33 ms·

Artificial intelligence is losing hype

• scubadude 2y ago
  I'm still waiting for the Virtual Reality from 1996 to change the world. Colour me surprised that AI is being found to be 90% hype.
  • eesmith 2y ago
    Also from the 1990s, "intelligent agents". Here's what Don Norman wrote in 1994 at https://dl.acm.org/doi/pdf/10.1145/176789.176796 https://dl.acm.org/doi/pdf/10.1145/176789.176796 : > The new crop of intelligent agents are different from the automated devices of earlier eras because of their computational power. They have Turing-machine powers, they take over human tasks, and they interact with people in human-like ways-perhaps with a form of natural language, perhaps with animated graphics or video. Some agents have the potential to form their own goals and intentions. to initiate actions on their own without explicit instruction or guidance, and to offer suggestions to people. Thus, agents might set up schedules, reserve hotel and meeting rooms, arrange transportation, and even outline meeting topics, all without human intervention.
• megamike 2y ago
  tell me I am already bored with it next.....
• mrinfinitiesx 2y ago
  Good. It's decent for summarizing and giving me bullet points and explaining things like I'm 5, makes it easy to code things that I don't want to code or spend time figuring out how to do with new languages, other than that, I see no real world applications outside of listening to burger king orders and putting them on a screen for people to make them. Simple support requests, and of course making buzzword-esque documents that you can feed in to a deck-maker for presentations and stuff. All in all, it helps assist us in new ways. Had somebody take a picture of a car part that had no markings and it identified it, found the maker/manufacturer/SKU and gave all the details etc. That stuff is useful. But now we're looking at in-authentic stuff. Artists, writers being plagiarized, job cuts (for said marketing/pitches, BS presentations to downsize teams). It's not just losing its hype, its losing any hype in building humanity for the better. It's just more buzzwords, more 'glamour' more 'pop' shoved in our faces. The layoffs aren't looking pretty. Works well to help us code though. Viva, sysadmins unite.
  • parpfish 2y ago
    Im really hoping that when this hype cycle ends and the next AI winter starts that all the generative stuff gets culled but we still see good work and tech using all the other advances (that would be described as “mere” deep learning). Document embedding from transformers are great and fit into existing search paradigms. Computer vision and image segmentation is at a level I thought impossible 10 years ago. Text to speech that sounds natural? I might actually use Siri and Alexa! (Ok, that one might be considered “generative”)
    • janalsncm 2y ago
      The research never ended. AI money was flooding in, but mostly going directly to Nvidia. If that cash flow turns off there will still be research happening because it was mostly unaffected in the first place.
  • kombookcha 2y ago
    The hype dying off will be good for literally everybody except the investors. It'll mean fewer people trying to jam it into products as feature bloat where it has no business being, or trying to make it do tasks that it's unsuited for. The sooner people start to find it boring, the sooner we can stop wasting time on all the hot air and just use the bits that work.
• mensetmanusman 2y ago
  I’m just surprised something nearly replaced google in my lifetime.
  • PcChip 2y ago
    Kagi. Kagi replaced Google.
    • BeetleB 2y ago
      Hard to say they replaced them, when they use Google in their backend...
      • manuelmoreale 2y ago
        And they have 30K users and serve 600K queries a day while Google serves some 8.5B a day apparently. Love Kagi but they’re definitely not replacing Google anytime soon.
    • 0points 2y ago
      What a ridiculous claim.
      • PcChip 2y ago
        I meant they replaced google for me and lots of people I know and interact with daily
  • langcss 2y ago
    Google is now "a tool" not "the tool" for finding information. Perplexity and Phind do a good job and DDG is there for the privacy angle. In addition to LLMs just giving you the answer you need.
    • bamboozled 2y ago
      How on earths name do you use an LLM to find information ? I just don’t get it. For current events it out of date and it confidently feeds me shit ? I might use them occasionally for a rubber ducky but , replacing Google ? Hm
• jimjimjim 2y ago
  But what about all those organizations that have "Do something with AI" as the goal for the quarter? All those bonuses driving people to somehow add AI to products. All the poor devs that have been told to replace features driven by deterministic code with AI good-enough-ness.
• 11thEarlOfMar 2y ago
  Not until we've seen a plethora of AI startups go public with no revenue.
  • upon_drumhead 2y ago
    I'm not sure that is realistic anymore. The run of free money is over and I expected that markets are going to be very picky compared to a few years ago
    • freemoney99 2y ago
      I must have missed the memo. Where could I get the free money? "a few years ago" we had a global pandemic. Are you claiming that markets will be very picky compared to that time?
      • momoschili 2y ago
        I think you missed the memo during the pandemic then. That was the biggest supply of free money in a while for many industries.
        • pdimitar 2y ago
          Which ones? What did you have to say to get the free money?
          • deleted 2y ago
            [deleted]
          • deleted 2y ago
            [deleted]
      • rendang 2y ago
        It's just interest rates. 0 a few years ago, 5% today.
• ianbutler 2y ago
  This supposed “cycle” has been crazy it’s been about 1.5 years since gpt4 came out, which is really the first generally capable model. I think a lot of this “cycle” is media’s wishful thinking. Humans, especially humans in large bureaucracies, just don't move this quickly. Enterprises have barely had time to dip their toes in. For what it’s worth hype doesn’t mean sustainability anyway. If all the jokers go onto a new fad it’s hardly the skin off the back of anyone taking this seriously, they’ve been through worse times.
  • yieldcrv 2y ago
    I’ve had a lot of corporate clients this year Large and small, entire development teams are completely unaware of the basics of “prompt engineering” for coding, and corporate has an entirely regressive anti-AI policy that doesnt factor in the existence of locally run language models, and just assumes ChatGPT and cloud based ones digesting trade secrets. People arent interested in seeing what the hype is about, and are disincentived from bothering on a work computer. I’m on one team where the Engineering Manager is advocating for Microsoft CoPilot licenses, as in, its a concept that hasnt happened and needs buy in to even start considering. I would say most people really haven't looked into it. Work is work, the sprint is the sprint, on to the next part of the product, rinse repeat. Time flies for those people, its probably most of the people here.
    • nonethewiser 2y ago
      I think most people outside of tech have barely even touched it. Obviously there are some savy users across all age groups and occupations. But from what Ive see its just not part of most people’s workflow.
      • yieldcrv 2y ago
        At the same time I think Meta and big tech adding more and more cloud based inference is driving demand for the processors OpenAI still hasnt released Sora video prompting for the general public and have already been leapfrogged by half a dozen competitors. I would say its still niche, but only as niche as using professional video editing tools are for creatives
• signa11 2y ago
  can someone please post an archive link to this article ? thank you !
  • nblgbg 2y ago
    https://archive.ph/PFmWw https://archive.ph/PFmWw
• throwup238 2y ago
  https://archive.ph/PFmWw https://archive.ph/PFmWw
• atleastoptimal 2y ago
  [flagged]
  • 29athrowaway 2y ago
    At some moment people wanted to use radioactivity for everything, even marking cattle.
    • taberiand 2y ago
      And then it swung back too far in the other direction and nuclear anything became a bogeyman.
    • langcss 2y ago
      https://en.m.wikipedia.org/wiki/Atomic_gardening https://en.m.wikipedia.org/wiki/Atomic_gardening
      • 005vc16607 2y ago
        See also: https://en.wikipedia.org/wiki/Radium_Girls https://en.wikipedia.org/wiki/Radium_Girls
    • mdp2021 2y ago
      > At some moment people wanted to use radioactivity for everything And people died without a jaw because somebody sold radioactive water as a rejuvenator. Humanity is generally not strong on principles of carefulness.
