11 ms·

Are we in an AI Overhang?

261 points by andyljones 6y ago

datameta 6y ago
I wonder if at some point the amount of extra data necessary to achieve an n-fold improvement will outstrip what we can provide. I think the time for AI legislation is now - before FAAMG deploys something like the next-gen of GPT-3. Of course with the legislative lag that exists even for decade-old tech I don't have the highest confidence in this being achieved by a federal government in the state it is in now.
- rewq4321 6y ago
  On that data point: I wonder if anyone can comment on how much useful training data we could get out of generating text based on knowledge graphs/databases that we have. You can construct an awful lot of sentences out of just a few facts (e.g. weights of various classes to generate sentences like: "x's are heavier than y's, but not as heavy as z's"). All the variations would contain the same information (of subsets of it), but the same could be said of lots of text online. Obviously this is an inefficient way to incorporate the databases into a GPT-like model, but it might make sense economically given the race that is now playing out - just shoehorn it in or you'll be left behind (at least in the short term) by those who do. "We can work out how to make it efficient after we're rolling around in cash." The knowledge databases could be used to generate what would essentially be "word problems" (in math classes), starting with simple things like "If I put three marbles in a cup, and then I take one out, and each marble weighs 20g, then the remaining marbles weigh 40g in total" and moving on to progressively more complex ones. If that were to happen, then you'd see companies employing people to create templates which essentially convert databases into sentences/paragraphs, which can then be consumed by the GPT-like model. It seems like this data would need to be used in a sort of pre-training step though, because you want the model to encode all the relationships, but you don't want it to learn to generate these types of concrete sentences, specifically.
- inetsee 6y ago
  As blueeyes has already pointed out "Legislation in one nation will simply handicap that nation." I don't have a lot of faith in our legislators ability to legislate safety without relegating us to an AI backwater.
  - datameta 6y ago
    You're right. I think ideally the legislation should be international. Maybe something like the Washington Naval Treaty that set an upper limit on the tonnage and armament of new battleships. Or perhaps more aptly something akin to SALT I & II where older models are taken offline to avoid derelict AI systems from falling into malicious hands and to keep the number from growing out of control. Although this parallel is somewhat weak considering the capabilities of one advanced model are more valuable than 10x models of the last generation. Theoretical wishful thinking, I suppose, but I strongly believe that corp/govt scale ML research should be treated like advanced weaponry because it isn't a matter of if but when AI will be weaponized (whether the flavor of warfare is physical or informational). Although of course as with weapons treaties - the major powers would likely tend to be selective in what they commit to limiting themselves in.
    - Jach 6y ago
      The world couldn't even come together on controlling 3D printed weaponry, there's no hope for an arms treaty for AI right now. The "it's not feasible to regulate even if you tried" stance applies too -- you can restrict central actors without much difficulty, and that would work for AI just as well as it works for battleships, but there's a lot of distributed compute whereas there's not a lot of distributed shipyards. Like, you just have to follow what's been done with anime image nets to see that something like GPT-3 is possible for a distributed worldwide group to achieve and is not limited to firms or governments. Maybe when we have a disaster directly attributable to AI, nations can get on-board with something like the BWC and CWC. Until then, be even more pessimistic. (If you want a fun if rather dry book to read on material technology developments that were in the pipeline a couple decades ago, some of which have come to fruition, as well as some policy recommendations for the technologies that aren't generally good, check out Jürgen Altmann's Military Nanotechnology.)
dougmwne 6y ago
Let me flip this argument on its head. Consider this: About 5 years ago several key SV people including Sam Altman, Peter Thiel and Elon Musk became suddenly very concerned about AI ethics and started OpenAI. What if they, with this insider status, had already seen a GPT-3 like system at Google, Facebook, Baidu or wherever and its capabilities for political and social manipulation so concerned them that they started OpenAI in an effort to bring this tech out of the shadows and into the sunlight so we could debate it and regulate it. GPT-3 might not be a state of the art breakthrough. It could be just catching up with where the big tech companies were 5 years ago so that we can finally see what they are capable of. Corporate secrets are a normal part of doing business and maybe the tech companies didn't like the PR they would have gotten from publicizing something like this. Remember the blowback from Google's project that called business for their store hours? They already struggle with regulators across the world as it is. Do we really believe that little OpenAI is so much farther ahead of Google like the posted article posits?
- stjo 6y ago
  Sounds like a nice conspiracy, but realistically, how could they hide something like that? Presumably there are hundreds or more employees working on this. If Elon Musk et al. heard about it 5 years ago, this must be one of the best kept secrets in recent history.
  - ColanR 6y ago
    > how could they hide something like that? Very carefully. I mean, that's not much of an argument. Lots of stuff is successfully kept secret. The US managed to keep a lid on their surveilance for decades (iirc) before the lid got blown on that, and people used to give the same argument you are in that context, too. What's the alternative? Do you think megacorps never keep illicit things under wraps for extended periods of time?
    - ben_w 6y ago
      There is a formula for working out how long it will take for a conspiracy theory to be made public based on how many people are involved in it. I guess you could use it in reverse and produce an upper limit on how many people could be involved in a conspiracy if you assume that it has been secret for five years. That said, Elon Musk gives every impression of being a massive chatterbox who can’t keep his mouth shut even when its the SEC threatening to take Tesla away from him, so I very much doubt any conspiracy involves him.
blueyes 6y ago
Both DeepMind and OpenAI were founded on the premise that we are in an AI overhang. OpenAI, in particular, believes in scale. Scale will get us there based on the algorithms we have, such as the Transformer. With each new release, they add evidence that they were correct. The call for legislation neglects that there exists a global arms race to make this technology succeed. Legislation in one nation will simply handicap that nation. Against that backdrop, legislation is probably unlikely among the nations already leading in AI.
- hyperbovine 6y ago
  > With each new release, they add evidence that they were correct. Is it though? If the goal is human-level AI, or hell, even rat-level AI, the evidence is pretty convincing that you should be able to train and deploy it without requiring enough energy to sail a loaded container ship across the Pacific Ocean. Our brains draw about 20 watts, remember. This suggests to me that no, in fact, scale will not get us "there". https://www.forbes.com/sites/robtoews/2020/06/17/deep-learnings-climate-change-problem/ https://www.forbes.com/sites/robtoews/2020/06/17/deep-learni...
  - vikramkr 6y ago
    That 20 watts is to run the network. Our brain has had a billion years to work out details of the architecture and encode a lot of basic stuff as instinct (and it still sucks at a lot of things). You should be counting that energy cost as well - we didnt get from nerve nets to frontal lobes overnight.
    - logicslave 6y ago
      This exactly, I am so tired of reading these posts online that ignore the billions of years the human brain took to evolve
      - slowmovintarget 6y ago
        *millions, not billions. Earth is about 4.5B years old, life is about 3.7B years old, multicellular life (including life with neural nets) is about 600 million years old. I don't think the span from microbe to multicellular organism counts in brain evolution.
cs02rm0 6y ago
I fear Betteridge's law of headlines applies here. A CS lecturer of mine told us that when he was a student he had a lecturer who advised him to be sceptical of AI revolutions. That was nearly 20 years ago. I've no doubt we'll see further steps but I'm not going to hold my breath for something transformative.
- gameswithgo 6y ago
  Do you feel like the lecturer was correct, or incorrect?
- 2sk21 6y ago
  Indeed, see Gary Marcus' critique from last year: https://thegradient.pub/gpt2-and-the-nature-of-intelligence/ https://thegradient.pub/gpt2-and-the-nature-of-intelligence/
  - andyljones 6y ago
    It's worth chasing that with gwern's critique of Marcus' critque: https://www.gwern.net/GPT-3#marcus-2020 https://www.gwern.net/GPT-3#marcus-2020 (the critique is: GPT-3 can in fact do all the things Marcus said it couldn't)
    - sgt101 6y ago
      I can't play with GPT-3 but when I play with GPT-2 I can easily trick it with counting games. It does well with 0,1,2,3,.... but things like 0,1,3,6,10, get poor responses. Is GPT-3 good at that?
      - sktguha 6y ago
        yes. I have tried it with gpt-3 Q: what comes next in the series: 0,3,6, A: 9 Q: what comes next in the series: 0,3,6,9, A: 12
        ypcx 6y ago
gillesjacobs 6y ago
I largely agree with the arguments made, but the following assertion is plain bogus > GPT-3 is the first NLP system that has obvious, immediate, substantial economic value. Text mining (relation extraction, named entity recognition, terminology mining) and sentiment analysis are billion dollar industries and are being directly applied right now in marketing, finance, law, search, automotive, basically every industry. Machine translation is another huge industry of its own. Chat bots were all the hype a few years ago. Let's not reduce the whole field of NLP to language generation.
