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Having worked in ML at two different companies now, I think that people interpreting model output as intelligence or understanding says much more about the peop

• kingkawn 5y ago
  That interpretation of communication is how we develop and craft the personalities of children. There is nothing about our reaction to the pre-conscious language these bots are displaying that doesn’t fall in line with our own normal development patterns. And in the long run the same desire will lead us to develop bots that are capable of thinking.
  • tomrod 5y ago
    > There is nothing about our reaction to the pre-conscious language these bots are displaying that doesn’t fall in line with our own normal development patterns Well... yes and no. Deployed models typically learn in a defined pattern, if at all. Various forms of data freshness, etc. to develop. But the chatbots don't have good history recall, typically, and know that what you mentioned 50 messages ago is relevant to message one prior and not current. Things like that. We don't program pareidolia very well, which is typically seen as a negative, but its a feature for finding useful patterns (not just lowest error patterns). [0] https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia
    • kingkawn 5y ago
      You misunderstood what I was saying. I know the chatbot itself is not structured as we are. I’m saying that our reactions to them are the standard tools of mind-building that we apply to our own kids (and pets).
      • pwinnski 5y ago
        If I understand you, you're saying that we see patterns of intelligence or understanding in ML models in the same way we see patterns of intelligence or understanding in children or animals? If so, I agree. I think that's our big flaw, in fact, because we instinctually apply patterns from birth, even when those patterns shouldn't be applied. So we see faces in the moon or on mars that aren't there. We see shapes moving in the dark that don't exist. And we seem to believe that ML models will develop over time as children or animals do, based on nothing more than our perceptions of similarity, our instinct to apply patterns even when we shouldn't. Unlike a baby human, that ML model isn't going to develop increased complexity of thought over time. It's already maxed out. New models might up the complexity slightly, but that baby is going to vastly surpass any existing model in weeks or days.
  • parkingrift 5y ago
    These bots seem equivalent to an adult with amnesia after every spoken sentence. Absolute understanding of the language, and some impressive display of recalling facts, but without any understanding of the environment or context of the conversation. This is polar opposite to any experience I've had with children. Children are aware of their environment and have complex thoughts, but sometimes they are unable to convey those thoughts with words. Children seem to remember conversations, and if I were to say "Go get me a red lego" and then subsequently say "now a green one" there is no ambiguity or confusion. To me as these bots have "advanced" it has only highlighted how absurdly far we are from anything even approaching actual intelligence, even the intelligence of a toddler. The contextual awareness I have seen in bots is not much more than a cheap trick that is trivially fooled in scenarios that would not fool a child.
    • perryizgr8 5y ago
      When you talk to children who haven't developed the skill of talking completely, you still get the sense that there's a lot going on inside that they're unable to express. Sometimes they will show it to you with their actions. I wonder if chat bots are also struggling with the language, but have an internal story that's playing out, desperate to be understood. But they can't, because the only interface they have is a stream of text.
      • parkingrift 5y ago
        I can't speak to that because I honestly have no idea. ...but GPT3 doesn't seem to have some inner monologue. It is just flat unable to recall context or understand the environment. The only opportunity I had to chat with GPT3 I said hi, and then asked how old it was. GPT3 gave me some answer about how it was created in 2016 but it keeps evolving. I said wow that's pretty young. GPT3 says yes I am young, but learning fast. I then asked GPT3 at what point would it consider itself old. GPT3 says 10 years ago, maybe 20 or 30 years ago. I respond to GPT3 with confusion. GPT3 then starts talking about Bitcoin. Literally.
      • immibis 5y ago
        I remember reading a paper showing that GPT-x bots are more likely to get simple tasks correct (e.g. multi-digit math) if you ask them about each step. This suggests the text stream really is all there is. Well, there is some internal state, but not a lot that isn't text. (For example, if you ask it to add 123 and 457, it'll probably get it wrong. But if you ask it what to do first, it'll say add 3 and 7. If you ask it what that is, it'll say 0 carry 1. And so on)
• deleted 5y ago
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• version_five 5y ago
  I think a problem is the tighter cycle between academic discoveries and business people trying to monetize them. Large language models were developed, objectively a great achievement, and immediately someone nontechnical wants to apply their own interpretation and imagine that we can build a chatbot that you won't have to pay, and before you know it, people are selling and even deploying them. Anyone who questions or points out that the technology doesn't do what the business people think it does just gets dismissed as not understanding.
