML by someone who (not me) knows what they’re talking about. Maybe they already have? These models will get better yes but only when they have access to google and bing’s full actual web indices.
- drw85 3y agoI don't think access to bing or google solves this problem. Right now, there are many questions that the internet gives unclear answers to. Try to find out if a plant is toxic for cats via google. Many times the results say both yes and no and it's impossible to assume which one is true based on the count of the results. Feeding the models more garbage data will not make the results any better.
- tudorw 3y agoone day it won't...
- mrguyorama 3y agoBecause we understand newtons laws of motion and the sun and earth seem to, with extreme certainty, follow such laws, and by evaluating those laws forward into the future we see the orbits will continue such that the sun rises. It's not magic. What rules and predictions can reliably describe how much machine learning will advance over time?