    • atleastoptimal 2y ago
      It's different though. Did we have the ubiquitous implementation of nuclear power in only its nascent early days so widespread? To assume it will peter out the same way assumes the metaphor is 1:1, when I brought it up to imply simply that since a lot of developments are happening in secret now and companies are delaying releases for safety reasons, to the public is appears as if things are slowing. Even so, AI models are still far better and cheaper now than they were simply a year ago. We have simply gotten used to breakthroughs.
• ummonk 2y ago
  Whether and to what extent AI can be monetized is an open question. But there's no question that LLMs are already seeing extensive use in everyday office work and already making large improvements to productivity.
  • burnerquestions 2y ago
    I question it. Source?
  • hatefulmoron 2y ago
    > But there's no question that LLMs are already seeing extensive use in everyday office work and already making large improvements to productivity. Are you referencing something specific here, or is there something you can link to? To be honest the only significant 'disruption' I've seen for LLMs so far has been cheating on homework assignments. I'd be happy to read something if you have it.
    • ummonk 2y ago
      It’s purely anecdotal on my part but I have an ever increasing proportion of nontechnical acquaintances telling me how they discovered they can use ChatGPT to save large amounts of time drafting emails, writing reports, etc. (something which is a major part of work duties for many average office workers).
  • pdimitar 2y ago
    I and many others are questioning it. Please provide some proof. I've only seen some lazy programmers get boilerplate generated quicker, and some kids cheating on homework. I actually saw executives make use of ChatGPT's text summarization capabilities... until one of them made the critical mistake to fully trust it and flunked an important contract because ChatGPT overlooked something that would be super obvious to a human. So again, let's see some proof of this extensive use and large improvements to productivity.
  • asadotzler 2y ago
    Links to studies/surveys/interviews/anything with even the suggestion of proof for your claim other than simple assertion?
• h_tbob 2y ago
  To be honest, I was surprised by ChatGPT. I didn’t think we were close. We are running out of textual data now to train on… so now they have switched to VIDEO. Geez now they can train on all the VIDEOS on the internet. And when they finally get bots working, they will have limitless streams of TACTILE data… Writing it off as the next fad seems fun. But to be honest, I was shocked by what openai did the first time. So they have my respect. I don’t think many of us saw it coming. And I think writing their creativity off again may not be wise. So when they say the bubble is about to break… I get it. But I don’t see how. I hardly ever pay for anything. But I gladly spend money on ai to get the answers I need. Just makes my work work! Also I would say the economic benefit of this tech for workers is that it will 2x the average worker as they catch on. Seriously I am a 2x coder compared to what I was because of this. Therefore if me a person who hardly ever spends money has to buy it… I think eventually all businesses will realize all their employees need it. This driving massive revenue for those who sell it. But it may not be the companies we think.
  • slashdave 2y ago
    Good point about robots. But there will be a throughput issue. You cannot accelerate physical movement.
  • icholy 2y ago
    > Seriously I am a 2x coder compared to what I was because of this. You probably shouldn't advertise that.
    • rahimnathwani 2y ago
      They're a 20x coder now.
    • CooCooCaCha 2y ago
      I am highly skeptical that a competent coder sees a 2x boost.
      • CuriouslyC 2y ago
• taberiand 2y ago
  Sure it's not all it's cracked up to be but I sure hope there's a sweet spot where I can run the latest models for a cheap price ($20 / month is a steal), and it doesn't instead crash to the point where they get turned off
• freemoney99 2y ago
  These days you can't be a respected news outlet if you don't regularly have an article/post/blog about AI losing hype. Wondering when that fad will reach its peak...
• Ologn 2y ago
  > Since peaking last month the share prices of Western firms driving the ai revolution have dropped by 15%. NVDA's high closes were $135.58 June 18, down to $134.91 July 10th and $130 close today. It's highest sale is $140.76. So it's close today is 8% off its highest sale ever, and 4% off its highest close ever, not a big thing for a volatile stock. It's earnings are next week and we'll see how it does. Nvidia and SMCI are the ones who have been earning money selling equipment for "AI". For Microsoft, Google, Facebook, Amazon, OpenAI etc., it is all big initial capital expenditure which they (and the scolding investment bank analysts) hope to regain in the future.
• gorgoiler 2y ago
  Asking an API to write three paragraphs of text still takes tens of seconds and requires working internet and an expensive data center. Meanwhile we’re seeing the first of the new generation of on-device inference chips being shipped as commodity edge compute. When the devices you use every day — cars, doorbells, TV remotes, points-of-sale, roombas — can interpret camera and speech input locally in the time it takes to draw a frame and with low enough power to still give you 10h between charges: then we’ll be due another round of innovation. The article points to how few parts of the economy are leveraging the text-only API products currently available. That still feels very Web 1.0, for me.
• KingOfCoders 2y ago
  Which is great, the internet exploded when TV stopped talking about "the internet" and everyone just used it.
  • 0points 2y ago
    Right, I forgot that is why internet became popular /s
    • KingOfCoders 2y ago
      You confuse causality with correlation, a common mistake.
      • 0points 2y ago
        I don't. I was busy laughing at your teenage conclusion.
• ssimoni 2y ago
  Hilarious. The article tries to go even one step further past the loss of hype, by making an additional argument that ai might not be in a hype cycle at all. Meaning they conjecture that it might not even come out of the trough of disillusion to mass adoption. That’s gonna be a bad take I think.
• technick 2y ago
  I was out at Defcon this year and it was all about AI this, AI that, AI will solve the worlds problems, AI will catch all threats, blah blah blah blah...
  • plastic-enjoyer 2y ago
    I was at a UX / Usability conference and it was basically the same. Everyone talked about AI here and AI there, but no one had an actual usecase or idea how to incorporate AI in a purposeful way. I can genuinely understand, why people feel that AI is a fad.
  • bamboozled 2y ago
    I work with people like this. The least skilled, least experienced, least productive people on my team constantly recommend “AI” solutions that are just a waste of time. I think that’s what people like about AI, it’s hope, maybe you won’t have to learn anything but still be productive. Sounds nice ?
    • 0points 2y ago
      My clients are like this lately. Non techies that now are suggesting how I design solutions for them by asking ChatGPT. And they seem to treat me like the stupid one for refusing.
• olalonde 2y ago
  > Silicon Valley’s tech bros The Economist, seriously?
• keiferski 2y ago
  It’s certainly possible that AI is being overhyped, and I think in some cases it definitely is - but being tired of hearing about it in no way correlates to its actual usefulness. In other words, lot of people seem to think that human attention spans are what determine everything, but the technological cycles at work here are much much deeper. Personally I have used Midjourney and ChatGPT in ways that will have huge impacts on many activities and industries. Denying that because of media trendiness about AI seems shortsighted.
  • pdimitar 2y ago
    > It’s certainly possible that AI is being overhyped, and I think in some cases it definitely is - but being tired of hearing about it in no way correlates to its actual usefulness. Please tell that to all types on HN who downvote anything related to Rust without even reading past the title. :D > In other words, lot of people seem to think that human attention spans are what determine everything, but the technological cycles at work here are much much deeper. IMO no reasonable person denies this, it's just that the "AI" technology regularly over-promises and under-delivers. At one point it's no longer discrimination, it's just good old pattern recognition. > Personally I have used Midjourney and ChatGPT in ways that will have huge impacts on many activities and industries. Denying that because of media trendiness about AI seems shortsighted. Some examples with actual links would go a long way. I for one am skeptical of your claim but I am open to have my mind changed (f.ex. my CFO told me once that ChatGPT helped him catch several bad contract clauses).
