14 ms·

Being a scifi geek and AI geek and neuroscience geek for pretty much the past 40 years, I've read countless predictions and scenarios and stories about society'

• famouswaffles 4y ago
  It's interesting for sure. There's also the fact that he didn't use the sota (GPT-4). I'm not saying that would be perfect. But i would be surprised if it wasn't big leap over 3.
  • weaksauce 4y ago
    it wasn't mentioned anywhere in there which version of the model he was using... how are you so sure?
    • famouswaffles 4y ago
      he references 3 when he talks about passing the exam. and i guess i doubt he'd evaluate 4 and not say anything about it.
      • weaksauce 4y ago
        they didn't specifically mention 3 in the linked article or in the linked linked article and they ran both 3 and 4 against those exams so I'm not sure either way. probably was 3 but I can't be certain.
• Maursault 4y ago
  > as "true" AI begins to emerge What, exactly, is that?
  • m3kw9 4y ago
    Meant AGI
  • fzzzy 4y ago
    The definition has always been the Turing test. Nobody has been able to provide a more precise answer.
    • barking_biscuit 4y ago
      I think it's kind of silly. The real metrics we will evaluate AI progress by is economic impact first, followed by political impact.
    • Maursault 4y ago
      Then "true" AI "began to emerge" more than 20 years ago.[1] [1] https://en.wikipedia.org/wiki/Eugene_Goostman https://en.wikipedia.org/wiki/Eugene_Goostman
    • dekhn 4y ago
      I think an updated version of the turing test needs to be done: combine a generative language model with a generative face model and speech model to create an interactive avatar that can converse with an individual. The individual is allowed to ask any and all questions of a series of avatars (50% of the avatars are actually humans) and judge whether they are Human or Not Human. If a particular avatar model is able to fool a representative sample population into identifying avatar models from humans at a similar rate, then it passes the test. This still doesn't really move the needle on any of the important questions about AI, but does hasten the public perception that proving self-awareness, consciousness, or agency exists in humans without depending on subjective experience is probably impossible.
  • ftxbro 4y ago
    "true" AI is one whose cognition is more advanced than that of existing AI
• asdff 4y ago
  But at the same time consider the problem, and if AI is even the tool for this job or if its total overkill. We are just matching symptoms to a diagnosis, you could get this utility from programming a simple dictionary with a fraction of the compute. Maybe even with a confidence estimate of the diagnosis this time. There's an old adage that rings true here I think: "keep it simple, stupid."
  • messe 4y ago
    But I think that's missing the "magic" of it—for lack of a better word. In the situation you describe, you have to build a specific tool for the task. With LLMs, we appear to have stumbled on a general purpose tool for a myriad of situations. Yes, they're not perfect; but the fact that they work remotely as well as they do even at these early stages is astonishing.
    • asdff 4y ago
      You have to build a specific tool, yes, but now you know the tool is good and its output reflects the input that you can see right in the source code, and you can use it the next time without rebuilding it. On the other hand with chatgpt, you have to build a specific prompt, and you don't know if that will lead to a consistent result or what its even pulling from. It introduces uncertainty that you cannot reliably estimate, whereas with the above toolmaking example, you can estimate your uncertainty, you can calculate how much power you have and how much you should have to resolve what you expect, and you can calculate a false positive rate. When decisions mean money or life, I wouldn't rely on the black box chatbot. I would take a little bit more time and make sure all i's are dotted and all t's crossed.
  • rootusrootus 4y ago
    AFAIK that's essentially what we already have. Doctors don't have the mental capacity to keep every little thing in their head. Go ask your GP a question, and when you see him typing on a screen, it's because he's asking the professional equivalent of WebMD to correlate symptoms and for him.
  • nradov 4y ago
• jacquesm 4y ago
  Half the time right is worse than nothing at all.
  • messe 4y ago
    Not when there are a lot more possible wrong answers than right answers.
    • jacquesm 4y ago
      That doesn't follow. 2+2 has more wrong answers than right answers, and yet, if you gave me the wrong answer to arithmetic sums half the time I'd think you were a lousy calculator. Worse still if those answers had the potential to kill me.
