18 ms·

Before the super bowl, I asked "Who won the superbowl?" and it told me the winner was the Philadelphia Eagles, who defeated the Kansas City Chiefs by 31-24 on F

• c-fe 4y ago
  Out of interest, what did the source used as reference for the 31-24 say exactly? Was it a prediction website and Bing thought it was the actual result, or did the source not mention these numbers at all.
  • googlryas 4y ago
    Giants beat the Vikings about a month ago with that score.
• spaniard89277 4y ago
  I've tried perplexity.ai a bunch of times and I'd say I haven't seen any query wrong, although it's true I always look for technical info or translations, so my sample is not the same. And the UI is better IMO.
  • leereeves 4y ago
    I just tried a similar query on perplexity.ai. "Who won the Daytona 500 in 2023?" (the race is scheduled for February 19th) Result: "Sterling Marlin won the 2023 Daytona 500, driving the No. 4 for Morgan-McClure Motorsports[1]. He led a race-high 105 laps and won his second career race at Daytona International Speedway[1]. The 64th running of the DAYTONA 500 was held on February 19, 2023[2]. Austin Cindric had previously won the DAYTONA 500 in February 5, 2023[3]."
    • kahnclusions 4y ago
      Ahah! A time travelling AI!
    • echelon 4y ago
      Place your bets now. The AI might have clairvoyance and be showing off.
    • 6510 4y ago
      The temporal mechanics is fascinating.
    • nickpeterson 4y ago
      It may be more profitable to ask what stocks gained the most in value next week.
    • deadmik3 4y ago
      Wow, a driver that’s been retired for 13 years won for a team that shut down 10 years ago in the first ever season that Nascar has decided to run 2 Daytona 500s in the same month.
  • astrange 4y ago
    LLMs are incapable of telling the truth. There's almost no way they could develop one that only responds correctly like that. It'd have to be a fundamentally different technology.
    • CommieBobDole 4y ago
      Yep, the idea of truth or falsity is not part of the design, and if it was part of the design, it would be a different and vastly (like, many orders of magnitude) more complicated thing. If, based on the training data, the most statistically likely series of words for a given prompt is the correct answer, it will give correct answers. Otherwise it will give incorrect answers. What it can never do is know the difference between the two.
      • astrange 4y ago
        > If, based on the training data, the most statistically likely series of words for a given prompt is the correct answer, it will give correct answers. ChatGPT does not work this way. It wasn't trained to produce "statistically likely" output, it was trained for highly rated by humans output.
        • mrtranscendence 4y ago
          Not exactly. ChatGPT was absolutely trained to produce statistically likely output, it just had an extra training step added for human ratings. If they relied entirely on human ratings there would not have been sufficient data to train the model.
          • astrange 4y ago
            The last step is what matters. "Statistically likely" is very underdetermined anyway, answering everything with "e" is statistically likely. (That's why original GPT3 is known for constantly ending up in infinite loops.)
            • 4y ago
  • hristov 4y ago
    I tried perplexity.ai and asked it in which stadium did the chargers have their perfect season. It couldn't figure out that the chargers used to be the san diego chargers before they moved to LA and kept talking about their Los Angeles stadium even though they have never had a perfect season there.
  • egillie 4y ago
    I really like perplexity, but I've noticed that it sometimes summarizes the paper incorrectly, as in it cites it as concluding the opposite of what it actually concludes, so I always click through to read the papers/studies. It's great for surfacing relevant studies, though.
  • jacooper 4y ago
    I have seen it multiple times answering correctly at first, then adding something which has nothing to do with the original question. That's almost always sourced from a website that didn't actually answer the question I had, so maybe its more of a query optimization issue.