    • keiferski 2y ago
      I don't understand how someone could think that ChatGPT or Midjourney aren't going to radically change many, many industries, and frankly to think this just seems like straight up ignorance or laziness. It's not that hard to find real examples of this stuff. But if you insist...here are two very small examples from my personal experience with AI tools. 1. I work as a technical writer. Recently I needed to add a summary section to the introduction of a large number of articles. So, I copied the article into ChatGPT and told it to summarize the piece into 3-4 bullet points. Were I doing this task a few years ago, I would have read each article and then written the bullet points myself – nothing particularly difficult, but very time-consuming to do for dozens of articles. Instead, I used ChatGPT and saved myself hours upon hours of mundane work. This is a quite minor and mundane example, but you can (hopefully) see how this will have major effects on any kind of routine text-creation. 2. I am working on a side project which requires the creation of a large number of custom images. I've had this project idea for a few years, but previously couldn't afford to spend $20k hiring an illustrator to make them all. Now with Midjourney, I am able to create essentially unlimited images for $30-100 a month. This new AI tool has quite literally unlocked a new business idea that was previously inaccessible.
• ChaitanyaSai 2y ago
  I've trained as a neuroscientist and written a book about consciousness. I've worked in machine learning and built products for over 20 years and now use AI a fair bit in the ed-tech work we do. So I've seen how the field has progressed and also have been able to look at it from a perspective most AI/engineering people don't -- what does this artificial intelligence look like when compared to biological intelligence. And I must say I am absolutely astonished people don't see this as opening the flood-gates to staggeringly powerful artificial intelligence. We've run the 4-minute mile. There are hundreds of billions of dollars figuring out how to get to the next level, and it's clear we are close. Forget what the current models are doing, it is what the next big leap (most likely with some new architecture change) will bring. In focusing on intelligence we forget that it's most likely a much easier challenge than decentralized cheap autonomy, which is what took the planet 4 billion years to figure out. Once that was done, intelligence as we recognize it took an eye-blink. Just like with powered-flight we don't need bioliogical intelligence to transform the world. Artificial intelligence that guzzles electricity, is brittle, has blind spots, but still capable of 1000 times more than the best among us is going to be here within the next decade. It's not here yet, no doubt, but I am yet to see any reasoned argument for why it is far more difficult and will take far longer. We are in for radical non-linear change.
  • limit499karma 2y ago
    Why are you throwing in 'consciousness' in a comment regarding mechanical intelligence?
  • juanani 2y ago
    [dead]
  • phito 2y ago
    > but I am yet to see any reasoned argument for why it is far more difficult and will take far longer I am yet to see any reasoned argument for why it is easy to build real AI and that it will come fast. As you said, AI has been there for decades and stagnated for pretty much the whole time. We've just had a big leap, but nothing says (except BS hype) that we're not in for a long plateau again.
• j_timberlake 2y ago
  They were writing pro-AI articles less than 2 months ago. They can just post AI-hype and AI-boredom articles so both sides will give them clicks. It's like an alternate form of Gell-Mann Amnesia that you're feeding.
  • pdimitar 2y ago
    Shockingly, people can change their minds.
• bufferoverflow 2y ago
  AI is not one thing at the moment. We have multiple systems that are being developed in parallel: • text generators • code generators • image generators • video generators • speech generators • sound/music generators • various robotics vision and control systems (often trained in virtual environments) • automated factories / warehouses / fulfillment centers • self-driving cars (trucks/planes/trains/boats/bikes/whatever) • scientific / reasoning / math AIs • military AIs I find all of these categories already have useful AIs. And they are getting better all the time. The progress might slow down here and there, but it keeps on going. Self-driving was pretty bad a year ago, and now we have Tesla FSD driving uninterrupted for multiple hours in complex city environments. Image generators now exceed 99.9% of humans in painting/drawing abilities. Text generators are decent. There are hallucination issues, and they are not creative at the best human level, but I'd say they write better than 90% of humans. When it comes to poetry/lyrics, they all still suck pretty badly. Video generators are in their infancy - we get decent quality, but absolutely mental imagery. Reasoning is the weakest point, in my opinion. Current gen models are just not good at reasoning. Sometimes they are brilliant, but then they make very silly mistakes that a 10-year old child wouldn't make. You just can't rely on their logical abilities. I have really high hopes for that area. If they can figure out reasoning, our science research will become a lot more reliable and a lot more fast.
  • asadotzler 2y ago
    [flagged]
  • skydhash 2y ago
    > Self-driving was pretty bad a year ago The threshold for acceptable self-driving is genuine effort from the automated system to avoid accidents as we can't punish it for bad driving. And I want auditable proof of that. > Image generators now exceed 99.9% of humans in painting/drawing abilities. I'm pretty sure the amount of people that can draw is less than that. And they can beat image generators by a mile as those generators are mostly doing automated matte painting. Yes copy-paste is faster than typing, but that's not write a novel. > Text generators are decent...but I'd say they write better than 90% of humans. Humans use language to communicate. And while there are bad communicators, I think lots of people are doing ok on that front. Text generators can be perfect syntax-wise, but the intent has to come from someone. And the produced text's quality is proportional to the amount of intent that it produces (that's why corporate language is so bland). > Video generators are in their infancy - we get decent quality, but absolutely mental imagery.* See Image Generator section, but in motion. > Reasoning is the weakest point, in my opinion... If they can figure out reasoning That's the 1-billion dollar question.
• moi2388 2y ago
  Well, maybe because people and companies still overwhelmingly seem to think LLMs == AI. AI ain’t going nowhere. And certainly isn’t overhyped. LLMs however, certainly are overhyped. Then again I find it a good interface for assistants and actual AI and APIs that it can call on your behalf
• castigatio 2y ago
  I think many things can be true at the same time: - AI is currently hyped to the gills - Companies may find it hard to improve profits using AI in the short term - A crash may come - We may be close to AGI - Current models are flawed in many ways - Current level generative AI is good enough to serve many use cases Reality is nobody truly knows - there's disagreement on these questions among the leaders in the field. An observation to add to the mix: I've had to deliberately work full time with LLM's in all kinds of contexts since they were released. That means forcing myself to use them for tasks whether they are "good at them" yet or not. I found that a major inhibitor to my adoption was my own set of habits around how I think and do things. We aren't used to offloading certain cognitive / creative tasks to machines. We still have the muscle memory of wanting to grab the map when we've got GPS in front of us. I found that once I pushed through this barrier and formed new habits it became second nature to create custom agents for all kinds of purposes to help me in my life. One learns what tasks to offload to the AI and how to offload them - and when and how one needs to step in to pair the different capabilities of the human mind. I personally feel that pushing oneself to be an early adopter holds real benefit.
  • jackhab 2y ago
    Can you give some examples of the tasks you did manage to offload successfully?
    • castigatio 2y ago
      - Emotional regulation. I suffer from a mostly manageable anxiety disorder but there are times I get overwhelmed. I have an agent setup to focus on principles of Stoicism and its amazing how quickly I can get back on track just by having a short chat with it about how I'm feeling. - Personalised learning. I wanted to understand LLM's at foundational technical level. Often I'll understand 90% of an explanation but there's a small part that I don't "get". Being able to deliberately target that 10% and be able to slowly increase the complexity of the explanation (starting from explain like I'm 5) is something I can't do with other learning material. - Investing. I'm a very casual investor. But I keep a running conversation with an agent about my portfolio. Obviously I'm not asking it to tell me what to invest in but just asking questions about what it thinks of my portfolio has taught me about risk balancing techniques I wouldn't have otherwise thought about. - Personal profile management. Like most of us I have public facing touch points - social media, blog, github, CV etc. I find it helpful to have an agent that just helps me with my thought process around content I might want to create or just what my strategy is around posting. It's not at all about asking the thing to generate content - it's about using it to reflect at a meta level on what I'm thinking and doing - which stimulates my own thinking. - Language learning - I have a language teaching agent to help me learn a language I'm trying to master. I can converse with it, adapt it to whatever learning style works best for me etc. The voice feature works well with this. - And just in general - when I have some thinking task I want to do now - like I need to plan a project or set a strategy I'll use an LLM as a thought partner. The context window is large enough to accomodate a lot of history - and it just augments my own mind - gives me better memory, can point out holes in my thinking etc. __ Edit: actually now that I have written out a response to your question I realise It's not so much offloading tasks in a wholesale way - its more augmenting my own thinking and learning - but this does reduce the burden on me to "think about" a range of things like where to get information or to come up with multiple examples of something or to think through different scenarios.
