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I maintain that LLMs are great for a few things: * Making stuff up. If you need bulk fiction writing then LLMs are useful (or marketing copy, etc.) * Transfor

• ljlolel 4y ago
  Don’t forget text expansion. Fluffing up some text into longer text (similar to the first point). Expansion (same as AI driven image zoom/superresolution) can similarly create new made up parts. Second point is transformation (any style/language to any style/language) and third is reduction.
  • teaearlgraycold 4y ago
    Yes, although I don't think that's the best use of an LLM. I worked for a company that specialized in doing that for marketing purposes.
• spullara 4y ago
  Translating almost any language to any other language.
  • prmph 4y ago
    So, with ChatGPT, is machine translation finally fully solved?
    • svachalek 4y ago
      Maybe not quite ChatGPT 2023 but it looks like from here it's just a matter of refinement. Someone may always create a better toothpaste, but I don't expect to ever really be surprised and impressed by a new toothpaste. ChatGPT is in the same place as a translator.
    • waboremo 4y ago
      No, not fully. ChatGPT and Google Translate/DeepL are about equal. This is for a couple reasons: machine translations are very good these days, and ChatGPT struggles with domain specific translations (whereas these commercial products have incentive to improve within). It's marginally better at sounding natural with translations though. "In fact, the frequent use of soap is bad for the skin." (chatgpt) vs "In fact, using soap too often is bad for your skin." (google)[1] [1] https://arxiv.org/abs/2301.08745 https://arxiv.org/abs/2301.08745
      • famouswaffles 4y ago
        I know what the paper says but actually using both, GPT-4 is far ahead of google and Deepl. I think the isolated one sentence datasets used for evaluations are no longer up to snuff. Trying something longer and more comprehensive and the difference is very clear. https://github.com/ogkalu2/Human-parity-on-machine-translations https://github.com/ogkalu2/Human-parity-on-machine-translati...
        • waboremo 4y ago
          Yes, that's where the improvements to sounding natural really compound.
      • jiggawatts 4y ago
        There is no comparison in my experience, especially when going between wildly unrelated languages instead of Latin-derived European languages. I tested some complex sentences translated from English to Hungarian to Chinese and then back to English and it preserved 90-95% of the meaning. Google Translate will shred anything that goes through those kinds of steps. It can also translate to Latin, which shouldn't be surprising but was still a bit of a shock when I tried it. It's Elvish and Klingon is not so good however, the round-trip only vaguely preserves the meaning.
  • spullara 4y ago
    [flagged]
• nyolfen 4y ago
  we are all in a bit of a bubble but i feel like if you put gpt4 up against the median man on the street it would be better at literally everything, except maybe one or two things that person specializes in
  • goatlover 4y ago
    Since you said literally, it wouldn't be better than: 1. Personal interactions since GPT4 doesn't know the median man's social circle and doesn't have a body. 2. Nearly everything after September 2021. 3. Manipulating and navigating the world including driving cars and everything else humans do a thousand times a day since it doesn't have a body.
    • airstrike 4y ago
      Sadly "literally" can literally mean "figuratively" nowadays
  • teaearlgraycold 4y ago
    Can you give a few examples of "literally everything"?
    • nyolfen 3y ago
      most tasks that could be performed via textual discourse. math is the big exception here but gpt4 has been capable of most things i’ve thrown at it at a level on par with a smart person
  • TaylorAlexander 4y ago
    I am not a researcher in this field but I suspect there is a huge list of things that a normal person can do that an LLM cannot. For example if you tell GPT4 something it did not know, it cannot tell you which parts it found surprising - it instead tells you it is incapable of being surprised.