  • bmelton 4y ago
    I just checked it with a random query while watching Buffy the Vampire Slayer reruns, and it wasn't great. (Spoiler alerts) "How did Buffy the Vampire Slayer die?" "Buffy the Vampire Slayer died in the Season One finale when she threw herself into a portal to save her sister Dawn[1]. This was the first time Buffy had died, occurring on June 2, 1997[2]. She died again in the Season Five finale while fighting off Glory's demons[3]. Other characters such as Anya and Kevin Benedict also died during the series[4][5]." Okay, so it was a trick question, because Buffy definitely died more than once, but it's conflated the fact that she died in Season 1 with the cause of her Season 5 death. Also, I had to Google Kevin Benedict to remember that he was Cordelia's sometimes boyfriend, and an extremely minor character, which makes me question how that death is more notable than Buffy's mom, or Tara, or Jenny Calendar, etc. I like that this seems to have been more lexical confusion than how ChatGPT seems to enjoy filling empty spaces with abject lies, but perhaps it's worth exploring what you're asking it that has left it with such a great batting average?
  • mrtranscendence 4y ago
    Maybe for your use cases. I've found perplexity.ai wrong a few times just today: * Misunderstanding one of its citations, it said that use of `ParamSpec` in Python would always raise a warning in Python 3.9 * When asked why some types of paper adhere to my skin if I press my hand against them for a few minutes (particularly glossy paper), it gave two completely different answers depending on how the question was worded, one of which doesn't necessarily make sense.
    • aworks 4y ago
      That's a good point about use cases. In my usage of ChatGPT, in areas I'm very knowledgable. I've mostly received answers that were stylistically excellent, creatively plausable and maybe even transcendent. The boilerplate around the answer tends to keep the answers grounded, though. In areas where I have some experience but not much theoretical knowledge, after multiple exploratory questions, I better understand the topic and feel ok adjusting my behavior appropriately. I haven't relied on it in areas where I am ignorant or naive e.g. knitting, discriminatory housing policy or the economy in Sudan. Since I have no priors in those areas, I may not feel strongly about the results whether they are profound or hallucinatory or benign. I also haven't used it for fact checking or discovery.
• dionysus_jon 4y ago
  Why would you have that expectation?
  • somenameforme 4y ago
    Because they're being marketed as a tool, and not as a substantially overengineered implementation of MadLibs.
  • 2bitencryption 4y ago
    Imagine you are autocorrect, trying to find the most "correct sounding" answer to a the question "Who won the super bowl?" What sounds more "correct" (i.e. what matches your training data better): A: "Sorry, I can't answer that because that event has not happened yet." B: "Team X won with Y points on the Nth of February 2023" Probably B. Which is one major problem with these models. They're great at repeating common patterns and updating those patterns with correct info. But not so great if you ask a question that has a common response pattern, but the true answer to your question does not follow that pattern.
    • croes 4y ago
      Does ChatGPT say, I don't know?
      • avereveard 4y ago
        on some topic at least it correctly identify bogus questions. I extensively tried to ask abount non existent apollo missions for example, including Apollo 3.3141952, Apollo -1, Apollo 68, and loaded question like when Apollo 7 landed on the moon, and was correctly pointing out impossible combinations. this is a well researched topic tho.
      • l33t233372 4y ago
        Only if it’s a likely response or if it’s a canned response. Remember that ChatGPT is a statistical model that attempts to determine the most likely response following a given prompt.
• nimithryn 4y ago
  It has the Super Bowl numbers wrong, too. The last Super Bowl is LVI, which was Rams vs Bengals… the Super Bowl before that one was Tampa Bay Buccaneers vs Kansas City. it has every fact wrong but in the most confusing way possible…
  • kranke155 4y ago
    But the HN chatter was convinced that GPT would dethrone Google! Google has no chance!! Another silly tech prediction brought to you by the HN hivemind.
    • wizofaus 4y ago
      A little premature to be calling such a prediction "silly". I think it's safe to assume some sort of LLM-based tech will be part of the most successful search engines within a relatively short period of time (a year tops). And if Google dallies its market share will definitely suffer.
      • pms 4y ago
        Written by HN hivemind.
      • kranke155 4y ago
        Based on what evidence are you assuming that we can teach LLM to distinguish between truth and fiction.
        • wizofaus 4y ago
          Do you think Google's search engine can do that now? Actually ChatGPT does appear to have the ability to distinguish the two, the big problem it has is presenting made-up information as though it were factual.