  • jay_kyburz 4y ago
    Yes, but we are not comparing half time right to nothing, we are comparing how often AI right to how often your local human right. (not the smartest expert human, the human you have access to)
    • jacquesm 4y ago
      And you believe your local human aka the doctor is right only half the time or even worse?
      • kelnos 4y ago
        I think the parent believes the opposite.
  • PeterisP 4y ago
    It depends, half the time right is very useful in scenarios where you can quickly verify the correctness - i.e. with troubleshooting suggestions or code which you can run and see if it does what you needed.
• civilized 4y ago
  What I didn't predict is so many AI boosters being so miffed at regular folks pointing out the obvious: a half-working system may be tremendously exciting to AI enthusiasts, but it's not that useful to society. Recently we've had all these people complaining of moving goalposts, when in fact most people never had goalposts for AI in the first place, and certainly never had goalposts that remotely resembled those that AI experts use to evaluate their own progress. My mental model seems to clash with that of the boosters. They appear to have little doubt that reliability will emerge from scaling the models up. I think reliability is the lion's share of the problem and probably won't be solved by current architectures. It will be interesting to see who is right.
  • gerdesj 4y ago
    It's a bloody calculator and a damn good one (but it can and does go mad). Use it like that and you'll be fine. Have you actually engaged with it in any way? Its free to sign up. By the way, what on earth is an AI booster? Do you really mean "enthusiast"? "My mental model seems to clash with that of the boosters." Please tell us about your mental model, we can't read your mind.
    • Turskarama 4y ago
      It is very nearly the opposite to a calculator. A calculator gives you exact results for a very small subset of problems (equations). Chat-GPT will give you approximate (and occasionally incorrect) solutions to an extremely wide range of problems. The main problem with AI is that there are only so many problems where an approximate solution is fine, and an incorrect solution is easy to work around. In order for AI to be broadly useful, it has to get to the point where it's correct more often than an expert human. It may well get there within the next couple of years, but this isn't a space LLMs are designed to be able to solve so it's not as guaranteed as a lot of people seem to think.
      • MVissers 4y ago
        Disagree. For AI to be useful it needs to be better at the job than how the job currently being done. We humans rely on thousands of things where an approximate solution is fine. Especially when it's about interacting with other humans. Programming is an exception, but what % of the population are programmers and engineers?
• dekhn 4y ago
  Ditto- I grew up reading Vernor Vinge, William Gibson, and John Varley and dedicated my career to applying ML to biological research problems while waiting for technology to reach the point where it could reliably be used to do some of the things that can be done in those books, biotechnologically (e.g. gene therapy for radical body modification). I was very disappointed to learn when I first started (around '93-94) that neural networks were hard to design, and impossible to train more than a couple layers deep, and the labelled data wasn't there anyway. None of that really changed visibly for a couple decades, but multiple unrelated research projects made the necessary breakthroughs: straightforward NN models (CNNs, etc) that people could adapt, frameworks to train and predict without having to implement the entire ML stack from scratch, absurdly fast computers and acccelerator hardware, and massive labelled data sets. So far, nothing I've seen is anywhere close to what I consider "true" AI. As we struggle with hard medical problems. But seeing computers handle text with such facility has been exciting because it's the sort of thing that normies can see and appreciate (and even be fooled by). However, medical problems aren't something that ML is going to magically solve. Because medicine has huge structural issues that are harder to solve than making diagnoses. It would be nice if James gleick wrote a new version of "What just happened" covering the time between the invention of Transformers and now.
• gerdesj 4y ago
  Jolly hockey sticks but we have to be rather careful with these things. I've had it devise multiple computer programs from OpenSCAD to BASH and Python because I can get a real feel for what it is really doing, given that I am an IT bod. I'm also a Civil Engineering grad and it really cocked up when I asked a question about normal beam strength and I suspect it will get the pressure at depth thing wrong too. Both of these are related to depth only - one dimensional. That's why we have I beams - the flanges are to resist twisting and buckling but the strength is in the depth. ChatGPT is a tool. It is basically a really good calculator. I do find myself saying please in my requests.