- ben_w 6y ago
  Is sentiment analysis really that good already? Every time I’ve looked at the start of the art in sentiment analysis, it seems to be suffering from the same issue that bag-of-words has with modifiers like “not”. Or is that more a theoretical problem than a practical one? I appreciate this is a rapidly moving field, so my knowledge could easily be out of date.
  - gillesjacobs 6y ago
    The issue you describe is typically called "Valence shifting" in this specific case "negation processing". It is of course a difficult problem to capture word-level sentiment and emotions but recent techniques in academic work obtain decent results. However, industry typically relies on sentence- or document-level sentiment in, for instance, customer reviews with systems obtaining 80-90 F1-score which is very good. Often in e-commerce, aspect-based sentiment analysis is used in which a qualifying sentiment is attached to a target aspect, e.g. from a phone review systems extract: battery: large > positive; screen: dim > positive. You might have seen these types of reports in aggregate on review our e-commerce sites yourself. It is however an ongoing field of research to process the scope of negation and uncertainty, but the field is making strides. State-of-the-art attention-based models obtain good scores on benchmark fine-grained sentiment analysis datasets such as the GoodFor/BadFor and MPQA2.0 of around 70% F1score [1]. This performance is nearly enough for commercial systems, depending on how you employ them. 1. https://link.springer.com/article/10.1186/s13673-019-0196-3 https://link.springer.com/article/10.1186/s13673-019-0196-3
GrantS 6y ago
I thought the overhang was going to be along the lines of the following, whether realistic or not: -GPT-3, as is, should be the inner loop of a continuously running process which generates 1000s+ of ideas for "how to respond next" to any query, with a separate network on top of it as the filter which cherry-picks the best responses (as humans are already doing with the examples they are posting) -Since GPT-3, as is, can already predict both sides of a conversation, it can steer a conversation toward a goal state just like AlphaGo does by evaluating 1000s+ of potential moves, lots of potential responses and counter-responses until it finds the best thing to say in order to get you to say what it "wants" you to say. It seems ready to go as the initial attempt at the inner loop of both of these tasks (and more) without modification or retraining of the core network itself, no?
- lambdatronics 6y ago
  I'd love to see what could be done with GPT-3 as part of a GAN. Text compression/summary, maybe?
- jobigoud 6y ago
  I was also thinking something like this. GPT-3 should be the internal monologue, the subconscious soup of words constantly exploring random thought alleys, and there should be another layer on top of it to bridge it with the outside.
PaulHoule 6y ago
It think it is all right except for the A.I. part. GPT-3 is taking a graph-structured object ("language" inclusive of syntax and semantics) over a variable-length discrete domain and crushing it into a high-dimensional vector in a continuous euclidean space. That's like fitting the 3-d spherical earth onto a 2-d map; any way you do it you do violence to the map. I think systems like GPT-3 are approaching an asymptote. You could put 10x the resources in and get 10% better results, another 10x and get 1% better results, something like that. You might do better with multi-task learning oriented towards specific useful functions (e.g. "is this period the end of a sentence?") but the training problem for GPT-3 is by no means sufficient for text understanding. GPT-3 fascinates people for various reasons, one of them being almost good enough at language, lacking understanding, faking it, and being the butt of a joke. If GPT-3 were a person with similar language skills and people blogged about that person, mocking it's output, the way we do with GPT-3, people would find that cringeworthy. Neurotypicals welcome it as one of their own, and aspies envy it because it can pass better than they can. At $2 a page it can replace richmansplainers such as Graham and Thiel who never listen. It's not a solution for folks like like Phillip Greenspun who read the comments on their blogs. For that matter, it may very well model the mindlessness of corporate America: if you accept GPT-3 you prove you will see the Emperor's clothes no matter how buck naked he is. AT&T executives had a perfectly good mobile phone business: what possessed them to buy a failing satellite TV business? Could GPT-3 replace that "thinking" at $2 a page? Such a bargain.
- SpicyLemonZest 6y ago
  Why do you think that systems like GPT-3 are approaching an asymptote? Most people I've talked to say the opposite; they wouldn't have expected GPT-3 could be so much better than GPT-2 with no major additional breakthroughs.
  - PaulHoule 6y ago
    It's just structurally wrong for the domain. For instance, understanding language requires some of the capabilities of a SAT solver. This was something everybody believed in 1972, but today is denied. Fundamentally "understanding" problems require the ability to consider multiple alternative interpretations of a situation, often choose one or work with the incomplete knowledge you have. Back in the 1970s we had intellectually honest people like Richard Dreyfus writing books like "Things Computers Can't Do" that describe many specific ways the architecture at the time fall short. People on GPT-3 are working in a way that is academically valid (able to make results that are meaningful to a community) but from engineering it is like building a bridge with one end or a tall tower that carries no load. GPT-3 has a structural mismatch with the domain it works in. Unlike early medical diagnosis systems like MYCIN, it is never a doctor, it just plays one on TV and it does the "passing for neurotypical" terrifyingly well. The secret of GPT-3 is that people want to believe in it. Somebody will have it generate 100 text snippets and they will show you the three best. Your mind makes up meaning to fill up for its mindlessness. When this was going on with ELIZA in 1965 people quickly understood that ELIZA was hijacking our instinct to make meaning. For some reason people don't seem to have that insight today, and it bothers me why that is. Back in the 1980s they had a lot of fear about compressing medical images because it could lead to a wrong diagnosis. Today you see articles in the press that are completely unquestioning that a neural network that has been trained to hallucinate healthy and cancerous tissues will always hallucinate the right thing when you are looking at a patient.
polytely 6y ago
I've been wondering about something: (I only know the basics of AI, this might be kinda incoherent) Right now if you look at GTP-3's output it seems like it's approaching a convincing approximation of a bluffing college student writing a bad paper, correct sentences and stuff but very 'cocky'. It cannot tell right from wrong, and it will just make up convincing rubbish, 'hoping' to fool the reader. (I know I'm anthropomorphizing but bear with me). Current models are being trained on a huge amount of internet text. As smarmy denizens of hackernews we know that people are very often wrong (or 'not even wrong') on the internet. It seems to me that anything trained on internet data is kinda doomed to poison itself on the high ratio of garbage floating around here? We've seen with a lot of machine-learning stuff that biased data will create biased models, so you have to be really careful what you train it on. The dataset on which GTP-n has to be trained has to be pretty huge(?); and moderation is hard(?) and doesn't scale; it's easier to generate falsehood than truth; and the further we go along the more of internet data will be (weaponized?) output of GTP-(n-1); So won't the arrival of AGI just be sabotaged by the arrival of AGI? Has anyone written something about the process of building AGI that deals with this?
- tda 6y ago
  > It would be a bit like how carbon dating or production of low background steel changed after 1945 due to nuclear testing https://news.ycombinator.com/item?id=23896293 https://news.ycombinator.com/item?id=23896293 I guess books published will be more useful than reddit rants (depending on the application)
- deleted 6y ago
  [deleted]
- lordnacho 6y ago
  I've been wondering about something similar to you, but I read one of Pearl's causality books recently and thought that might be the missing piece. It's certainly impressive what GPT-3 can do, but it boggles the mind how much data went into it. By contrast a well-educated renaissance man might have read a book every month or so from age 15 to 30? That doesn't seem to be anywhere near what GPT could swallow in a few seconds. When you look at how GPT answers things, it kinda feels like someone who has heard the keywords and can spout some things that at least obscure whether it has ever studied a given subject, and this is impressive. What I wonder is whether it can do reasoning of the causality kind: what if X hadn't happened, what evidence do we need to collect to know if theory Z is falsified, which data W is confounding? To me it seems that sort of thing is what smart people are able to work out, with a lot of reading, but not quite the mountain that GPT reads.
sgt101 6y ago
Any one know how to test this yet?
legulere 6y ago
Nah we’re in for the next AI winter. GPT-3 shows how much energy is needed to perform a nice trick with current technology. We mostly have reached the limits of the technology. Investing more compute power for a few percentage points more Precision is not going to bring the technology forward.
- pretendscholar 6y ago
  If you look at the paper doesn't it scale well across many different metrics?
- TomMarius 6y ago
  It's a one time investment though, and not that large relatively speaking. Is is that much more costly and less rewarding than other investment opportunities?
- Hypx 6y ago
  Agreed. Nearly every AI startup or idea has disappointed or failed. We’re spending the equivalent of billions of dollars on something dumber than a rat in most cases.
- Veedrac 6y ago