• justmarc 2y ago
  Maybe it's because people are finding out that it's actually not as intelligent as they thought it would be in its current iteration. The future is most definitely exciting though, and sadly quite scary, too.
• carlmr 2y ago
  I'm really wondering if we're going to see a lack of people with CS degrees a few years from now because of Jensen Huang saying AI will do all that and we should stop learning how to program.
  • sham1 2y ago
    Clearly Jensen is a genius and just ensured us infinite job security. Well, either that or he was just driving the hype b/c nvidia sells the shovels for the AI gold rush. Personally, I'd wager the latter.
• kkfx 2y ago
  ML is born in two master branches, one it's image manipulation, where video manipulation follow, another is textual search and generation toward the saint Graal of semantic search. The first was started with simple non-ML image manipulation and video analysis (like finding baggage left unmoved for a certain amount of time in a hall, trespassing alerts for gates and so on) and reach the level of live video analysis for autonomous drive. The second date back a very big amount of time, maybe with the Conrad Gessner's libraries of Babel/Biblioteca Universalis ~1545 with a simple consideration: a book is good to develop and share a specific topic, a newspaper to know "at a glance" most relevant facts of yesterday and so on but we still need something to elicit specific bit of information out of "the library" without the human need to read anything manually. Search engines does works but have limits. LLMs are the failed promise to being able to juice information (in a model) than extract it on user prompt distilled well. That's the promise, the reality is that pattern matching/prediction can't work much for the same problem we have with image, there is no intelligence. For an LLM if a known scientist (as per tags in some parts of the model ingested information) say (joking in a forum) that eating a small rock a day it's good for health, the LLM will suggest such practice simply because it have no knowledge of joke. Similarly having no knowledge of humans a hand with ten fingers it's perfectly sound. That's the essential bubble, PRs and people without knowledge have seen Stable Diffusion producing an astronaut riding a horse, have ask some questions to ChatGPT and have said "WOW! Ok, not perfect but it will be just a matter of time" and the answer is no, it will NOT be at least with the current tech. There are some use, like automatic translation, imperfect but good enough to be arranged so 1 human translator can do the same job of 10 before, some low importance ID checks could be done with electronic IDs + face recognition so a single human guards can operate 10 gates alone in an airport just intervening where face recognition fails. Essentially FEW low skill jobs might be automated, the rest is just classic automation, like banks who close offices simply because people use internet banking and pay with digital means so there is almost no need to pick and deposit cash anymore, no reasons to go to the bank anymore. The potential so far can't grow much more, so the bubble burst. Meanwhile big tech want to keep the bubble up because LLM training is a thing not doable at home as single humans alone, like we can instead run a homeserver for our email, VoIP phone system, file sharing, ... Yes, it's doable in a community, like search with YaCy, maps with Open Street Maps etc but the need of data an patient manual tagging is simply to cumbersome to have a real community born and based model that match or surpass one done by Big Tech. Since IT knowledge VERY lately and very limited start to spread a bit enough to endanger big tech model... They need something users can't do at home on a desktop. And that's a part of the fight. Another is the push toward no-ownership for 99% to better lock-in/enslave. So far the cloud+mobile model have created lock-in but still users might get data and host things themselves, if they do not operate computers anymore, just using "smart devices" well, the option to download and self host is next to none. So here the push for autonomous taxis instead of personal cars, connected dishwashers who send 7+Gb/day home and so on. This does not technically work so despite the immense amount of money and the struggle of the biggest people start to smell rodent and their mood drop.
• cs702 2y ago
  The OP is not about AI as a field of research. It's about whether the gobs of money invested in "AI" products and services in recent years, fueled by hype and FOMO, will earn a return, and whether we are approaching the bust of a classic boom-bust over-investment cycle. Seemingly every non-tech company in the world has been trying to figure out an "AI strategy," driven by hype and FOMO, but most corporate executives have no clue as to what they're doing or ought to be doing. They are spending money on poorly thought-out ideas. Meanwhile, every tech company providing "AI services" has been spending money like a drunken sailor, fueled by hype and FOMO. None of these AI services are generating enough revenue to cover the cost of development, training, or even, in many cases, inference. Nvidia, the dominant software-plus-hardware platform (CUDA is a big deal), appears to be the only financial beneficiary of all this hype and FOMO. According to the OP, the business of "AI" is losing hype, suggesting we're approaching a bust.
  • rifty 2y ago
    > Nvidia appears to be the only financial beneficiary It depends how you look at it. A lot of the spend by big tech can be seen as protecting what they already have from disruption. Its not all about new product revenues it’s about keeping the revenue share in the markets they already have.
  • dogcomplex 2y ago
    Investors need to stop looking at AI as a path to profits, and more as an enormous risk to the profitability of every other business. If AGI gets hit, it's probably not going to stay monopolized for long, nor is it going to be market neutral. Every business is going to need an AI infusion to compete, which many will get, cutting costs and raising efficiencies of each business in an ongoing competitive spiral to the bottom line - which... will be eventually measured in just: robotic labor, energy, and compute. The profit margins on all those businesses shrink to nothing as they become basically utilities. The net effect? Possibly a massive deflation, and "crashing" of the stock market, even as the total utility and value of the system skyrockets. This isn't a bull bet, it's a bear. AI would need to be perfectly monopolized to capture all the gains, and it's increasingly looking like that won't be the case - as all the component pieces are already open source at competitive levels, and any final architecture improvements that cross the final thresholds could be leaked in a 50GB file. Whoever gets to it first has a few months head start, at most, and probably not enough time or control to sell products - or shovels. After that it's a neverending race to zero, to the benefit of the consumer and the detriment of the investor. Nvidia is a great example case. They currently dominate the GPU market, an "essential hardware for AI", yet ternary asic chips specialized for transformer-only architectures are looking quite viable at 1999s tech levels. Wouldn't bet on that monopoly sticking around much longer.
• gennarro 2y ago
  I tried to do some AI database clean up this weekend - simple stuff like zip lookup and standardizing spacing, and caps - and ChatGPT managed to screw it ip over and over. It’s the sort of thing there a little error means the answer is totally wrong so I spent an hour refining the query and then addressing edge cases etc. I could have just done it all in excel in less with less chance of random (hard to catch) errors.
  • minkles 2y ago
    Similar experience. In fields I have less experience with it seems feasible. In fields I am an expert in, I know it's dangerous. That makes me worry about the applicability of the former and people's critical evaluation ability of the whole idea. I err on the side of "run away".
  • JamesBarney 2y ago
    If the SQL took you an hour to just clean up and you're an expert that is some pretty complex SQL. I could understand how it could get it wrong.
  • anon291 2y ago
    The point is that these problems will follow the same growth trajectory as every other tech bug. In other words, they will go away eventually. But the Rubicon is still crossed. There is a general purpose computer system that understands human language and can write real sounding human language. That's a sea change.
    • smt88 2y ago
      > will follow the same growth trajectory as every other tech bug What you're referring to isn't a bug. It's inherent to the way LLMs work. It can't "go away" in an LLM model because... > understands human language ...they don't. They are prediction machines. They don't "understand" anything.