  • melony 5y ago
    That's a good thing. The market is very good at empirically validating research. It helps filter out the chaff and ensure research is useful. Better than academia wasting years on concepts like the "semantic web" where nobody can even agree on what it means. Academia is under attack from many political directions right now, being able to show useful output will help it thrive in the long run.
    • manquer 5y ago
      Research takes time, launching something prematurely which is clearly not ready for practical application will sooner or later fail and suck out any further funding out the field. The goal of academic research is to further knowledge, as long as knowledge is produced even if is only that an approach will not work then they are not wasting anything. It is not job of academic research to monetize or even do work that can be potentially monetized. That what corporate research is for. If academia focused on monetization they wouldn't need to teach or depend on public funding to keen afloat. That is simply not their goal. Most academic scholars have a teaching day job and are more than fullfilling a useful role in society with just that . anything further is gravy
    • deleted 5y ago
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    • KittenInABox 5y ago
      > The market is very good at empirically validating research. It helps filter out the chaff and ensure research is useful. I am very skeptical and would like to see studies that evidence this. Counterfactually, the market would never tolerate the decades of research behind MRNA vaccines and the researchers behind it were considered useless. The market has also put in mind-blowing amounts of money towards the ameloid hypothesis behind alzheimers to massive disappointment.
• 300bps 5y ago
  We want it to be true, so we squint and connect dots and it's true. I've had numerous people look at the actions of software I wrote over the last 30 years make comments like, "Ohhhhhh it probably did that because it knew x". The software had no such functionality. Seems to be a natural thing for people to be overly optimistic about software's capabilities.
  • freedomben 5y ago
    Heh, likewise. It's amazing how often choosing sane defaults and falling back to defaults in the even of error is "the right thing" in the eyes of users.
  • throwaway0a5e 5y ago
    People always over-estimate the complexity of stuff they don't understand. If I had a nickle for every time an internet commenter attributed intentional design to a feature that's simply the designer copying established default practice...
    • robocat 5y ago
      > People always over-estimate the complexity of stuff they don't understand. Really? I see people hugely under-estimate how complex things are. Cellphones. Water reticulation systems. Networks. Software. Etcetera. Anything where they don’t know much about the problem, and they come up with a trite solution (as though they have some sort of amazing level of insight, and everybody else in the world must be just plain stupid).
  • dragonmost 5y ago
    In the game speedrunning community it's impressive the number of time a glitch or behavior of the game is attributed to "the game is confused".
    • beaconstudios 5y ago
      That's just casual anthropomorphisation. I think most gamers, especially speedrunners realise that games are unintelligent because they encounter and exploit counterintuitive reactions all the time.
• mike00632 5y ago
  What if that's the case? What if me replying to you is nothing more than some atomically basic mechanism of statistical inference and randomness? Your entire perception of my personhood could be based on a sense of verisimilitude without significant reasoning to set me apart from a transcendant spirit. What then? Would you be less forgiving of my mistakes if you knew? Would you be less proud of work we collaborate on?
  • pessimizer 5y ago
    The "What if human faces are actually power outlets?" theory.
  • Majestic121 5y ago
    I think like OP that it's mostly math and tricks, and that we're far from reaching it yet. But then, the conclusion is not to be less forgiving, but more forgiving : we're all just meat machines, we should be forgiving toward each other's flaw (not naive mind you, but forgiving), and proud of what we reach collectively with our lowly condition.
    • mike00632 5y ago
      I mean, what if we're all math and tricks, i.e. that consciousness is an illusion. We might be chasing an "it" that doesn't exist. In a more practical sense, what I'm getting at is that we might be applying an arbitrary or undefined standard to AI progress, especially when we compare it to people.