• valine 4y ago
  How it should work is the model should be pre-trained to interact with the bing backend and make targeted search queries as it sees fit. I wouldn’t put it past Microsoft to do something stupid like ground gpt3.5 with the top three bing results of the input query. That would explain the poor results perfectly.
  • daveguy 4y ago
    That would require function and intelligence far outside the bounds of current large language models. These are models. By definition they can't do anything. They can just regurgitate the best sounding series of tokens. They're brilliant at that and LLMs will be a part of intelligence, but it's not anywhere near intelligent on its own. It's like attributing intelligence to a hand.
    • withinboredom 4y ago
      The model can be trained to output tokens that can intercepted by the backend before returning to the user. Also, the model can take metadata inputs that the user never sees.
      • daveguy 4y ago
        Yes. It is possible to do additional things with the model outputs or have additional prompt inputs... That is irrelevant to the fact that the intelligence -- the "trained" part -- is a fixed model. The way in which inputs and outputs are additionally processed and monitored would have completely different intelligence characteristics to the original model. They are, by definition of inputs and outputs, separate. Models of models and interacting models is a fascinating research topic, but it is nowhere near as capable as LLMs are at generating plausible token sequences.
    • valine 4y ago
      Except it’s already been shown LLMs can do exactly that. You can prime the model to insert something like ${API CALL HERE} into its output. Then it’s just a matter of finding that string and calling the api. Toolformer does something really neat where they make the API call during training and compare next word probability of the API result with the generated result. This allows the model learn when to make API calls in a self supervised way. https://arxiv.org/abs/2302.04761 https://arxiv.org/abs/2302.04761
• soco 4y ago
  I asked myself, why would ask somebody an AI trained on previous data, about events in the future? Of course you did it for fun, but on further thinking, as AI is sold as search engine as well, people will do that routinely then live with the bogus "search results". Alternate truth was so yesterday, welcome to alternate reality where b$ doesn't even have a political agenda.
  • inanutshellus 4y ago
    "Time to generate a bunch of b$ websites stating falsehoods and make sure these AI bots are seeded with it." ~Bad guys everywhere
    • tomxor 4y ago
      Because I loathe captcha, I make sure that every time I am presented one I sneak in an incorrect answer just to fuck with the model I'm training for free. Garbage in, garbage out.
      • A_non_e-moose 4y ago
        Glad to see a kindred soul out there. I thought I was the only one :)
        • breppp 4y ago
          Generalizing over the same idea, I believe that whenever you are asked for information about yourself you should volunteer wrong information. Female instead of male, single instead of married etc. Resistance through differential privacy
          • Eupraxias 4y ago
            I've lived in 90210 since webforms started asking.
            • 4y ago
• mitthrowaway2 4y ago
  I treat GPT as I would a fiction writer. The factual content correlates to reality only as closely as a fiction author would go in attempt to suspend disbelief. This answer is about as convincing, apt, well-researched and factually accurate as I would expect to find in a dialogue out of a paperback novel published five years ago. I wouldn't expect it to be any better or worse at answering who won the 2023 Quidditch Cup or the 2023 Calvinball Grand Finals.
  • Aeolun 4y ago
    The only reasonable use case for ChatGPT now is if you already know what the output should be (e.g. you are in a position to judge correctness).
    • Rustwerks 4y ago
      Super grammar checker.
• __MatrixMan__ 4y ago
  I think we need to work on what constitutes a citation. Your browser should know whether: - you explicitly trust the author of the cited source - a chain of transitive trust exists from you to that author - no such path exists ...and render the citation accordingly (e.g. in different colors)
  • mcguire 4y ago
    And that the cited document actually exists and says what it's claimed to say.
    • __MatrixMan__ 4y ago
      Agreed. Existence is easy, just filter untrusted citations. Presumably authors you trust won't let AI's use their keys to sign nonsense. Claim portability is harder but I think we'd get a lot out of a system where the citation connects the sentence (or datum) in the referenced article to the point where it's relevant in the referring article so that is easier for a human to check relevance.
  • deleted 4y ago
    [deleted]
• Alex3917 4y ago
  At least that's relatively innocuous. I asked it how to identify a species of edible mushroom, and it gave me some of the characteristics from its deadly look alike.