      • potatoman22 2y ago
        What do you mean by understand?
• nerdjon 2y ago
  I feel like I have to disagree, even though I really don't want too. This technology is seriously overhyped. We have to realize that there is a ton of money right now behind pushing AI everywhere. We have entire conventions for leadership pushing that a year later "is the time to move AI to Prod" or "Moving past the skeptics". We have investors seemingly asking every company they invest in "how are you using generative AI" before investing. We have Microsoft, Google, and Apple (to a lesser degree) forcing AI down our throats whether we like it or not and ignoring any reliability (inaccurate) issues. FFS Microsoft is pushing AI as a serious branding part of Windows going forward. We have too much money committed to pushing the idea that we already have general AI, too much marketing, etc. Consumer hype and money in this situation are going to be very different things. I do think a bust is going to happen, but I don't think in any meaningful way the "hype" has died down. I think and I hope it will die down, we keep seeing how the technology just simply can't do what they are claiming. But I honestly don't think it is going to happen until something catastrophic happens, and it is going to be ugly when it does. Hopefully your company won't be so reliant on it to not recover.
• matrix87 2y ago
  > Silicon Valley’s tech bros are having a difficult few weeks. they need to find a different derogatory slur to refer to tech workers ideally one that isn't sexist and doesn't erase the contributions of women to industry
• j-a-a-p 2y ago
  TL;DR, article is not so much about AI, it is more about Gartner's hype cycle. According the Economist data only 25% of tech hypes follow this pattern. Many more (no percentage given) are just a flash in the pan. AI is following more a seasonal pattern with a AI Winters, can we expect a new winter soon?
• _acco 2y ago
  AI (specifically Claude Sonnet via Cursor) has completely transformed my workflow. It's changed my job description as a programmer. (And I've been doing this for 13y – no greenhorn!) This wasn't the case with GPT-4/o. This capability is very new. When I spoke to a colleague at Microsoft about these changes, they were floored. Microsoft has made themselves synonymous with AI, yet their company is barely even leveraging it. The big cos have put in the biggest investments, but also will be the slowest to change their processes and workflows to realize the shift. Feels like one of those "future is here, not evenly distributed yet" moments. When a tool like Sonnet is released, it's not like big tech cos are going to transform over night. There's a massive capability overhang that will take some time to work itself through these (now) slow-moving companies. I assume it was the same with the internet/dot-com crash.
  • ch4s3 2y ago
    What do you see as your biggest wins using Claude?
    • _acco 2y ago
      It helps me stay in flow by keeping me one layer up. In pair programming, it's ideal to have a driver (hands on keyboard) and a navigator (planning, direction). Claude can act as the driver most of the time so I can stay at the navigator level. This is so helpful, as it's easy as programmers to get sucked into implementation details or low-level minutiae that's just not important.
  • candiddevmike 2y ago
    How did it "change your job description"?
    • _acco 2y ago
      As I mentioned in a sibling comment, I now "pair program" all day. Instead of being the driver and navigator all day, I can mostly sit "one layer up" in the navigator seat.
• omnee 2y ago
  I just asked Google's Gemini the following question: Q: How many N's are there in Normation? A: There is one N in the word "Normation" Note that the answer is the same when asked n's instead of N's. And this is but one example of many simple cases demonstrating that these model are indeed not reasoning in a similar manner to humans. However, the outputs are useful enough that I myself use Claude and GPT-4o for some work, but with full awareness that I must review the outputs in cases where factual accuracy is required.
  • Chirono 2y ago
    This is just an artefact of tokenisation though. The model simply isn’t ever shown the letters that make up words, unless they are spelled out explicitly. It sees tokens representing groups of words. This is a little like saying a human isn’t intelligent because they couldn’t answer your question that you asked in an ultrasonic wavelength. If you’d like to learn more this video is a great resource: https://youtu.be/zduSFxRajkE?si=LvpXbeSyJRFBJFuj https://youtu.be/zduSFxRajkE?si=LvpXbeSyJRFBJFuj
  • AlexDragusin 2y ago
    Local instance of Llama 3.1 8B Q: How many N's are there in Normation? A: There are 2 N's in the word "Normation". Q: Confidence level? A: A more precise question! After rechecking, I can confirm that there is indeed 2 N's in the word "Normation". Confidence level: 100% Q: How many N's are there in the string "Normation"? A: There are 2 N's in the string "Normation". On several regenerations, at some point it gives: A: There are 3 N's in the word "Normation". Q: Confidence level? A: A slight challenge! After re-checking, I see that there are actually 2 N's in the word "Normation".
• julienchastang 2y ago
  As usual, when we see a thread on this topic on HN, the reactions tend to be bimodal: either "Yes, AI has transformed my workflow" (which is where I mostly fall), or "No, it's over-hyped." The latter often comes with an anecdote about how an LLM failed at a relatively simple task. I speculate that this diversity in opinion might be related to whether or not the user is employing a pro-tier LLM. Personally, I've been very impressed by ChatGPT-4 across a wide range of tasks, from debugging K8s logs to coding and ideation. I also wonder if some of the negative reactions stem from bad luck with an initial "first contact" with an LLM, where the results fell flat for any number of reasons (e.g., poor prompting), leading the user to conclude that it's not worth their time.
  • fragmede 2y ago
    You can usually tell that a lot of people just go off rumors they read once off Twitter or reddit or somewhere about hallucinations or doing math, against a weaker model, without every validating what they read online or updating their model of how well latest models work. Just have to learn to let it go, despite xkcd 386.
    • codexon 2y ago
      I use gpt4o and claude 3.5 while coding every day. The rumors on twitter and reddit are accurate. I constantly run into incorrect answers from the LLMs every day. Just recently I asked whether I needed to reverse the bit shift to mask the upper 24 bits in an IP address on a little endian platform and it incorrectly told me no probably because most of the answers on Google appeared to answer no to similarly phrased questions.
      • theaussiestew 2y ago
        That's probably the best we can do for now with LLMs. Surely it would be unreasonable for LLMs to provide a correct answer when the majority of people on various forums also provide the wrong answer. Some day LLMs will be able to objectively provide truthful statements but we're not there yet. Regardless, that LLMs are competing with longstanding programmers is already an impressive feat, even if they're not 100% correct.
• jdefr89 2y ago
  It’s hilarious seeing people getting LLMs to traditional takes traditional discrete algorithms do perfectly already. “Let’s use LLM to do basic arithmetic!” Like, that’s not what they are built for. We want more generalization… So much to unpack here and I’m tired of having to explain these basic things. You will know our models got more powerful if they can do something like solve the ARC challenge, not cramming it with new updated information we know it will already process a certain way…
• moridin 2y ago
  Good, maybe now we can focus on building killer apps rather than hype-posturing.
• i_got_censored 2y ago
  [flagged]
• janalsncm 2y ago
  “AI” never existed, at least AGI never did. AI that works is called machine learning and it’s not going away because it actually drives revenue at many companies. But the people who are working on that were working on it before blockchain and they’ll be working on it long after the next hype cycle runs out of steam. Unlike grifting, actual expertise takes time. I have mixed feelings. On the one hand, I have a ton of schadenfreude for the AI maximalists (see: Leopold Aschenbrenner and the $1 trillion cluster that will never be), hype men (LinkedIn gurus and Twitter “technologists” that post threads with the thread emoji regurgitating listicles) or grifters (see: Rabbit R1 and the LAM vaporware). On the other hand, I’m worried about another AI winter. We don’t need more people figuring out how to make bigger models, we need more fundamental research on low-resource contexts. Transformers are really just a trick to be able to ingest the whole internet. But there are many times where we don’t have a whole internet worth of data. The failure of LLMs on ARC is a pretty clear indication we’re not there yet (although I wouldn’t consider ARC sufficient either).