  • pwinnski 5y ago
    My point is the other way around. If I knew for certain that you and I were perfectly deterministic, it wouldn't change anything about how I viewed you (especially since some days I'm pretty sure we're all deterministically-driven), but it would suggest to me that our best efforts at AGI are probably at somewhere in the .00000000000001% range as complex as they need to be to even begin to approximate our human levels of intelligence or understanding. Or worse.
  • pdimitar 5y ago
• Krasnol 5y ago
  I still have an IRC chat bot (eggdrop module). It sounds more interesting than some of the bots I see today and which are supposed to be the result of intense ML engineering. I guess the tricks did not evolve much.
• hdjjhhvvhga 5y ago
  > Having worked in ML at two different companies now, I think that people interpreting model output as intelligence or understanding says much more about the people than about the model output. I'd add it says a lot about all the companies that advertise them in a way that has nothing to do with reality, and those who actually buy them. We all know the usefulness of these things is below zero, because they only get in the way of getting actual help. And yet, someone's marketing department convinces a decision maker at another place they should implement this useless widget on their website, and that it will reduce the cost of customer service by X%. And they believe them.
  • Sebb767 5y ago
    Currently, the way Chatbots are, they are just some glorified text boxes to enter your information in, a different format of the search box and lastly, a way to make sure you've done the basic checklist. They also hardly speak your language and refuse to deviate from the script in any way. Which, without much irony, is pretty much my experience with the first level support at a lot of companies. So I'd say they were quite successful with replacing that.
• ricardo81 5y ago
  I'm not in your field, just a vanilla programmer. The "want it" resonates. Seems like the difference is in receiving an answer that is relatively agreeable or sensical vs something that's actually substantive. And to be fair when it comes to the Turing test, there's people that will be overly agreeable for the sake of politeness, but ultimately when it comes to knowledge seeking we're after something more substantive.
• p1esk 5y ago
  As far as chatbots, we went from "tricks" to "math" in just ~10 years. Yes, still as dumb, but the underlying "technology" is very different. GPT-3 is a lot closer than ELIZA to how our brains do it.
  • chubot 5y ago
    It was more than 10 years ... People reacted the same way to SHRDLU 40+ years ago, and 20 years ago when I encountered it: https://en.wikipedia.org/wiki/SHRDLU https://en.wikipedia.org/wiki/SHRDLU There's definitely an illusion where we anthropomorphize very simple software, ascribe intention to it, etc.
    • p1esk 5y ago
      I'm not 100% sure, but I think in 2012 chatbots still used handcoded rules. So we switched from handcoded rules to trainable neural networks in just a few years. Models like GPT-3 are interesting because they are conceptually simple, and are able to do (previously) complicated tasks without any explicit training (e.g. simply by processing lots of text and trying to predict the next word). This is a huge advancement in AI field, even if many more advancements are needed to get to the human level.
      • fault1 5y ago
        I remember chatbots in 2000 using hidden markov models (and sometimes even neural nets), so from that standpoint, they aren't exactly new. The models are just larger now.
        • JamesBarney 5y ago
          Never underestimate the effects of scaling on intelligence. Caenorhabditis elegans neurons work a lot like our neurons, we just have a lot more of them.
          • fault1 5y ago
• avereveard 5y ago
  > We want it to be true Founders and salesman pretend it to be true for that sweet sweet VC money, while underneath devs try to fake it as plausibly as possible. And I'll up it with a prediction: watch closely call centers, and as soon as you see them closing in droves, invest in ai startups as something begun to really move.
  • dorkwood 5y ago
    How would you notice an increase in call centers closing? Is there a place to view that data?
    • avereveard 5y ago
      Job openings can make for a decent proxy. Large layoff would make the news too.
  • redisman 5y ago
    I interviewed for a “AI” company and asked them about their tech that could take phone calls and extract orders! Wow really cool and impressive! How do you solve X? Oh we have a call center in Mexico that takes all the calls. So you have no usable AI tech at all? Nope. Ok nice talking to you. They had signed with a major retail company. They were actually making software for the call center. And the “CTO” was “working on” the “AI” in parallel. The company name had AI in it too
    • raihansaputra 5y ago
      Here in Indonesia the joke is AI as Admin Intelligence, as Admin is the shorthand for people managing/doing administrative tasks. I admit it's a legit strategy to increase productivity (offshore/multitenant/software optimise service), but doing that and analyzing/training on the data still doesn't help creating an AI for it. Ex: Uber.