  2017 SOTA on Penn Treebank was 47.69 perplexity. GPT-3 is at 20.5. AI has already been productized on consumer devices through Siri, Google Assistant, speech detection, speech generation, textual photo library search, similar data augmentations for web search, Google Translate, recommendation algorithms, phone cameras, server cooling optimization, phone touch screens' touch detection, video game upscaling, noise reduction in web calls, file prefetching, Google Maps, OCR, and more. DLSS alone justified continued investment by NVIDIA. NVIDIA Ampere will be ~6x as fast at running consumer-targeted models as Turing, given raw throughput increases compounded with sparsity and int8 hardware. A huge number of research threads around AI have direct applicability to large tech companies.
  - legulere 6y ago
    I'm not arguing that current machine learning technologies are not useful. I'm just arguing that progress is based on increasing some metric, usually depending on a trade-off of computation. This can even make ML-techniques applicable to some new fields, but it's not what is holding back autonomous driving, the often touted parade example which also brings in a lot of employment for machine learning. This article clearly sits on the peak of inflated expectations in the hype cycle. https://en.wikipedia.org/wiki/Hype_cycle https://en.wikipedia.org/wiki/Hype_cycle
rvz 6y ago
ʸᵉˢ Explainability in AI is really overlooked and often skipped over as there is little progress in this area. GPT-3 is essentially GPT-2 + tons of data, compute and parameters and yet it still cannot explain itself as to why it can generate 'human-level' text, much like how AlphaGo can't explain why it performed move 37. Not discrediting these achievements, but explainability is just as important in these AI models. Once you have an AI-based 'auto-pilot' in any vehicle, the importance of AI explainability will haunt manufacturers when the regulators would want them to explain why this 'AI' took this decision and they're unable to explain this. I hope GPT-4 isn't just going to be GPT-3 + 1000x the data. Otherwise nothing would have changed here other than the parameters and data.
- lambdatronics 6y ago
  The easy solution is to copy what the human brain does: just make up something plausible. There's pretty good evidence that we don't have great introspective access to much of our own internal processing. We just paper it over as "intuition" or "judgement."
- sbierwagen 6y ago
  What does explainability mean for superhuman AI? We already see this in superhuman stock algorithms. You can "debug" them, in the sense that for a given trade, it can tell you what signals provoked it. But they don't make any sense: it saw rainfall in the Amazon tick up, the price of beef in Russia tick down, and the UK call a snap election, so it bought more GE stock. You could... theoretically... write a story that connected those dots, but it will either be facile or nonsensical. That's because the model of the market the algo has is bigger and more complete than anything a human can have. It's drawing a straight line through some upper-dimensional manifold that you can't comprehend. It can't explain what it's doing to you anymore than you can explain "algorithmic stock trading" to a three year old child. You can say what the outcome was, but you can't explain it in such a way that the kid could replicate the performance.
simonw 6y ago
I've been pandemic-rewatching Person of Interest. It's really quite shocking how much more relevant it feels today compared to when it aired just a few years ago. It's fun looking at things like GPT-3 and imagining how they could be used to build the surveillance AI at the heart of Person of Interest. (If you haven't watched Person of Interest yet, here's my pitch for it: it's a CBS procedural where the hook is that an engineer built a secret, surveillance feed tracking AI for the government after 9/11 - but he cared about civil liberties, so he built it as an impenetrable black box. All it does is kick out the SSN of someone who is about to be either the victim or the perpetrator of a terrorist attack - which means government agents still have to investigate what's going on rather than taking the AI's word for it. "The Machine" also sees victims/perpetrators of violent crimes - but the government don't care about those. Finch, the machine's inventor, does - so he fakes his own death, hooks into a backdoor into the machine that gives him those SSNs and sets up a private vigilante squad to help stop the violent crimes from happening. So that gives you the "case of the week". Only it's actually an extremely deep piece of philosophical science fiction disguised as a case-of-the-week procedural, and as time goes on the plots become much more about AI, the machine, attempts to build rival machines, AI ethics and so on. It's the best fictional version of AI I've ever seen. The creative team later worked on Westworld.)
- Barrin92 6y ago
  I honestly don't think the show has much to do with AI at all and is more like a retelling of the Greek classics in a sci-fi wrapping, which is actually something that comes up in the show at several points excplicitly. The AIs in the show very quickly turn into godlike characters with antropomorphic personalities and the real world issues of AI such as surveillance, economics and so on are all dealt with in very shallow fashion. I had the same issues with Westworld too. It turns from an AI premise into a classical Christian morality tale very fast. ("we need to suffer to become conscious").
  - simonw 6y ago
    One of the things I loved about the show is that different characters have different philosophies concerning AI, and they argue about them. Nathan v.s. Fitch. Fitch vs. Root. Control, Greer - for the most part the show tried to give some depth and background to their thinking around the implications of what they were responsible for. Way smarter than you would expect from a CBS procedural!
g_airborne 6y ago
> The current hardware floor is nearer to the RTX 2080 TI's $1k/unit for 125 tensor-core TFLOPS, and that gives you $25/pflops-d. It's definitely true that the RTX 2080 Ti would be more efficient money-wise, but the Tensor Cores are not going to get you the advertised speedup. Those speedups can only be reached in ideal circumstances. Nevertheless, the article as a whole makes a very good point. The thing that is most scary about this is that it would become very hard for new players to enter the space. Large incumbents would be the only ones able to make the investments necessary to build competitive AI. Because of that, I really hope the author isn't right - unfortunately they probably are.
- tomp 6y ago
  OpenAI is kind-of a new player. Well-funded, but still - there's a lot of money available for this kind of exponential opportunities.
maerF0x0 6y ago
> so dropping $1bn or more on scaling GPT up by another factor of 100x is entirely plausible right now. I'd note it's rare that cost of scaling a computing project is a linear growth function. 100x-ing an AI project could be 1100x cost.
- lacker 6y ago
  For AI on this scale, there are two important costs. The cost of compute, and the cost of engineer salaries. Scaling the compute is probably a bit above linear due to various overheads, but the cost of engineer salaries is far less than linear, so I would expect the total cost of scaling large-scale AI to be sublinear.
  - gwern 6y ago
    I would expect sublinear scaling in compute cost. Learning curve effects are strong in DL: https://cdn.openai.com/papers/ai_and_efficiency.pdf https://cdn.openai.com/papers/ai_and_efficiency.pdf The more runs you do, the cheaper they get. Plus, OA is no longer running on rented V100s, they have their own MS Azure supercomputer, remember (in fact, GPT-3's evaluation was interrupted because they moved to it), so they remove the enormous cloud margins.
deleted 6y ago
[deleted]
lacker 6y ago
GPT-3 is the first AI system that has obvious, immediate, transformative economic value. I think the jury is still out on this one. It certainly seems powerful, it's doing interesting things, and it's better in many ways than any system that has come before. But there's a different between exciting demos and transformative economic value. It's too soon to be sure, but to me, the most interesting question is whether any valuable startups will be built on top of GPT-3. Some leading indicators before that are whether useful products are built on GPT-3, and whether early-stage startups built on GPT-3 get seed investment. I'm not aware of any of these yet but maybe latitude.io counts as one.
cl42 6y ago
This is a fascinating discussion -- I'm curious what people thing is the next step with AI? The post and several commenters here talk about how the tech in GPT-3 is "dumb" in that it's a big network but the network architecture is a fairly standard approach. I'm curious what people think are the next stages of AI research that companies are working on... Is it Probabilistic Graphical Models? Is it Probabilistic Programming? Is it knowledge graph extraction from text? Is it something else? Curious what people think...
- ilaksh 6y ago
  I think its about automatically building accurate and well-factored world models online that ultimately integrate not only high-dimensional sense data (such as visual information) but also language. This involves effectively solving the symbol grounding problem among other things. There is some serious effort in this direction in deep learning. There are also other efforts using different types of probabilistic programming as well as symbolic and neural net combinations. There's another link on one of the first few HN pages right now about dreaming. I think that dreaming gives one a lucid demonstration of some of the capabilities that we need to emulate if we are going to have human-like intelligence. AI will need to be able to visualize new situations, basically like on-demand, flexible simulations of mashed-up possibilities, involving things like physics and psychology etc. I think we almost need the AI to have something like a 3d gaming engine with physics, but also it can effortlessly conjure up AI agents in this simulation, but also, many of the physics rules and behaviors of the AI agents are automatically learned with only a few examples. This is the type of capability that allows humans (and some other animals) to adjust so readily to new situations. I speculate that there may be some representation or type of computation that has not been invented yet which facilitates both the simulation-type data and also the abstractions over it, all the way up to language, in a more seamless way than has so far been described. I saw a paper talking about the symbol grounding problem in terms of everything being categories, but really in the end it was broken down into something kind of like Lisp + probabilistic programming, and it seemed to not really have sufficient granularity to really do justice or properly integrate sense data. Certainly not in a seamless or truly unified way in my opinion. Although I guess I don't really understand category theory.