• rambojohnson 2y ago
  blah blah blah
• someonehere 2y ago
  It’s become an invaluable resource for my team in debugging scripts we’ve written for our services. There are a couple of third-party integrations that have been helping us greatly increase our release of features and fixes for problems in our company.
  • DaoVeles 2y ago
    This is why I feel like OpenAI and the public release of ChatGPT has probably done more damage than good. In trying to get ahead of everyone else, they have thrown a lot of great tech under the bus trying to get market dominance. It has made people lump all AI technology into a bubble regardless of it is functional or not. You are using this stuff to do some really cool things, but having hype attached to it can be very positive short term, damaging in the medium term and neutral long term. We are moving into the medium term.
• qo34j52o84j5 2y ago
  [flagged]
• robertlf 2y ago
  Why post an article that's behind a paywall? How many of us can read it?
• lz400 2y ago
  AI is a very strange thing where 2 seemingly smart coders use it and one comes out thinking it's obviously revolutionary and the other one thinking it's a waste of time and where 2 seemingly smart journalists use it and one thinks AGI and the end of the world is nigh and the other one thinks the market will crash when the hype dies over. I think part of it is due to the politically and internet-induced death of nuance. But part of it I can't fully understand. Personally I think it's rather useful. I don't consider myself a heavy user and still use it almost every day to help code, I ask it a lot of questions about specific and general stuff. It's partially or totally substituted for me: Stack Overflow, Google Search, Google Translate, most tech references. In the office I see people using it all the time, there's almost always a chatgpt window open in some of the displays. I think it's very difficult to say this is 100% hype and/or a "phase". It's almost a proven fact it's useful and people will want it in their lives. Even if it never improves again, ever. It's a new tool in the toolbox and there will be businesses providing it as a service, or perhaps we will get to open source general availability. On the other extreme, all the AI doomerism and AGI stuff to me seems almost as unfounded as before generative AI. Sure, it's likely we'll get to AGI one day. But if you thought we were 100 years away, I don't think chatgpt put us any closer to it and I just don't get people who now say 5. I'd rather they worried about the impact of image gen AI in deepfakes and misinformation. That's _already_ happening.
  • kaoD 2y ago
    > AI is a very strange thing where 2 seemingly smart coders use it and one comes out thinking it's obviously revolutionary and the other one thinking it's a waste of time My take on this is that those 2 developers are often working on very different tasks. If you're a very smart coder working in a large codebase with tons of domain knowledge you'll find it's useless. If you're a very smart coder working in a consultancy and your end result looks like a few thousand lines of glue code, then you're probably going to get a lot out of LLMs. It's a bit like "software engineering" vs "coding". Current iterations of LLMs is good for "coding" but crap at "software engineering".
    • lz400 2y ago
      there's probably truth to that, but I find it's useful at a more micro-level. I don't tell the llm to write an architecture or a big piece. It's more like, I have this data in this shape, I want a function that gives data out that shape and it will spit something pretty good and idiomatic. I read it, understand it and implement it. Need to be careful with blind copy-paste, there are sometimes subtle bugs in the code. It's specially useful when learning new frameworks, languages, etc. To me this is all applicable regardless of domain as the micro-level patterns tend to be variations of things that have been seen. I suspect if you try to load it with a lot of very specific high level domain logic, there are more chances of taking the llm out of its comfort zone.
• mikhael28 2y ago
  As long as Zuck keeps releasing open-source models, the moat will continue to disappear from these companies. Only expensive, corporate processing tiers will exist and everyone will run stuff locally. Not a lot of money to be made from local processing.
• naasking 2y ago
  Yes, hype happens because something new that can potentially be applied to many problems triggers lots of experimentation and discussion. Once people figure out the problems to which it's well-suited and ill-suited, experimentation and discussion die down and there's just application. Nothing to see here, this is standard and expected.
• mark_l_watson 2y ago
  Yes and no. Hype over ‘API wrapper’ projects and startups will crash a bit, I think. On the other hand we are no where near approaching hard limits on LLMs. When LLMs start to be trained for smaller subject areas with massive hand curated examples for solving problems, then they will reach expert performance in those narrow tech areas. These specialized models will be combined in general purpose MoEs. Then new approaches beyond LLMs, RL, etc. will be discovered, perfected, made more efficient. Seriously, any hard limits are far into the future.
  • mrmetanoia 2y ago
    Yeah I think there's an empty marketing driven hype that will die off, but I think we're going to start to see it continue to integrate into peoples real life workflows and competition's going to heat up in delivering more consistently reliable results. with regard to art AI, I think the debates are going to die off and the artists and people making stuff are going to just keep doing that, and some of them will use AI in ways that will challenge people in ways good art often does.
  • ikjasdlk2234 2y ago
    I agree. We're seeing great results for very narrow use cases using smaller LLMs too. It's no different than classical ML and emerging AI over the past 10 years. If you don't have a well scoped use case, you're not going to succeed. Now the one API wrapper projects that I love are my meeting transcription and summarization apps. You can tear those from my cold, dead hands.
• m3kw9 2y ago
  Hype is relative to your circle, where you are getting your info, and how the algorithm targets you with info that interests you. So yes, the hype is tiring for that Economist journalist, but for many they have not even heard or used it, and then there is everyone in between. As for myself there is hype but tongue seem justified based on how good the LLMs are currently
• bpiroman 2y ago
  I use ChatGPT almost everyday as a part of my coding work flow
• zelcon 2y ago
  Copium
• nbzso 2y ago
  Honestly. I am enjoying it. From Dave will replace you, to this is not working well in 6 months. A new record. Logically, everyone around me forgot that I patiently explained the limits of stochastic parrots and the false hope on synthetic data. If we lived in the remotely responsible place, some people would have their heads rolling down the stairs. The psychological damage over workforce from AI hype is comparable only with the negative effect of social networks on the society. :)
• kderbyma 2y ago
  Honestly, the only thing I have found somewhat useful with LLMs is to get smarter tab complete and to occasionally fill out small methods in classes, and finally writing unit tests and adding documentation. it saves me a little time on mostly improving test coverage and readability. But until I give it some examples it usually hallucinates even method names within the class that are very similar but slightly different and some of the time saved is lost by having to fix it's mistakes. I would say it's improving my LoC output by maybe 5-15% max, but the tab complete is nice when writing code.
• cleandreams 2y ago
  The problem is that current generative AI is not actually intelligent. Yann LeCunn had a great tweet on this: Sometimes, the obvious must be studied so it can be asserted with full confidence: - LLMs can not answer questions whose answers are not in their training set in some form, - they can not solve problems they haven't been trained on, - they can not acquire new skills our knowledge without lots of human help, - they can not invent new things. Now, LLMs are merely a subset of AI techniques. Merely scaling up LLMs will not lead systems with these capabilities. link https://x.com/ylecun/status/1823313599252533594?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Etweet https://x.com/ylecun/status/1823313599252533594?ref_src=twsr... To focus on this: - LLMs can not answer questions whose answers are not in their training set in some form, - they can not solve problems they haven't been trained on Given that we are close to maximum in the size of the training set, this means they are not going to improve without some completely unknown at the moment technical breakthrough. Going from "not intelligent" to "intelligent" is a massive shift.
  • jonahx 2y ago
    "in some form" is doing a lot of work there. The problem is that, by the standards of most human beings, they are in fact doing what we informally call "inference" or "creating new things". That this is being accomplished by something that is "technically a fancy autocomplete" doesn't seem to matter practically... it's still doing all this useful and surprising stuff.