    • brookst 4y ago
• Gigachad 4y ago
  I've been pasting comments in to ChatGPT and asking what they mean by certain terms or phrases and it works extremely well and with an answer I can independently verify to be true. Far more useful than getting a simple dictionary lookup. An example response I got recently: >In the context of this comment, "dovish" means that the speaker perceives Powell's statement to be more accommodative towards economic growth and less concerned about inflation than Lowe's statement. This suggests that Powell may be more inclined to keep interest rates low or lower them further to stimulate economic growth, rather than raise them to combat inflation. The term "dove" is often used to describe policymakers who prioritize economic growth over inflation concerns. In contrast, "hawkish" refers to policymakers who prioritize fighting inflation over economic growth. Meanwhile google gives me this response for a defintion >Definitions of dovish. adjective. opposed to war. synonyms: pacifist, pacifistic peaceable, peaceful. not disturbed by strife or turmoil or war.
  • teaearlgraycold 4y ago
    Yes. And that gets into the awkward bit. LLMs are incidentally good as databases/search engines for information that is prevalent on the internet and not disputed. Ideally we could separate the LLM from the information. So when asking it for a definition you'd provide your query, the source comment, and then the computer would add in the definition and some uses in context. Right now we've tangled an understanding of language and a corpus of information together in a way that causes distrust in AI. If the AI gets some fact wrong (like Bard did when demo'd earlier this year) people laugh and think LLMs are a failure. They should not be used for information recall. That is a task that a vector database would be good at.
    • svachalek 4y ago
      Yes, I think this explains a lot of skeptics. They look at ChatGPT as an information repository, as so it is impressive but flawed, while looking right past its incredible facility with language which is positively superhuman. Some humans can exceed its language skills on some details, but no one can speak so many languages (both natural and programming languages) with the ease and facility of ChatGPT.
• deleted 4y ago
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• hasmanean 4y ago
  They’re API: Automated pseudo intelligence. The only people impressed by them are pseudo-intellectuals. The only jobs they will replace are jobs that required medium intelligence.
  • flangola7 4y ago
    The majority of jobs in the world require medium or lower intelligence. Historically at 20-40% unemployment wars start and country leaders are physically disassembled in the street.
    • oblio 4y ago
      > Historically at 20-40% unemployment wars start and country leaders are physically disassembled in the street. Close, but not quite. At 20-40% unemployment and with people <<starving>> and <<physically insecure>>, wars start.
      • flangola7 4y ago
        That seems like distinguishing without a difference. At 40% unemployment starvation and lack of physical security are practically a given.
        • oblio 3y ago
          No, they aren't. Spain and Greece have had... https://tradingeconomics.com/spain/unemployment-rate https://tradingeconomics.com/spain/unemployment-rate https://tradingeconomics.com/greece/unemployment-rate https://tradingeconomics.com/greece/unemployment-rate About 30% each. Yes, it was hard, but society didn't collapse.
    • EVa5I7bHFq9mnYK 4y ago
      Isn't that the usual figure of unemployment rate in Spain? They sure like to protest, but have not started any wars recently.
  • the_jeremy 4y ago
• xvilka 4y ago
  Add here language learning. Arguably, it's the best fit application for large language models.
• mxkopy 4y ago
  Transformers originally were made for language translation. So the way I think about it, GPT models translate questions to answers. Hence the hallucinations - some questions can't be answered by just associative reasoning and pattern matching.
• potatolicious 4y ago
  This is correct. I sometimes feel like I'm yelling into a hurricane when I say this, but it bears repeating: it's a language model, not a world knowledge model. LLMs are exceptionally good at transforming many aspects of language - its proficiency in coding is derived from this, not because it "knows" imperative logic. Tasks where you're asking it to transform text from one form to another (make it shorter, make it longer, make it a different language, etc.) are where it excels. It's particularly poor at knowledge retrieval (i.e., hallucinations galore) and very bad at reasoning - but so far all of the breathless hype has been specifically about the use cases it's bad at and rarely about the cases where it's amazing!
  • teaearlgraycold 4y ago
    In some ways I'm glad people are unable to understand how to use them. It gives me a competitive advantage.