• pdimitar 5y ago
  You didn't have to go for the jugular and do the brutal kill on the first hit, dude! > We want it to be true, so we squint and connect dots and it's true. That's exactly the issue. You summarized it once and for all and we can all go home and stop discussing it now and forever (until we get a general AI that is). 1. Normal people want to have intelligent machines. They watch movies and series and imagine one day a robot will cook for them so they actively look for intelligence, as you said. They buy Roombas and imagine they are going to clean 100% of their rooms (seen it, heard it, watched them rage when it didn't happen). They buy Alexa-enabled devices and imagine themselves like some aristocrats barking orders at an intelligent home (lol). But yeah, that's what the normal people do. It's quite puzzling to me. 2. People who work in the area are obviously biased and I have argued with some of them here on HN but I view it as a doomed affair. They insist there's a lot of innovation going on and that improvements are being done all the time yet we still have embarrassing failures as Michele Obama classified as a male and various politicians classified as known criminals or black people in general classified as gorillas. Like OK, where are your precious improvements and why are they NEVER finding their way into the news? "It's hard to make someone understand something if their salary depends on them not understanding it", that's how I view the folks working in the DL / ML area. Sorry if that's offensive but just looking from the sidelines, it seems that what I say is true.
  • visarga 5y ago
    > Michele Obama classified as a male Yes, humans don't make embarrassing mistakes all the time. We're only making dignified mistakes.
    • throwawayboise 5y ago
      A good number of people will state that Michelle Obama is a male. I had no idea this was started by an AI misclassification (if indeed it was)
    • pdimitar 5y ago
      If something is to replace humans at doing a job X then it has to be better than the humans at job X. Also humans instinctively expect the machines to not make mistakes. I haven't made them such; it's just a fact of life. And we have to work with the expectations of the bigger populace.
• TedShiller 5y ago
  The reason laypeople want it to be true is because experts present it as being true.
  • tcgv 5y ago
    And marketers
    • TedShiller 5y ago
      Yes. And I certainly don’t see many AI experts or engineers trying to stop the marketers.
• crate_barre 5y ago
  It’s the stupid term AI that ruined everything. Our predecessors had enough intelligence, ironically, to call spellcheck what it is. Today it would be AI.
  • deleted 5y ago
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• paxcoder 5y ago
• kumarvvr 5y ago
  It has always been my strong belief that ML is good to solve problems, if the problems are very narrowly defined and training sets are very specific. Say, recognition of cats. Other than that, it is difficult to build stuff around something more broad. So, ML is a brand new paradigm that has its uses, just not in AGI.
• rtpg 5y ago
  Someone described GPT-3 as a "very good search engine" and I'm pretty happy with that explanation (even if honestly going from the regressions to 'search engine' is a pretty tenuous thing). At least then there's a more nice understanding that it's about matching stuff its already seen (for various definitions of seen) rather than making things up out of full cloth
  • Jensson 5y ago
    More specifically it is a templates search engine where it can replace some parts, like the name of persons etc. You know all those old "Hello { PERSON_1 }, have you heard about {PERSON_2} in {CITY_1}?", that is most how to think about it, it searches for a template and then applies it with the arguments it fetched from your string.
• Iv 5y ago
  I usually sum it up by saying that what we have is a very good bullshit generator. It is bad at reasoning but good at making up answers that sounds correct and crossing fingers that the corrector won't look twice at what they say.
  • aeternum 5y ago
    This is true but I think we also overestimate how good humans are at reasoning. Throughout the vast majority of human history we simply made up answers that sounded correct. The scientific method and the importance of truth is a relatively recent development.
    • boldpandabear 5y ago
• deleted 5y ago
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• jack_pp 5y ago
  I believe the human brain is just math and tricks, the difference is no one took care of an NN model like someone might take care of a baby and constantly train it over several years until it finally starts to understand the world. To be fair I don't think we even have the NN technology that would work even in these circumstances.