    • intended 2y ago
      It is carrying lots of weight. At this moment that’s the most sophisticated we can be in talking about LLMs. I will say that the utility of these tools is not being denied. It’s just the struggle to explain the varied experiences. I can get only as far as analogy, not precise definitions. For me, LLMs are like the invention of microwave ovens. Very useful. They aren’t like the discovery of fire.
  • stevenhuang 2y ago
    Yea, LeCunn is kind of a meme. You're doing yourself and readers a disservice when you quote him without mentioning his conflict of interest. His research is in analytical approaches to ML hence his bitterness against current LLM techniques and skepticism towards Sutton's Bitter Lesson.
• e-clinton 2y ago
  Claude 3.5 is vastly better the 4o. I produce new features at a rate that’s 2-3x faster than I could without it. Not perfect and isn’t great in all use cases but overall transformational. I’ve been coding for 20+ years.
• kmarc 2y ago
  My employer PoC'd, collaborated with and eventually bought Codeium's solution. I couldn't care less about (any, so also neither about) the LLM hype. Especially didn't bother going to a new web site (ChatGPT), or installing new IDEs etc. I checked Codeium's mycompany-customized landing page: a one-liner vim plug-in installation and copy pasting an auth token. I started typing in the very same editor, very same environment, very same everything, and the thing just works, most of the time guesses well what I would want to write, so then I just press tab to accept and voila. I wasn't expecting such a seamless experience. I still haven't integrated its "chat" functionality into my workflow (maybe I won't at all). I'm not hyped about it, it just feels like a companion to already working (and correct) code completion. I read a lot about other people's usages (I'm a devXP engineer), and I feel like that for whatever reason there is more love / hype / faith on their chosen AI companion than how much they actually could improve if took a humble way of understanding code, reading (and writing) docs, reasoning about the engineering solution. As everything, now AI is losing hype, but somehow (in my bubble) seems like engineers are still high on it. But I also see that this will distill further the set of people who I look up to and want to collaborate with, because e of that mentioned humbleness, as opposed to just accepting text predicted solutions mindlessly.
  • antupis 2y ago
    > As everything, now AI is losing hype, but somehow (in my bubble) seems like engineers are still high on it. But I also see that this will distill further the set of people who I look up to and want to collaborate with, because e of that mentioned humbleness, as opposed to just accepting text predicted solutions mindlessly. It is dotcom bust again. Mainstream is losing interest but at the same time I see our internal chatbots / ai agents doing hockey stick growth and I am using several hours code pilot daily.
  • 0x008 2y ago
    I think we need to be very careful making blanket statements about the usefulness of certain LLMs. In my experience for every task another LLM is excelling and where one was good it might fail for the other task. They can do great things, but it’s not guaranteed and a lot of manual intervention and back and forth is still needed. We are not at the point where using AI in the company ist just a blanket win for everyone involved. Companies are investing a lot but the return is hard to measure and not always guaranteed. This is the problem with early technologies, they work sometimes but not guaranteed and we build our expectations on extrapolating their usefulness. We should not judge this technology by the current success rate, but rather by how much impact it will have once we get the success rate to higher and higher levels. Still, what we can say is that for certain occupations it already helps them reduce their work by 15% (software engineers) and probably even more for some (writers, product owners, office warriors and alike). This is a great achievement in of itself, think how much this will make up in a company as large as MS or Google.
• aussieguy1234 2y ago
  If anything, i'm getting more hyped up over time. Here are the things i've used LLMs for, with success in all areas as a solo technical founder. Business Advice including marketing, reaching out to investors, understanding SAFE notes (follow up questions after watching the Y Combinator videos), customer interview design. All of which, as an engineer, I had never done before. Create SQL queries for all kinds of business metrics including Monthly/Daily Active users, breakdown of users by country, abusive user detection and more. Automated unit test creation. Not just the happy path either. Automated data repository creation, based on a one shot example and MySQL text output describing the tables involved. From this, I have super fast data repositories that use raw SQL to get/write data. Helping with challenging code problems that would otherwise need hours of searching google or reading the docs. Database and query optimization. Code Review. This has caught edge case bugs that normal testing did not detect. I'm going to try out aider + claude sonnet 3.5 on my codebases. I have heard good things about it and some rave reviews on X/twitter. I watched a video where an engineer had a bug, described it to some tool (which wasn't specified, but I suspect aider), then Claude created a test to reproduce the bug and then fixed the code. The test passed, they then did a manual test and the bug was gone.
  • arcticbull 2y ago
    > Helping with challenging code problems that would otherwise need hours of searching google or reading the docs. I'm glad this has been working for you -- generally any time I actually have a really difficult problem, ChatGPT just makes up the API I wish existed. Then when I bring it up to ChatGPT, it just apologizes and invents new API.
    • chromanoid 2y ago
      Do you provide code examples? In my experience the more specific you get with your problem the more specific are the provided solutions (probably a "natural" occurence in LLMs). Hallucianted APIs are sometimes a problem for me, but then I just specify which API to use.
      • brailsafe 2y ago
• laichzeit0 2y ago
  That's great. Then don't use it? I however find it immensely useful and will continue to use it.
• meindnoch 2y ago
  The only time I found LLMs useful was creatig fake heartwarming stories to farm likes from boomers on Facebook.
• bulbosaur123 2y ago
  Same way mobile phones are losing their hype...they've become ubiquitous
• futureshock 2y ago
  This should really be retitled to “The AI investment bubble is losing hype.” LLMs as they exist today will slowly work their way into new products and use cases. They are an important new capability and one that will change how we do certain tasks. But as to the hype, we are in a brief pause before the election where no company wants to release anything that would hit the news cycle in a bad way and cause knee-jerk legislation. Are there new architectures and capabilities waiting? Likely some. Sora showed state of the art video generation, OpenAI has demoed an impressive voice mode, and Anthropic has teased that Opus 3.5 will be even more capable. OpenAI also clearly has some gas in the tank as they have focused on releasing small models such as GPT-4o and 4o mini. And many have been musing about agents and methods to improve system 2 like reasoning. So while there’s a soft moratorium on showing scary new capability there is still evidence of progress being made behind-the-scenes. But what will a state of the art model look like when all of these techniques have been scaled up on brand new exascale data centers? It might not be AGI, but I think it will at least be enough for the next hype Investment bubble.
  • dubcanada 2y ago
    So you’re suggesting all innovation/new functionality releases are paused because US has elections coming up? I find that hard to believe.
    • greg_V 2y ago
      "no no, it's not that the technology has reached it's current limits and these companies are bleeding money, they're just withholding their newest releases not to spook the normies!"
      • throwaway4aday 2y ago
        Here's an experiment you can try, go to https://www.udio.com/home https://www.udio.com/home and grab a free account which comes with more than enough credits to do this. Use a free chat LLM like Claude 3.5 Sonnet or ChatGPT 4o to workshop some lyrics that you like, just try a few generations and ask it to rewrite parts you don't like until you have something that you don't find too cringe. Then go back over to Udio, go to the create tab turn on the Manual Mode toggle and type in only 3 or 4 comma separated tags that describe the genre you like keep them very basic like Progressive Rock, Hip Hop, 1995, Male Vocalist or whatever you don't need to combine genres these are just examples of tags. Then under the Lyrics section choose Custom and paste in just the chorus or a single verse from the lyrics you generated and then click Create. It'll create two samples for you to listen to, if you don't like either of them then just click Create again to get another two but normally it doesn't take too many tries to get something that sounds pretty good. After you have one you like then click on the ... menu next to the song title and click Extend, you can add sections before or after and you just have to add the corresponding verse from the lyrics you generated or choose Instrumental if you want a guitar solo or something. You'll wind up with something pretty good if you really listen to each sample and choose the best one. Music generation is one of the easiest ways to "spook the normies" since most people are completely unaware of the current SOTA. Anyone with a good ear and access to these tools can create a listenable song that sounds like it's been professionally produced. Anyone with a good ear and competence with a DAW and these tools can produce a high quality song. Someone who is already a professional can create incredible results in a fraction of the time it would normally take with zero budget. One of the main limitations of generative AI at the moment is the interface, Udio's could certainly be improved but I think they have something good here with the extend feature allowing you to steer the creation. Developing the key UI features that allow you to control the inputs to generative models is an area where huge advancements can be made that can dramatically improve the quality of the generated output. We've only just scratched the surface here and even if the technology has reached its current limits, which I strongly believe it hasn't since there are a lot of things that have been shown to work but haven't been productized yet, we could still see steady month over month improvements based on better tooling built around them alone. Text generation has gone from markov chain babblers to indistinguishable from human written. Image generation has gone from acid trip uncanny valley to photorealistic. Audio generation has gone from 1930's AM radio quality to crystal clear. Video generation is currently in fugue dream state but is rapidly improving. 3D is early stages. ???? is next but I'm guessing it'll be things like CAD STL models, electronic circuits, and other physics based modelling outputs. The ride's not over yet.