akyu 6y ago
I'm very heavily inclined to believe that yes we are. And my money is still on DeepMind.
- ghostbrainalpha 6y ago
  Can you link to the critical paper you are referencing?
01100011 6y ago
One thing I don't hear a lot of people talking about are ML/AI systems in the hands of government agencies. We know that the military and NSA are often ahead in many technologies but when it comes to AI the assumption seems to be that the industry is moving faster than the government. Is that really a safe assumption? The goverment is openly using autonomous systems to pilot drones, but what else are they leveraging AI for? Threat analysis? Logistics? Weapons optimization? PsyOps? The DoE is openly a very large consumer of GPUs. What about the military?
- heavyset_go 6y ago
  > Is that really a safe assumption? Not at all. The government can throw billions of dollars at a problem that, if solved, will never turn a profit or immediately benefit a business.
- confeit 6y ago
  You can get a glimpse by scrolling websites like: https://www.darpa.mil/opencatalog?ppl=view200&sort=title&ocFilter=software https://www.darpa.mil/opencatalog?ppl=view200&sort=title&ocF... [.mil] and looking at DARPA and Office of Naval Research sponsored ML/AI research. The military has been deeply involved with ML/AI research since its inception, and it is near impossible to avoid first - or second degree involvement, if active in ML/AI. The military wants: automated chat agents/web users that can be sent to dark web markets and hacker IRC channels and report back intelligence. Common sense inference from security and drone footage: predict who the killer is when watching a movie. Author deanonimization and cross-device tracking. Global-scale 99.9%+ accurate face detection. The Dutch Intelligence Agency organizes a yearly competition with difficult codes to crack. [1] It is rare for someone to answer all questions correctly. The answers require logic, creativity, common sense, linguistics, causal inference, spatial reasoning, expertise, analysis, and systematic thinking. I bet the military would be mighty interested in an automated problem solver for that. And mighty scared some other country gets there first. [1] https://www.aivd.nl/onderwerpen/aivd-kerstpuzzel https://www.aivd.nl/onderwerpen/aivd-kerstpuzzel
amelius 6y ago
Perhaps I missed it but what are some useful applications of GTP-3?
- earthboundkid 6y ago
  Promoting your SoundCloud account at the end of a Twitter thread.
- ALittleLight 6y ago
  For it "as is" - i.e. without imagining any new things, I would say you could make money from AI Dungeon, chat bot, and selling API access. I know that I badly want to play with the AI and would pay some amount per month to get some number of queries.
  - ilaksh 6y ago
    AI Dungeon has a monthly fee now to upgrade to GPT-3 and a new much better engine. It works pretty well.
earthboundkid 6y ago
GPT-3 is a search engine pretending to be an AI.
- deleted 6y ago
  [deleted]
haolez 6y ago
If I had the money and the dataset for training a model like GPT (that's a big if), is the code to implement such a thing trivial? Or is it a valuable proprietary asset of OpenAI as well as their instance per se?
- zitterbewegung 6y ago
  You would have to also reproduce their code which is the largest cost in any software development project . The code is non trivial but if you wait someone reimplements it . The dataset is also nontrivial because they probably cleaned the data which. It’s a valuable asset but it’s not like someone couldn’t reproduce it.
YeGoblynQueenne 6y ago
>> GPT-3 is the first AI system that has obvious, immediate, transformative economic value. To say the least, it is not immediately clear where that "transformative economic value" lies. From what I've seen so far GPT-3 can generate structurally smooth but completely incoherent text and despite claims to the contrary cannot perform anything close to "reasoning" [1]. It can also perform some side-tasks like machine translation and question answering, though with nowhere near good enough accuracy for it to be used as a commercial solution for these tasks. All this is not very useful or even interesting. Text generation is a fun passtime but unless one can control the generation to very precise specifications, to generate good quality text that makes sense on a particular subject, text generation is nothing but a toy with no commercial value (and even its scientific value is not very clear). And GPT-3's generation cannot be controlled to such precise specifications. We've had AI software that could interact intelligently with a user since the 1970's, with Terry Winograd's SHRDLU [2] and that never led to "immediate, transformative economic value", even though it was every bit the sci-fi-like AI program that could be directed by natural language to perform specific tasks with competence, albeit in a restricted enviroment (a "blocks world"). GPT-3 is not even capable of doing anything like that (nor are any other modern systems). How is a language model that is likely to respond with "blue offerings to the green god of mad square frogs" to a request to "place the blue pyramid on the red sphere" bring "transfomative" value? In fact, we've had systems capable of generating much more coherent (and still grammatically corret) text for some time [3] and even those have not caused a dramatic upheaval of "transformative economic value". I'm sorry but I'm afraid that, with GPT-3, we're again in a spiralling peak of hype, just as we were a few years ago with all the claims about sef driving cars "next year" etc. I think we all know how those panned out. In any case, you don't have to take my word for it. As with self-driving cars, all we have to do is wait a few years. Say, until 2024. We'll have a good idea about GPT-3's "transformative value" by then. __________________ [1] Unless of course one insists on Procrusteanising the definition of "reasoning" sufficently to cover essentially random guessing. [2] https://en.wikipedia.org/wiki/SHRDLU https://en.wikipedia.org/wiki/SHRDLU [3] I'll need to dig up some references if you ask, but in the meantime search for "story generation".
SeanLuke 6y ago
> GPT-3 is the first AI system that has obvious, immediate, transformative economic value. This statement is unbelievably ignorant of history. Just picking one random example out of a hat: planning and scheduling systems have had a profound impact on the manufacturing and shipping industries for many decades now.
f00zz 6y ago
In Kernighan and Pike's "The Practice of Programming" there's a chapter that covers the implementation in different languages of a random text generator using markov chains. It's a nice exercise and a lot of fun to play with. I'm guessing that not many people have read that book, because I'm seeing here and elsewhere even technical people talking about GPT-3 as if it's heralding the imminent advent of SkyNet. I get that transformers have a somewhat longer attention span than markov chains, but it's still a statistical language model. It can't even do the kind of planning or reasoning that early AI demos like SHRDLU could.
- Jach 6y ago
  It's a good book, and one most programmers should read early on (it grows less useful over time), but to think of this as only an incremental improvement over markov chains is underselling the advance. The technology is different, and can scale to much higher levels of capability. AGI levels? Almost certainly not. But it's passing usefulness thresholds so things can progress elsewhere.
  - scottlocklin 6y ago
    >to think of this as only an incremental improvement over markov chains is underselling the advance. Erm, citations needed. It's a giant, inefficient and shitty KNN model, which is capable of mimicking markov chains. Wonderful marketing achievement and not much else.
    - Jach 6y ago
      https://www.gwern.net/GPT-3 https://www.gwern.net/GPT-3 (Edit: in case it's not clear, I suspect if you give an honest perusal of that page, and the pages it links, and the pages they link, you'll come away with a different opinion.)
- Veedrac 6y ago
  It's ridiculous to equate this to a traditional Markov chain language model. Here's something a Markov chain certainly cannot do: Human: I want to test your creativity. Please invent a new word and give its meaning. GPT-3: Ok. Um... Tana means to hit someone with the intention to wound them. Human: Please use the word tana in a sentence. GPT-3: You are about to tana the man attacking you. Human: Speak like a dwarf. GPT-3: I ain't talkin' like a dwarf. https://www.reddit.com/r/MachineLearning/comments/hvssqn/d_gpt3_demos/fyylreb/ https://www.reddit.com/r/MachineLearning/comments/hvssqn/d_g...
Pamar 6y ago
You know what? There are two people I'd really would like to interview about GTP-3 (and what an hypothetical GTP-4 or 5 could achieve). One is Hofstadter. The other is Ted Chang.
mijail 6y ago
Can anyone provide the background or framework of how I should see the business value of gpt3. Are there businesses that have a tremendous needs for the possibilities it provides? I've seen use cases that some NLG companies provide like sports and stock summaries but what world should I imagine where this is transformative?
eternalban 6y ago
Question: Is a collapse in learning time a possible breakthrough for future, or do we have definitive ~information theoretic bounds for says number of dimensions, etc.
andrewnc 6y ago
> GPT-3 is the first AI system that has obvious, immediate, transformative economic value. Seriously? No other piece of machine learning has had economic value? How short sighted.
mrfusion 6y ago
> GPT-3 is the first AI system that has obvious, immediate, transformative economic value. What is it’s economic value? What does it transform? I’ve been trying to figure that out since I heard about it. Anyone have any ideas?
- crowbahr 6y ago
  Theory based on some output I've read: It's good enough to actually start replacing a lot of customer service jobs. Not just being a shitty annoyance like current bots but being useful in that it will be as flexible as a human, directing you to good help via vague terms, potentially being smart enough to refer you higher up if necessary. Getting rid of all those screening call center employees is potentially very lucrative.