  • marviel 4y ago
    (EDIT: I'm understanding you to say "there is no world model in there". This was written as a counterpoint to that.) Counterpoint by example: Imagine someone reads the first half of the original article, and then closes their eyes and (without peeking) writes both: (1) The rest of the article, verbatim (2) All of the hacker news comments on this article's posting here, including yours, and this one. If this person existed, would they not have built a very successful world model?
  • famouswaffles 4y ago
    LLMs are not very bad at reasoning. People say this but struggle to demonstrate how. There are many types of reasoning. For some like abstract analogical reasoning, LLMs are top % performers. https://arxiv.org/abs/2212.09196 https://arxiv.org/abs/2212.09196 There's many papers at this point demonstrating pretty strong reasoning. From Casual Reasoning, and explanatory reasoning to algorithmic reasoning. https://arxiv.org/abs/2305.00050 https://arxiv.org/abs/2305.00050 https://arxiv.org/abs/2204.02329 https://arxiv.org/abs/2204.02329 https://arxiv.org/abs/2211.09066 https://arxiv.org/abs/2211.09066 The only kind of reasoning that's somewhat lacking is logical reasoning. And even then it's not awful, just lagging behind equivalent human performance. So someone really needs to tell where this oft repeated "can't reason" rhetoric is coming from. Because the numbers sure as hell don't support it.
• deleted 4y ago
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• kristopolous 4y ago
  I use it 90% for entertainment. In improv theater there's something called "yes and" - essentially you take the premise father, no matter how absurd, without redirection. You can come up with the most ridiculous things and it just goes with it, hilariously. I'll come up with one on the spot. "I'm having a very difficult time with my pet snails being social. I'm thinking of starting a social networking site and giving snails tiny phones so they can chat with each other. I need a company name, an elevator pitch, and some copy for my landing page." And then you ask it to do I dunno, NFTs and crypto currency for snails, give them tiny VR headsets. Have it come up with a jingle and a commercial. You can say instead of unfriending you salt them. Etc... It'll just keep going. Even "A snails rights luddite group of Mennonites and Amish are now protesting my idea. I need a way to appease my critics. Can you write a letter for me that defends snailconnect as healthy and good?" One of my favorite outputs from this session "How about SnailConnect; a small trail for snail, one giant leaf for snailkind." You don't ever get to a place where it's like "well now you're just being ridiculous" But I agree. It's just a big data version of Eliza - spitting my reflection back at me
  • teaearlgraycold 4y ago
    I think it’s fun to use as a way to write Star Trek fan fiction. It can get pretty witty.
• jillesvangurp 4y ago
  Actually they are also getting good at using tools, generating working code for common problems, helping you explore vast bodies of published information (limited by what it has been trained on obviously, particularly Bing chat seems good at this), and a few related topics. Limiting factors are mainly interfacing with other systems and tools, expanding the training data to include stuff you need (e.g. up to date documentation for whatever you are using vs. the two years out of date stuff it was trained on). This is more a limitation of UX (chat) than it is a limitation of the underlying model. It's weak on logic problems, math, and a few other things. But then a most people are also not very good at that and you could use tools for that (which is something chat gpt 4 can do). And people hallucinate, lie, and imagine things all the time. And if you follow US politics, there have been a few amusingly bad examples on that front in recent months. To the point where you might wonder if some politicians are using AI to write their speeches; or would be better off doing so. It's our ability to defer to tools and other people that makes us more capable than each of us individually is. Individually, most of us aren't that impressive. Even a few years ago, the notion that you could have a serious chat via a laptop with an AI on just about any topic and get coherent responses would have been science fiction. Now it is science fact. Most of the AIs from popular science fiction movies (Space Odyssey, Star Trek, Star Wars, etc.) are actually not that far of from what chat GPT 4 can do. Or arguably a bit retarded even (usually for comedic effect). You can actually get chat gpt to role play those.