• DebtDeflation 2y ago
  Things that are coming to an end: - Startups whose entire business model is to just provide a wrapper around OpenAI's API. - Social Media "AI Influencers" and their mindless "7 Ways To Become A Millionaire With ChatGPT" videos. - Non-technical pundits claiming we are 1-2 years from AGI (and AGI talk in general). - The stock market assigning insane valuations to any company that claims to somehow be "doing AI". Things that are NOT coming to an end: - Ongoing R&D in AI (and not just LLMs). - Companies at the frontier of AI (OpenAI, Anthropic, Mistral, Google, Meta) releasing ever more capable models and tooling around those models. - Forward looking companies in all industries using AI both to add capabilities to their products and to drive efficiencies in internal processes.
  • ksynwa 2y ago
    At the core of it, the high valuations were not a product of the potential LLMs had in assisting and enhancing individuals' works, rather because there was a hope that LLMs could replace some varieties of human labourers wholesale, thereby cutting labour costs which are often the highest expenditure of many companies. This has not materialised. I have seen more stories of PR nightmares out of attempting this than those with good endings. But maybe that is because of the sensationalist nature of news media itself. Either way if it is indeed a bubble that will burst at some point, it doesn't bode well for the tech industry. With the mass layoffs, which are ongoing, seems like there won't be enough jobs for everyone.
  • philipwhiuk 2y ago
    > - Forward looking companies in all industries using AI both to add capabilities to their products and to drive efficiencies in internal processes. This collapses as soon as this collapses > - The stock market assigning insane valuations to any company that claims to somehow be "doing AI".
    • kombookcha 2y ago
      I can't wait. There are so many pointless or directly insulting AI 'features' around now, and they suck. LinkedIn trying to write my messages for me. Outlook is trying to write my emails. Instagram has a deeply cursed AI button to "make it funnier". I hope the stock market will give all of these people an atomic wedgie for creating the most pointless garbage to ever pass before human eyes.
• gsky 2y ago
  It's the only one creating tech jobs at the moment
• rldjbpin 2y ago
  while this field is now paying my bills, i am lowkey happy to see this notion in the mainstream. > “An alarming number of technology trends are flashes in the pan.” this has been a trend that seems to keep on recurring but does not stop from the tech bros from pushing the marketing beyond the realities. raising money in the name of the future will give you similar results as self-driving cars or vr. the potential is crazy, but it is not going to make you double your money in a couple financial years. this should help serious initiatives find better-aligned investors.
• anonyfox 2y ago
  The sweet spot of the current LLMs (not whatever the next gen might or might not improve on) for me is similar to suddenly having an army of idiots at my fingertips. There are a lot of smallish tasks/problems people/systems needs to deal with, some of them even waste notable real engineering capacity, and a highschooler could do manually quite easily by hand. Example: find out if a text contains an email address, including all kinds of shenanigans people do to mask it (may not be allowd, ... whatever). From a purely coding standpoint, this is a cats-and-mouse game of improving regex solutions in many cases to also find the more sophisticated patterns, but there will always be uncatched/new ways or simply errors that produce false positives. But a highschooler can be given a text and instantly spot the email address (or confirm none is in there). In order to "solve" these types of small problems, LLMs are pretty much fantastic. It needs to only be reliable enough to produce a structured answer within a few attempts and cheap enough to not be a concern for finance/operations. Thats why for me it makes absolutely sense that the #1 priority for OpenAI since GPT4 has been building smaller/faster/cheaper models. Automators need exactly that, not genius-level AGI. Also for me I think we're not even scratching the surface still about many tasks can be automated away within the current constraints/flaws of LLMs (hallucination, accuracy, ...). Everyone tries to hype up some super generic powerful future (that usually falls flat after a while), whereas the true value of LLMs is in the many small things where hardcoding solutions is expensive but an intern could do it right away.
  • ekabod 2y ago
    The problem is that these idiots are very expensive for now.
    • anonyfox 2y ago
      indeed, but prices have been falling dramatically in the last 2 years, and I think the latest smallest gpt4o-mini is already in the "mostly don't care" ballpark. I am happy if we somehow get this down even more orders of magnitude, to the point where I can `npm install llm` and have it run alongside my normal code on a $5 VPS, without a GPU, and still handling (resonable) requests/minute with it. Yes I know we are _very far_ from that still, but one can dream.
• yawboakye 2y ago
  > artificial intelligence is losing hype. among which audience? is the hype necessary for further development? we attained much, if not all, of the recent achievements without hype. if anything, i'm strongly in favor of ai losing all the hype so that our researchers can focus on what's necessary, not what will win the loudest applause from so fickle a crowd. i'd be worried if ai was attracting less researchers than, say, two or three years ago. that doesn't seem to be the case.
• nottorp 2y ago
  https://en.wikipedia.org/wiki/AI_winter https://en.wikipedia.org/wiki/AI_winter Those who do not know history are doomed to repeat it. But then, the current hype wasn't there to produce something useful, but for "serial entrepreneurs" to get investor money. They'll just move to the next hyped thing.
• pif 2y ago
  The most useful ChatGPT has been for me consisted in teaching me some nice recipes for elk eggs. For the record, before spelling the recipes out, it made sure I understood that collecting elk eggs may be unlawful in some jurisdictions.
• dbrueck 2y ago
  For me, the most amazing thing about LLMs is translation between written languages (human, not programming). I can only speak to the translation between English and Spanish on everyday topics, but ChatGPT often produces translations that are near native speaker quality, and even when it doesn't, the results are almost always far, far above the "good enough to communicate clearly" threshold. It's incredible.
• Kuinox 2y ago
  Why is there no journalist name on this article ?
  • majewsky 2y ago
    That's house style for the Economist.
    • Kuinox 2y ago
      Thanks, didn't knew.
• dwighttk 2y ago
  Good… it’s all hype
• zombot 2y ago
  Such bad timing! As I was just about to replace my dentist, my oncologist, my GP, my tax attorney, and my investment banker with CrapGPT, expecting baldness and cancer to be cured by tomorrow, not to mention a get-rich-quick scheme for everybody and their grandmother. Son, I am disappoint.
• bentt 2y ago
  All we need to see is a computer that operates itself according to what you ask it to do, and the hype will be back. For some reason nobody's really showing this, but it seems obvious. Maybe it's too dangerous.
• XCSme 2y ago
  I think text-to-SQL is quite cool and works reasonably well.
• black_13 2y ago
  [dead]
• iainctduncan 2y ago
  The real take away from this article is that the Gartner hype cycle is bullshit.
• ryoshu 2y ago
  Good. Time to build.