  • theK 4y ago
    I'd say the critical question here would be whether these characteristics can also be found on the edible mushroom or if it wanted to outright poison you :-D
    • Alex3917 4y ago
      > I'd say the critical question here would be whether these characteristics can also be found on the edible mushroom That's a non-trivial question to answer because mushrooms from the same species can look very different based on the environmental conditions. But in this case it was giving me identifying characteristics that are not typical for the mushroom in question, but rather are typical for the deadly Galerina, likely because they are frequently mentioned together. (Since, you know, it's important to know what the deadly look alikes are for any given mushroom.)
  • skissane 4y ago
    I asked OpenAI’s ChatGPT some technical questions about Australian drug laws, like what schedule common ADHD medications were on - and it answered them correctly. Then I asked it the same question about LSD - and it told me that LSD was a completely legal drug in Australia - which is 100% wrong. Sooner or later, someone’s going to try that as a defence - “but your honour, ChatGPT told me it was legal…”
    • Spivak 4y ago
      Y’all are using this tool very wrong and in a way that none of the AI integrated search engines will. You assume the AI doesn’t know anything about the query, provide it the knowledge from the search index and ask it to synthesize it. That seed data is where the citations come from.
• scarface74 4y ago
  And this doesn’t seem like it’s a hard problem to solve 1. Recognize that the user is asking about sports scores. This is something that your average dumb assistant can do. 2. Create an “intent” with a well formatted defined structure. If ChatGPT can take my requirements and spit out working Python code, how hard could this be? 3. Delegate the information to another module that can call an existing API just like Siri , Alexa, or Google Assistant Btw, when I asked Siri, “who won the Super Bowl in 2024”, it replied that “there are no Super Bowls in 2024” and quoted the score from last night and said who won “in 2023”.
• ska 4y ago
  >> "I would've expected it to not get such a basic query so wrong." Isn't this exactly what you would expect, with even a uperficial understanding of what "AI" actually is? Or were you pointing out that the average person, using a "search" engine that is actually at core a transformer model doesn't' a) understand that it isn't really a search and b) have even the superficial understanding of what that means, and therefore would be surprised by this?
• senectus1 4y ago
  hmm interesting. back when chatGPT was new I asked it what the most current version of PSADT (Powershell App Deployment Toolkit) was. It told me that its model was old but it thought 3.6 was the most current version. I then told it that 3.9 was the most current version. I then started a new chat and asked it the same question again. It told me its model was old but version 8 was the most current version! (there has never been a version 8 of PSADT) I asked that question again today. It has now told me to go check github cause its model is too old to know.
• josephg 4y ago
  Seems like we should be able to train the AI to output not just text, but the text along with a confidence score. During training, instead of rms(error) or whatever, you could use error * square(confidence). So the AI would be “punished” a lot more for being confident about incorrect responses. The confidence could also be exposed during inference. “Philadelphia eagles won the Super Bowl, but we’re only 2% confident of that”.
  • salawat 4y ago
    ...Why? An idiot will tell you the wrong answer with 100% confidence, and 0 valid basis to warrant said confidence. I swear, Babbage must be rolling in his grave. Turns out the person asking him about putting garbage in and getting a useful answer out was just an ahead of their time Machine Learning Evangelist!
    • josephg 4y ago
      If the learning rate was scaled based on its own confidence output, then ideally the system should learn to have a low confidence in everything early in training, and only raise its confidence late in training as it gets increasingly confident in its own outputs.
      • naijaboiler 4y ago
        LLM would have to fundamentally change. Current transformer architecture can not but hallucinate. It is inherent. More training won't solve it. A complete rethinking of the approach is the only way to
    • nerdponx 4y ago
      Much of the field of statistics is dedicated to estimating probability distributions from data. It's absolutely a legitimate question to ask.
  • SomewhatLikely 4y ago
    I've thought about this before but I think you have normalize the confidence over lots of examples for it to not lead to a degenerate solution. So if the confidence is divided by the sum of confidence for the whole minibatch the model would have an incentive to spread this out correctly rather than just always hedging.