  • kelnos 4y ago
    A really bad calculator, IMO. A calculator gives you always-correct answers to specific, well-formed questions in a relatively small domain (math). ChatGPT gives you approximate and sometimes-wrong answers to arbitrary questions in nearly any domain.
• AH4oFVbPT4f8 4y ago
  I have case that 5 doctors so far can't explain. I fed in what was happening (generally speaking) and it provided a rather good list of what it "might" be. I then asked it to start describing each scenario and I was able to determine nope, that's not it or if it was plausible. Meeting with doctor no 6 tomorrow to talk about what it could be but at least I have a better insight at what the more obvious possibilities. Side note, my ER doctor wrote me a referral to neurologist to who was considered the best in the state and they refused to even talk to me since the referral said car accident. They wouldn't budge and wouldn't reconsider. The car accident was a result of an episode, not the cause of my issues. I hate the US healthcare system.
  • maxbond 4y ago
    That's awful. I hope that gets worked out and you get the care you need. The thing is, if the healthcare system is shitty and broken, it doesn't necessarily matter how good ChatGPT is. If it's applied with the intent of "streamlining" the medical system by ignoring people's concerns and rejecting their insurance claims in an automated fashion, it might be much harder to even get in front of a doctor. You could fiddle with your dosage, work with your doctor, etc, and then your insurance company can say, "sorry, ChatGPT has decided that you can do with a lower dosage, so we won't be paying for it. The machine is a black box, so it's not accountable to you or us or anyone else, so you just have to accept it's verdict." No amount of training data, no architecture, heck no alignment or safety work solves that problem. Because technology doesn't solve social problems.
  • petilon 4y ago
    How does it matter to the doctor if the car accident was the cause of your issues?
    • trogdor 4y ago
      I think some doctors don’t want to get involved with patients who are involved in litigation regarding the reason for their visit.
      • 4y ago
• Genbox 4y ago
  I've noticed a very weird trend when trying to incorporate GPT4 into my workflow. I lead a small team of developers and security consultants, and whenever someone runs into a problem, it is immediately searched for on the web. However, there is a real resistance/reluctance in using GPT for the same task. I think it is a combination of factors. The wrong answers are part of it, but I think another part is the anthropomorphization of it makes it almost like asking another human for help, which is somehow 'less good' than asking a search engine. For better or worse, LLMs are here to stay.
  • tomlagier 4y ago
    For me, the reason I prefer Google to GPT is that it's much easier for me to assess the credibility of a Google answer vs a GPT one. There are so many signals in any primary source. Some obvious ones are things like number of upvotes, site reputability, presence of (working) examples, when was the answer written? More intangible things are like how closely does this solution match my problem statement, does the author write in a trustworthy manner are also easy for me to pick up at a glance. With GPT, I don't have any of that (or maybe I just need to re-learn it?) Also, I get a useful answer from most Google queries. GPT performs at a significantly lower bar (at least right now) - it works well for some stuff but not others, and the time it takes to figure out whether it's going to do a good job (and maybe do a couple of rounds of prompt refinement) is much more than just Googling.
    • danielrpa 4y ago
      Bing Chat gives you references.
      • botanical 4y ago
        Except what it says isn't backed up by the references. It makes things up.
  • jhanschoo 4y ago
    For me it's because I've been trained by decades of searching to type in only a few words to obtain a presumably relevant answer, whereas I think I have to write a mini-essay to ChatGPT to get an answer. But when I acquiesce to explaining to ChatGPT my information need, I get a more useful answer. (though frequently inaccurate in details)
• nradov 4y ago
  You may not have been paying attention to the medical literature but we have had clinical decision support tools including automated diagnostics for literally decades that achieve at least 50% accuracy. That didn't require LLMs, and LLMs aren't "true" AGI. Those existing CDS tools aren't widely used because the data entry is cumbersome and slows down clinician workflow. So LLMs may offer some opportunity there.
• jschveibinz 4y ago
  This is spot on. I keep thinking that it has to do with our collective ability in both recognizing change and accepting change. I commented a couple of days ago that we feel that the slope of change is relatively flat. As if the current snapshot of AI capabilities is somehow the way it’s going to be for awhile. But in fact, the slope of technology change is almost vertical. A year or two from now, AI will be a very (perhaps radically) different capability unless something is done to interrupt its development.