261 points by andyljones 6y ago

I wonder if at some point the amount of extra data necessary to achieve an n-fold improvement will outstrip what we can provide. I think the time for AI legislation is now - before FAAMG deploys something like the next-gen of GPT-3. Of course with the legislative lag that exists even for decade-old tech I don't have the highest confidence in this being achieved by a federal government in the state it is in now.

rewq4321 6y ago
On that data point: I wonder if anyone can comment on how much useful training data we could get out of generating text based on knowledge graphs/databases that we have. You can construct an awful lot of sentences out of just a few facts (e.g. weights of various classes to generate sentences like: "x's are heavier than y's, but not as heavy as z's"). All the variations would contain the same information (of subsets of it), but the same could be said of lots of text online. Obviously this is an inefficient way to incorporate the databases into a GPT-like model, but it might make sense economically given the race that is now playing out - just shoehorn it in or you'll be left behind (at least in the short term) by those who do. "We can work out how to make it efficient after we're rolling around in cash." The knowledge databases could be used to generate what would essentially be "word problems" (in math classes), starting with simple things like "If I put three marbles in a cup, and then I take one out, and each marble weighs 20g, then the remaining marbles weigh 40g in total" and moving on to progressively more complex ones. If that were to happen, then you'd see companies employing people to create templates which essentially convert databases into sentences/paragraphs, which can then be consumed by the GPT-like model. It seems like this data would need to be used in a sort of pre-training step though, because you want the model to encode all the relationships, but you don't want it to learn to generate these types of concrete sentences, specifically.
inetsee 6y ago
As blueeyes has already pointed out "Legislation in one nation will simply handicap that nation." I don't have a lot of faith in our legislators ability to legislate safety without relegating us to an AI backwater.
- datameta 6y ago
  You're right. I think ideally the legislation should be international. Maybe something like the Washington Naval Treaty that set an upper limit on the tonnage and armament of new battleships. Or perhaps more aptly something akin to SALT I & II where older models are taken offline to avoid derelict AI systems from falling into malicious hands and to keep the number from growing out of control. Although this parallel is somewhat weak considering the capabilities of one advanced model are more valuable than 10x models of the last generation. Theoretical wishful thinking, I suppose, but I strongly believe that corp/govt scale ML research should be treated like advanced weaponry because it isn't a matter of if but when AI will be weaponized (whether the flavor of warfare is physical or informational). Although of course as with weapons treaties - the major powers would likely tend to be selective in what they commit to limiting themselves in.
  - Jach 6y ago
    The world couldn't even come together on controlling 3D printed weaponry, there's no hope for an arms treaty for AI right now. The "it's not feasible to regulate even if you tried" stance applies too -- you can restrict central actors without much difficulty, and that would work for AI just as well as it works for battleships, but there's a lot of distributed compute whereas there's not a lot of distributed shipyards. Like, you just have to follow what's been done with anime image nets to see that something like GPT-3 is possible for a distributed worldwide group to achieve and is not limited to firms or governments. Maybe when we have a disaster directly attributable to AI, nations can get on-board with something like the BWC and CWC. Until then, be even more pessimistic. (If you want a fun if rather dry book to read on material technology developments that were in the pipeline a couple decades ago, some of which have come to fruition, as well as some policy recommendations for the technologies that aren't generally good, check out Jürgen Altmann's Military Nanotechnology.)

dougmwne 6y ago

Let me flip this argument on its head. Consider this: About 5 years ago several key SV people including Sam Altman, Peter Thiel and Elon Musk became suddenly very concerned about AI ethics and started OpenAI. What if they, with this insider status, had already seen a GPT-3 like system at Google, Facebook, Baidu or wherever and its capabilities for political and social manipulation so concerned them that they started OpenAI in an effort to bring this tech out of the shadows and into the sunlight so we could debate it and regulate it. GPT-3 might not be a state of the art breakthrough. It could be just catching up with where the big tech companies were 5 years ago so that we can finally see what they are capable of. Corporate secrets are a normal part of doing business and maybe the tech companies didn't like the PR they would have gotten from publicizing something like this. Remember the blowback from Google's project that called business for their store hours? They already struggle with regulators across the world as it is. Do we really believe that little OpenAI is so much farther ahead of Google like the posted article posits?

stjo 6y ago
Sounds like a nice conspiracy, but realistically, how could they hide something like that? Presumably there are hundreds or more employees working on this. If Elon Musk et al. heard about it 5 years ago, this must be one of the best kept secrets in recent history.
- ColanR 6y ago
  > how could they hide something like that? Very carefully. I mean, that's not much of an argument. Lots of stuff is successfully kept secret. The US managed to keep a lid on their surveilance for decades (iirc) before the lid got blown on that, and people used to give the same argument you are in that context, too. What's the alternative? Do you think megacorps never keep illicit things under wraps for extended periods of time?
  - ben_w 6y ago
    There is a formula for working out how long it will take for a conspiracy theory to be made public based on how many people are involved in it. I guess you could use it in reverse and produce an upper limit on how many people could be involved in a conspiracy if you assume that it has been secret for five years. That said, Elon Musk gives every impression of being a massive chatterbox who can’t keep his mouth shut even when its the SEC threatening to take Tesla away from him, so I very much doubt any conspiracy involves him.

blueyes 6y ago

Both DeepMind and OpenAI were founded on the premise that we are in an AI overhang. OpenAI, in particular, believes in scale. Scale will get us there based on the algorithms we have, such as the Transformer. With each new release, they add evidence that they were correct. The call for legislation neglects that there exists a global arms race to make this technology succeed. Legislation in one nation will simply handicap that nation. Against that backdrop, legislation is probably unlikely among the nations already leading in AI.

hyperbovine 6y ago
> With each new release, they add evidence that they were correct. Is it though? If the goal is human-level AI, or hell, even rat-level AI, the evidence is pretty convincing that you should be able to train and deploy it without requiring enough energy to sail a loaded container ship across the Pacific Ocean. Our brains draw about 20 watts, remember. This suggests to me that no, in fact, scale will not get us "there". https://www.forbes.com/sites/robtoews/2020/06/17/deep-learnings-climate-change-problem/ https://www.forbes.com/sites/robtoews/2020/06/17/deep-learni...
- vikramkr 6y ago
  That 20 watts is to run the network. Our brain has had a billion years to work out details of the architecture and encode a lot of basic stuff as instinct (and it still sucks at a lot of things). You should be counting that energy cost as well - we didnt get from nerve nets to frontal lobes overnight.
  - logicslave 6y ago
    This exactly, I am so tired of reading these posts online that ignore the billions of years the human brain took to evolve
    - slowmovintarget 6y ago
      *millions, not billions. Earth is about 4.5B years old, life is about 3.7B years old, multicellular life (including life with neural nets) is about 600 million years old. I don't think the span from microbe to multicellular organism counts in brain evolution.

cs02rm0 6y ago

I fear Betteridge's law of headlines applies here. A CS lecturer of mine told us that when he was a student he had a lecturer who advised him to be sceptical of AI revolutions. That was nearly 20 years ago. I've no doubt we'll see further steps but I'm not going to hold my breath for something transformative.

gameswithgo 6y ago
Do you feel like the lecturer was correct, or incorrect?
2sk21 6y ago
Indeed, see Gary Marcus' critique from last year: https://thegradient.pub/gpt2-and-the-nature-of-intelligence/ https://thegradient.pub/gpt2-and-the-nature-of-intelligence/
- andyljones 6y ago
  It's worth chasing that with gwern's critique of Marcus' critque: https://www.gwern.net/GPT-3#marcus-2020 https://www.gwern.net/GPT-3#marcus-2020 (the critique is: GPT-3 can in fact do all the things Marcus said it couldn't)
  - sgt101 6y ago
    I can't play with GPT-3 but when I play with GPT-2 I can easily trick it with counting games. It does well with 0,1,2,3,.... but things like 0,1,3,6,10, get poor responses. Is GPT-3 good at that?
    - sktguha 6y ago
      yes. I have tried it with gpt-3 Q: what comes next in the series: 0,3,6, A: 9 Q: what comes next in the series: 0,3,6,9, A: 12
      - ypcx 6y ago

gillesjacobs 6y ago

I largely agree with the arguments made, but the following assertion is plain bogus > GPT-3 is the first NLP system that has obvious, immediate, substantial economic value. Text mining (relation extraction, named entity recognition, terminology mining) and sentiment analysis are billion dollar industries and are being directly applied right now in marketing, finance, law, search, automotive, basically every industry. Machine translation is another huge industry of its own. Chat bots were all the hype a few years ago. Let's not reduce the whole field of NLP to language generation.