• beezlewax 4y ago
  I like your take on this but isnt it overly optimistic? I have the feeling that this tech will hit the a wall pretty quickly like driverless car technology. It looks great on the surface but it isn't anything like the kind of sci-fi ai I think we're aiming for. Not in anything but a superficial way at least. It doesnt know when its wrong and just straight up lies constantly inventing all sorts of fantastical stuff. I asked it for history of a small local area in Ireland and it created a bunch of castles and tribes that never existed.
  • Izkata 4y ago
    > It looks great on the surface but it isn't anything like the kind of sci-fi ai I think we're aiming for. Not in anything but a superficial way at least. And then there's ChatGPT querying WolframAlpha: https://writings.stephenwolfram.com/2023/03/chatgpt-gets-its-wolfram-superpowers/ https://writings.stephenwolfram.com/2023/03/chatgpt-gets-its...
• pharmakom 4y ago
  I can't think of any science fiction where th AI is there but not at all reliable. Maybe evil, but not unreliable. It's a failure mode we weren't anticipating. It also might make it next to useless for some real world applications, despite being incredible technology.
  • kgabis 4y ago
    It's quite ironic that AI will be inherently human in it's unreliability.
  • python999 4y ago
    C3PO? Or maybe its challenge is more relevance and conciseness?
• Shorel 4y ago
  It makes predictions based on incomplete and or biased information. Just as doctors do. The funny thing is not that we are holding AI to a high standard. The funny thing is that we normally do not do the same for doctors, marketers, politicians, etc.
  • kelnos 4y ago
    Sure we do, for doctors at least. A doctor that got ER diagnoses wrong 50% of the time would get fired. (And the AI here didn't even get it right 50% of the time, just 50% of the time it presented the right answer as one of several options. If pressed, it may not have made the one single correct answer for each of those patients.)
• blibble 4y ago
  > This article and the comments in this thread are right up that alley. I mean, can you imagine say 1 or 2 years ago saying we'd have a readily accessible system that you could feed it the symptoms a patient is experiencing (in any language!) and out would split a well-described explanation of the diagnosis (in any language!) around half the time? we've had this for years (bar the "any language") they're traditional expert systems with the same flowchart a GP (family doctor) would use to ask questions and they do a lot better than 50%
• apwheele 4y ago
  I think people are more impressed with the "well-described explanation", which is a (mostly bullshit) interpolation from prior text on the internet. If you just focus on the "feed a narrative and get out a probability of a particular diagnosis" -- the ability to do that with other machine learning models has already existed. If you give me a sample of 100k ER text records and final diagnoses, I would be pretty confident I can estimate a model that gets you +50% accuracy on my laptop using open source tools.
  • MrPatan 4y ago
    What percent of human doctors diagnoses is also well-decribed bullshit, though?
    • apwheele 4y ago
      Totally get the point -- in the ER it is a mix of narrative and tests, so hopefully not so many. But in general practice, probably more BS than any of us would like. Part of the difficulty with healthcare (that I myself don't have an easy answer to), is that you have very asymmetric error costs. So chest pain, even if heart attack is low probability, you should be risk adverse in many scenarios and still do due diligence to eliminate that as a possibility. Not saying that smart systems are not possible, just still skeptical that level of complexity is possible on the backbones of LLMs. The smart sounding narratives I am concerned are mostly red herrings though in terms of outcomes we care about, accuracy/making right decisions/etc.
• b112 4y ago
  But here we are! Yet in the end, the correct answers are likely identical to webmd or mayo, and the wrong to reddit or twitter, so all we have is a fancy google search. So far, all the magic I see is the prompts. The knowledge is just random junk, the validation is just "how many internet pages said it", so you can never train new models again, because you now are training on gpt output.
• mannykannot 4y ago
  The responses are bounded by two wings: those who can't see the successes and those who can't see the problems. The author of this article seems to me to be doing a good job of navigating between them.
• lerchmo 4y ago
  Watch the late night shows making fun of (the internet) in the 90’s. This is just the next round of that in my opinion.