ben_w 6y ago
Is sentiment analysis really that good already? Every time I’ve looked at the start of the art in sentiment analysis, it seems to be suffering from the same issue that bag-of-words has with modifiers like “not”. Or is that more a theoretical problem than a practical one? I appreciate this is a rapidly moving field, so my knowledge could easily be out of date.
- gillesjacobs 6y ago
  The issue you describe is typically called "Valence shifting" in this specific case "negation processing". It is of course a difficult problem to capture word-level sentiment and emotions but recent techniques in academic work obtain decent results. However, industry typically relies on sentence- or document-level sentiment in, for instance, customer reviews with systems obtaining 80-90 F1-score which is very good. Often in e-commerce, aspect-based sentiment analysis is used in which a qualifying sentiment is attached to a target aspect, e.g. from a phone review systems extract: battery: large > positive; screen: dim > positive. You might have seen these types of reports in aggregate on review our e-commerce sites yourself. It is however an ongoing field of research to process the scope of negation and uncertainty, but the field is making strides. State-of-the-art attention-based models obtain good scores on benchmark fine-grained sentiment analysis datasets such as the GoodFor/BadFor and MPQA2.0 of around 70% F1score [1]. This performance is nearly enough for commercial systems, depending on how you employ them. 1. https://link.springer.com/article/10.1186/s13673-019-0196-3 https://link.springer.com/article/10.1186/s13673-019-0196-3

GrantS 6y ago

I thought the overhang was going to be along the lines of the following, whether realistic or not: -GPT-3, as is, should be the inner loop of a continuously running process which generates 1000s+ of ideas for "how to respond next" to any query, with a separate network on top of it as the filter which cherry-picks the best responses (as humans are already doing with the examples they are posting) -Since GPT-3, as is, can already predict both sides of a conversation, it can steer a conversation toward a goal state just like AlphaGo does by evaluating 1000s+ of potential moves, lots of potential responses and counter-responses until it finds the best thing to say in order to get you to say what it "wants" you to say. It seems ready to go as the initial attempt at the inner loop of both of these tasks (and more) without modification or retraining of the core network itself, no?

lambdatronics 6y ago
I'd love to see what could be done with GPT-3 as part of a GAN. Text compression/summary, maybe?
jobigoud 6y ago
I was also thinking something like this. GPT-3 should be the internal monologue, the subconscious soup of words constantly exploring random thought alleys, and there should be another layer on top of it to bridge it with the outside.

PaulHoule 6y ago

It think it is all right except for the A.I. part. GPT-3 is taking a graph-structured object ("language" inclusive of syntax and semantics) over a variable-length discrete domain and crushing it into a high-dimensional vector in a continuous euclidean space. That's like fitting the 3-d spherical earth onto a 2-d map; any way you do it you do violence to the map. I think systems like GPT-3 are approaching an asymptote. You could put 10x the resources in and get 10% better results, another 10x and get 1% better results, something like that. You might do better with multi-task learning oriented towards specific useful functions (e.g. "is this period the end of a sentence?") but the training problem for GPT-3 is by no means sufficient for text understanding. GPT-3 fascinates people for various reasons, one of them being almost good enough at language, lacking understanding, faking it, and being the butt of a joke. If GPT-3 were a person with similar language skills and people blogged about that person, mocking it's output, the way we do with GPT-3, people would find that cringeworthy. Neurotypicals welcome it as one of their own, and aspies envy it because it can pass better than they can. At $2 a page it can replace richmansplainers such as Graham and Thiel who never listen. It's not a solution for folks like like Phillip Greenspun who read the comments on their blogs. For that matter, it may very well model the mindlessness of corporate America: if you accept GPT-3 you prove you will see the Emperor's clothes no matter how buck naked he is. AT&T executives had a perfectly good mobile phone business: what possessed them to buy a failing satellite TV business? Could GPT-3 replace that "thinking" at $2 a page? Such a bargain.

SpicyLemonZest 6y ago
Why do you think that systems like GPT-3 are approaching an asymptote? Most people I've talked to say the opposite; they wouldn't have expected GPT-3 could be so much better than GPT-2 with no major additional breakthroughs.
- PaulHoule 6y ago
  It's just structurally wrong for the domain. For instance, understanding language requires some of the capabilities of a SAT solver. This was something everybody believed in 1972, but today is denied. Fundamentally "understanding" problems require the ability to consider multiple alternative interpretations of a situation, often choose one or work with the incomplete knowledge you have. Back in the 1970s we had intellectually honest people like Richard Dreyfus writing books like "Things Computers Can't Do" that describe many specific ways the architecture at the time fall short. People on GPT-3 are working in a way that is academically valid (able to make results that are meaningful to a community) but from engineering it is like building a bridge with one end or a tall tower that carries no load. GPT-3 has a structural mismatch with the domain it works in. Unlike early medical diagnosis systems like MYCIN, it is never a doctor, it just plays one on TV and it does the "passing for neurotypical" terrifyingly well. The secret of GPT-3 is that people want to believe in it. Somebody will have it generate 100 text snippets and they will show you the three best. Your mind makes up meaning to fill up for its mindlessness. When this was going on with ELIZA in 1965 people quickly understood that ELIZA was hijacking our instinct to make meaning. For some reason people don't seem to have that insight today, and it bothers me why that is. Back in the 1980s they had a lot of fear about compressing medical images because it could lead to a wrong diagnosis. Today you see articles in the press that are completely unquestioning that a neural network that has been trained to hallucinate healthy and cancerous tissues will always hallucinate the right thing when you are looking at a patient.

polytely 6y ago

I've been wondering about something: (I only know the basics of AI, this might be kinda incoherent) Right now if you look at GTP-3's output it seems like it's approaching a convincing approximation of a bluffing college student writing a bad paper, correct sentences and stuff but very 'cocky'. It cannot tell right from wrong, and it will just make up convincing rubbish, 'hoping' to fool the reader. (I know I'm anthropomorphizing but bear with me). Current models are being trained on a huge amount of internet text. As smarmy denizens of hackernews we know that people are very often wrong (or 'not even wrong') on the internet. It seems to me that anything trained on internet data is kinda doomed to poison itself on the high ratio of garbage floating around here? We've seen with a lot of machine-learning stuff that biased data will create biased models, so you have to be really careful what you train it on. The dataset on which GTP-n has to be trained has to be pretty huge(?); and moderation is hard(?) and doesn't scale; it's easier to generate falsehood than truth; and the further we go along the more of internet data will be (weaponized?) output of GTP-(n-1); So won't the arrival of AGI just be sabotaged by the arrival of AGI? Has anyone written something about the process of building AGI that deals with this?

tda 6y ago
> It would be a bit like how carbon dating or production of low background steel changed after 1945 due to nuclear testing https://news.ycombinator.com/item?id=23896293 https://news.ycombinator.com/item?id=23896293 I guess books published will be more useful than reddit rants (depending on the application)
deleted 6y ago
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lordnacho 6y ago
I've been wondering about something similar to you, but I read one of Pearl's causality books recently and thought that might be the missing piece. It's certainly impressive what GPT-3 can do, but it boggles the mind how much data went into it. By contrast a well-educated renaissance man might have read a book every month or so from age 15 to 30? That doesn't seem to be anywhere near what GPT could swallow in a few seconds. When you look at how GPT answers things, it kinda feels like someone who has heard the keywords and can spout some things that at least obscure whether it has ever studied a given subject, and this is impressive. What I wonder is whether it can do reasoning of the causality kind: what if X hadn't happened, what evidence do we need to collect to know if theory Z is falsified, which data W is confounding? To me it seems that sort of thing is what smart people are able to work out, with a lot of reading, but not quite the mountain that GPT reads.

sgt101 6y ago

Any one know how to test this yet?

legulere 6y ago

Nah we’re in for the next AI winter. GPT-3 shows how much energy is needed to perform a nice trick with current technology. We mostly have reached the limits of the technology. Investing more compute power for a few percentage points more Precision is not going to bring the technology forward.

pretendscholar 6y ago
If you look at the paper doesn't it scale well across many different metrics?
TomMarius 6y ago
It's a one time investment though, and not that large relatively speaking. Is is that much more costly and less rewarding than other investment opportunities?
Hypx 6y ago
Agreed. Nearly every AI startup or idea has disappointed or failed. We’re spending the equivalent of billions of dollars on something dumber than a rat in most cases.
Veedrac 6y ago
2017 SOTA on Penn Treebank was 47.69 perplexity. GPT-3 is at 20.5. AI has already been productized on consumer devices through Siri, Google Assistant, speech detection, speech generation, textual photo library search, similar data augmentations for web search, Google Translate, recommendation algorithms, phone cameras, server cooling optimization, phone touch screens' touch detection, video game upscaling, noise reduction in web calls, file prefetching, Google Maps, OCR, and more. DLSS alone justified continued investment by NVIDIA. NVIDIA Ampere will be ~6x as fast at running consumer-targeted models as Turing, given raw throughput increases compounded with sparsity and int8 hardware. A huge number of research threads around AI have direct applicability to large tech companies.
- legulere 6y ago
  I'm not arguing that current machine learning technologies are not useful. I'm just arguing that progress is based on increasing some metric, usually depending on a trade-off of computation. This can even make ML-techniques applicable to some new fields, but it's not what is holding back autonomous driving, the often touted parade example which also brings in a lot of employment for machine learning. This article clearly sits on the peak of inflated expectations in the hype cycle. https://en.wikipedia.org/wiki/Hype_cycle https://en.wikipedia.org/wiki/Hype_cycle

rvz 6y ago

ʸᵉˢ Explainability in AI is really overlooked and often skipped over as there is little progress in this area. GPT-3 is essentially GPT-2 + tons of data, compute and parameters and yet it still cannot explain itself as to why it can generate 'human-level' text, much like how AlphaGo can't explain why it performed move 37. Not discrediting these achievements, but explainability is just as important in these AI models. Once you have an AI-based 'auto-pilot' in any vehicle, the importance of AI explainability will haunt manufacturers when the regulators would want them to explain why this 'AI' took this decision and they're unable to explain this. I hope GPT-4 isn't just going to be GPT-3 + 1000x the data. Otherwise nothing would have changed here other than the parameters and data.

lambdatronics 6y ago
The easy solution is to copy what the human brain does: just make up something plausible. There's pretty good evidence that we don't have great introspective access to much of our own internal processing. We just paper it over as "intuition" or "judgement."
sbierwagen 6y ago
What does explainability mean for superhuman AI? We already see this in superhuman stock algorithms. You can "debug" them, in the sense that for a given trade, it can tell you what signals provoked it. But they don't make any sense: it saw rainfall in the Amazon tick up, the price of beef in Russia tick down, and the UK call a snap election, so it bought more GE stock. You could... theoretically... write a story that connected those dots, but it will either be facile or nonsensical. That's because the model of the market the algo has is bigger and more complete than anything a human can have. It's drawing a straight line through some upper-dimensional manifold that you can't comprehend. It can't explain what it's doing to you anymore than you can explain "algorithmic stock trading" to a three year old child. You can say what the outcome was, but you can't explain it in such a way that the kid could replicate the performance.

simonw 6y ago

I've been pandemic-rewatching Person of Interest. It's really quite shocking how much more relevant it feels today compared to when it aired just a few years ago. It's fun looking at things like GPT-3 and imagining how they could be used to build the surveillance AI at the heart of Person of Interest. (If you haven't watched Person of Interest yet, here's my pitch for it: it's a CBS procedural where the hook is that an engineer built a secret, surveillance feed tracking AI for the government after 9/11 - but he cared about civil liberties, so he built it as an impenetrable black box. All it does is kick out the SSN of someone who is about to be either the victim or the perpetrator of a terrorist attack - which means government agents still have to investigate what's going on rather than taking the AI's word for it. "The Machine" also sees victims/perpetrators of violent crimes - but the government don't care about those. Finch, the machine's inventor, does - so he fakes his own death, hooks into a backdoor into the machine that gives him those SSNs and sets up a private vigilante squad to help stop the violent crimes from happening. So that gives you the "case of the week". Only it's actually an extremely deep piece of philosophical science fiction disguised as a case-of-the-week procedural, and as time goes on the plots become much more about AI, the machine, attempts to build rival machines, AI ethics and so on. It's the best fictional version of AI I've ever seen. The creative team later worked on Westworld.)

Barrin92 6y ago
I honestly don't think the show has much to do with AI at all and is more like a retelling of the Greek classics in a sci-fi wrapping, which is actually something that comes up in the show at several points excplicitly. The AIs in the show very quickly turn into godlike characters with antropomorphic personalities and the real world issues of AI such as surveillance, economics and so on are all dealt with in very shallow fashion. I had the same issues with Westworld too. It turns from an AI premise into a classical Christian morality tale very fast. ("we need to suffer to become conscious").
- simonw 6y ago
  One of the things I loved about the show is that different characters have different philosophies concerning AI, and they argue about them. Nathan v.s. Fitch. Fitch vs. Root. Control, Greer - for the most part the show tried to give some depth and background to their thinking around the implications of what they were responsible for. Way smarter than you would expect from a CBS procedural!

g_airborne 6y ago

> The current hardware floor is nearer to the RTX 2080 TI's $1k/unit for 125 tensor-core TFLOPS, and that gives you $25/pflops-d. It's definitely true that the RTX 2080 Ti would be more efficient money-wise, but the Tensor Cores are not going to get you the advertised speedup. Those speedups can only be reached in ideal circumstances. Nevertheless, the article as a whole makes a very good point. The thing that is most scary about this is that it would become very hard for new players to enter the space. Large incumbents would be the only ones able to make the investments necessary to build competitive AI. Because of that, I really hope the author isn't right - unfortunately they probably are.

tomp 6y ago
OpenAI is kind-of a new player. Well-funded, but still - there's a lot of money available for this kind of exponential opportunities.

maerF0x0 6y ago

> so dropping $1bn or more on scaling GPT up by another factor of 100x is entirely plausible right now. I'd note it's rare that cost of scaling a computing project is a linear growth function. 100x-ing an AI project could be 1100x cost.

lacker 6y ago
For AI on this scale, there are two important costs. The cost of compute, and the cost of engineer salaries. Scaling the compute is probably a bit above linear due to various overheads, but the cost of engineer salaries is far less than linear, so I would expect the total cost of scaling large-scale AI to be sublinear.
- gwern 6y ago
  I would expect sublinear scaling in compute cost. Learning curve effects are strong in DL: https://cdn.openai.com/papers/ai_and_efficiency.pdf https://cdn.openai.com/papers/ai_and_efficiency.pdf The more runs you do, the cheaper they get. Plus, OA is no longer running on rented V100s, they have their own MS Azure supercomputer, remember (in fact, GPT-3's evaluation was interrupted because they moved to it), so they remove the enormous cloud margins.

deleted 6y ago

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lacker 6y ago

GPT-3 is the first AI system that has obvious, immediate, transformative economic value. I think the jury is still out on this one. It certainly seems powerful, it's doing interesting things, and it's better in many ways than any system that has come before. But there's a different between exciting demos and transformative economic value. It's too soon to be sure, but to me, the most interesting question is whether any valuable startups will be built on top of GPT-3. Some leading indicators before that are whether useful products are built on GPT-3, and whether early-stage startups built on GPT-3 get seed investment. I'm not aware of any of these yet but maybe latitude.io counts as one.

cl42 6y ago

This is a fascinating discussion -- I'm curious what people thing is the next step with AI? The post and several commenters here talk about how the tech in GPT-3 is "dumb" in that it's a big network but the network architecture is a fairly standard approach. I'm curious what people think are the next stages of AI research that companies are working on... Is it Probabilistic Graphical Models? Is it Probabilistic Programming? Is it knowledge graph extraction from text? Is it something else? Curious what people think...

ilaksh 6y ago
I think its about automatically building accurate and well-factored world models online that ultimately integrate not only high-dimensional sense data (such as visual information) but also language. This involves effectively solving the symbol grounding problem among other things. There is some serious effort in this direction in deep learning. There are also other efforts using different types of probabilistic programming as well as symbolic and neural net combinations. There's another link on one of the first few HN pages right now about dreaming. I think that dreaming gives one a lucid demonstration of some of the capabilities that we need to emulate if we are going to have human-like intelligence. AI will need to be able to visualize new situations, basically like on-demand, flexible simulations of mashed-up possibilities, involving things like physics and psychology etc. I think we almost need the AI to have something like a 3d gaming engine with physics, but also it can effortlessly conjure up AI agents in this simulation, but also, many of the physics rules and behaviors of the AI agents are automatically learned with only a few examples. This is the type of capability that allows humans (and some other animals) to adjust so readily to new situations. I speculate that there may be some representation or type of computation that has not been invented yet which facilitates both the simulation-type data and also the abstractions over it, all the way up to language, in a more seamless way than has so far been described. I saw a paper talking about the symbol grounding problem in terms of everything being categories, but really in the end it was broken down into something kind of like Lisp + probabilistic programming, and it seemed to not really have sufficient granularity to really do justice or properly integrate sense data. Certainly not in a seamless or truly unified way in my opinion. Although I guess I don't really understand category theory.

akyu 6y ago

I'm very heavily inclined to believe that yes we are. And my money is still on DeepMind.

ghostbrainalpha 6y ago
Can you link to the critical paper you are referencing?

01100011 6y ago

One thing I don't hear a lot of people talking about are ML/AI systems in the hands of government agencies. We know that the military and NSA are often ahead in many technologies but when it comes to AI the assumption seems to be that the industry is moving faster than the government. Is that really a safe assumption? The goverment is openly using autonomous systems to pilot drones, but what else are they leveraging AI for? Threat analysis? Logistics? Weapons optimization? PsyOps? The DoE is openly a very large consumer of GPUs. What about the military?

heavyset_go 6y ago
> Is that really a safe assumption? Not at all. The government can throw billions of dollars at a problem that, if solved, will never turn a profit or immediately benefit a business.
confeit 6y ago
You can get a glimpse by scrolling websites like: https://www.darpa.mil/opencatalog?ppl=view200&sort=title&ocFilter=software https://www.darpa.mil/opencatalog?ppl=view200&sort=title&ocF... [.mil] and looking at DARPA and Office of Naval Research sponsored ML/AI research. The military has been deeply involved with ML/AI research since its inception, and it is near impossible to avoid first - or second degree involvement, if active in ML/AI. The military wants: automated chat agents/web users that can be sent to dark web markets and hacker IRC channels and report back intelligence. Common sense inference from security and drone footage: predict who the killer is when watching a movie. Author deanonimization and cross-device tracking. Global-scale 99.9%+ accurate face detection. The Dutch Intelligence Agency organizes a yearly competition with difficult codes to crack. [1] It is rare for someone to answer all questions correctly. The answers require logic, creativity, common sense, linguistics, causal inference, spatial reasoning, expertise, analysis, and systematic thinking. I bet the military would be mighty interested in an automated problem solver for that. And mighty scared some other country gets there first. [1] https://www.aivd.nl/onderwerpen/aivd-kerstpuzzel https://www.aivd.nl/onderwerpen/aivd-kerstpuzzel

amelius 6y ago

Perhaps I missed it but what are some useful applications of GTP-3?

earthboundkid 6y ago
Promoting your SoundCloud account at the end of a Twitter thread.
ALittleLight 6y ago
For it "as is" - i.e. without imagining any new things, I would say you could make money from AI Dungeon, chat bot, and selling API access. I know that I badly want to play with the AI and would pay some amount per month to get some number of queries.
- ilaksh 6y ago
  AI Dungeon has a monthly fee now to upgrade to GPT-3 and a new much better engine. It works pretty well.

earthboundkid 6y ago

GPT-3 is a search engine pretending to be an AI.

deleted 6y ago
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haolez 6y ago

If I had the money and the dataset for training a model like GPT (that's a big if), is the code to implement such a thing trivial? Or is it a valuable proprietary asset of OpenAI as well as their instance per se?

zitterbewegung 6y ago
You would have to also reproduce their code which is the largest cost in any software development project . The code is non trivial but if you wait someone reimplements it . The dataset is also nontrivial because they probably cleaned the data which. It’s a valuable asset but it’s not like someone couldn’t reproduce it.

YeGoblynQueenne 6y ago

>> GPT-3 is the first AI system that has obvious, immediate, transformative economic value. To say the least, it is not immediately clear where that "transformative economic value" lies. From what I've seen so far GPT-3 can generate structurally smooth but completely incoherent text and despite claims to the contrary cannot perform anything close to "reasoning" [1]. It can also perform some side-tasks like machine translation and question answering, though with nowhere near good enough accuracy for it to be used as a commercial solution for these tasks. All this is not very useful or even interesting. Text generation is a fun passtime but unless one can control the generation to very precise specifications, to generate good quality text that makes sense on a particular subject, text generation is nothing but a toy with no commercial value (and even its scientific value is not very clear). And GPT-3's generation cannot be controlled to such precise specifications. We've had AI software that could interact intelligently with a user since the 1970's, with Terry Winograd's SHRDLU [2] and that never led to "immediate, transformative economic value", even though it was every bit the sci-fi-like AI program that could be directed by natural language to perform specific tasks with competence, albeit in a restricted enviroment (a "blocks world"). GPT-3 is not even capable of doing anything like that (nor are any other modern systems). How is a language model that is likely to respond with "blue offerings to the green god of mad square frogs" to a request to "place the blue pyramid on the red sphere" bring "transfomative" value? In fact, we've had systems capable of generating much more coherent (and still grammatically corret) text for some time [3] and even those have not caused a dramatic upheaval of "transformative economic value". I'm sorry but I'm afraid that, with GPT-3, we're again in a spiralling peak of hype, just as we were a few years ago with all the claims about sef driving cars "next year" etc. I think we all know how those panned out. In any case, you don't have to take my word for it. As with self-driving cars, all we have to do is wait a few years. Say, until 2024. We'll have a good idea about GPT-3's "transformative value" by then. __________________ [1] Unless of course one insists on Procrusteanising the definition of "reasoning" sufficently to cover essentially random guessing. [2] https://en.wikipedia.org/wiki/SHRDLU https://en.wikipedia.org/wiki/SHRDLU [3] I'll need to dig up some references if you ask, but in the meantime search for "story generation".

SeanLuke 6y ago

> GPT-3 is the first AI system that has obvious, immediate, transformative economic value. This statement is unbelievably ignorant of history. Just picking one random example out of a hat: planning and scheduling systems have had a profound impact on the manufacturing and shipping industries for many decades now.

f00zz 6y ago

In Kernighan and Pike's "The Practice of Programming" there's a chapter that covers the implementation in different languages of a random text generator using markov chains. It's a nice exercise and a lot of fun to play with. I'm guessing that not many people have read that book, because I'm seeing here and elsewhere even technical people talking about GPT-3 as if it's heralding the imminent advent of SkyNet. I get that transformers have a somewhat longer attention span than markov chains, but it's still a statistical language model. It can't even do the kind of planning or reasoning that early AI demos like SHRDLU could.

Jach 6y ago
It's a good book, and one most programmers should read early on (it grows less useful over time), but to think of this as only an incremental improvement over markov chains is underselling the advance. The technology is different, and can scale to much higher levels of capability. AGI levels? Almost certainly not. But it's passing usefulness thresholds so things can progress elsewhere.
- scottlocklin 6y ago
  >to think of this as only an incremental improvement over markov chains is underselling the advance. Erm, citations needed. It's a giant, inefficient and shitty KNN model, which is capable of mimicking markov chains. Wonderful marketing achievement and not much else.
  - Jach 6y ago
    https://www.gwern.net/GPT-3 https://www.gwern.net/GPT-3 (Edit: in case it's not clear, I suspect if you give an honest perusal of that page, and the pages it links, and the pages they link, you'll come away with a different opinion.)
Veedrac 6y ago
It's ridiculous to equate this to a traditional Markov chain language model. Here's something a Markov chain certainly cannot do: Human: I want to test your creativity. Please invent a new word and give its meaning. GPT-3: Ok. Um... Tana means to hit someone with the intention to wound them. Human: Please use the word tana in a sentence. GPT-3: You are about to tana the man attacking you. Human: Speak like a dwarf. GPT-3: I ain't talkin' like a dwarf. https://www.reddit.com/r/MachineLearning/comments/hvssqn/d_gpt3_demos/fyylreb/ https://www.reddit.com/r/MachineLearning/comments/hvssqn/d_g...

Pamar 6y ago

You know what? There are two people I'd really would like to interview about GTP-3 (and what an hypothetical GTP-4 or 5 could achieve). One is Hofstadter. The other is Ted Chang.

mijail 6y ago

Can anyone provide the background or framework of how I should see the business value of gpt3. Are there businesses that have a tremendous needs for the possibilities it provides? I've seen use cases that some NLG companies provide like sports and stock summaries but what world should I imagine where this is transformative?

eternalban 6y ago

Question: Is a collapse in learning time a possible breakthrough for future, or do we have definitive ~information theoretic bounds for says number of dimensions, etc.

andrewnc 6y ago

> GPT-3 is the first AI system that has obvious, immediate, transformative economic value. Seriously? No other piece of machine learning has had economic value? How short sighted.

mrfusion 6y ago

> GPT-3 is the first AI system that has obvious, immediate, transformative economic value. What is it’s economic value? What does it transform? I’ve been trying to figure that out since I heard about it. Anyone have any ideas?

crowbahr 6y ago
Theory based on some output I've read: It's good enough to actually start replacing a lot of customer service jobs. Not just being a shitty annoyance like current bots but being useful in that it will be as flexible as a human, directing you to good help via vague terms, potentially being smart enough to refer you higher up if necessary. Getting rid of all those screening call center employees is potentially very lucrative.