4 ms·

It's worth pointing out that on their eval set for "issues resolved" they are getting 13.86%. While visually this looks impressive compared to the others, anyth

• beauzero 3y ago
  "To really solve novel problems with LLMs will take a large amount of research, experimentation and prototyping of ideas, but people funding this hype have no patience for that. I fear we'll get hit by a major AI winter when investors get bored, but we'll end up leaving a lot of value on the table simply because there wasn't enough focus and patience on making these incredible tools work." ...this is what happened in 99-2000. It took 3-7 years for the survivors to start making it usable and letting the general public adjust to a new user paradigm (online vs on PC).
• NicoJuicy 3y ago
  Don't forget the 20/80 rule. They haven't even gotten to 15% yet. Our jobs are safe. I would even expect more "beginners" to try something with AI and then need an actual programmer to help them ( At least, if they are unwilling to invest the time in development and debugging themselves Ps. Probably all the given examples are in top 3 most popular programming languages.
  • emporas 3y ago
    Machines currently are at an amateur level, but amateurs across the board on the knowledge base. Amateurs at Python, Fortran, C, C++ and all programming languages. Amateurs at car engineering, airplane engineering, submarine engineering etc. Amateurs at human biology, animal biology, insect biology and so on. I don't know anyone who is an amateur at everything.
    • zeroonetwothree 3y ago
      What does it mean exactly to be an amateur at "submarine engineering"? It certainly doesn't mean you know how to build a submarine.
      • sarkhan 3y ago
        Perhaps, amateur could engineer a submarine that goes and stays at 10 feet deep? And it does not carry a nuc load?
        • HarHarVeryFunny 3y ago
          Maybe there's a future for AIs designing narco-subs ?
      • emporas 3y ago
        They cannot make a submarine themselves, or design it, but when they reach 50 percent, they will reach 50% at everything. In submarine engineering, they will be able to design and construct it in some way like 3d print it, and the submarine will be able to move into the water for some time before it sinks. Yeah, probably for submarines a higher percent should be achieved before they are really useful.
• avip 3y ago
  Thanks, insightful comment.
• ThalesX 3y ago
  > The problem with this entire space is that we have VC hype for work that should ultimately still be being done in research labs. I also have two crypto-bro friends that are hyping it up without having anything to show for it. Which is why I'm sort of complaining about they hype surrounding it. I agree with your post to a large extent. This is not production ready technology. Maybe tomorrow.
  • mediaman 3y ago
    LLMs are quite good at text based tasks such as summarization and extracting entities. These generally don't require advanced logic or thought, though they can require some moderate reasoning ability to summarize two slightly conflicting text extracts. Lots of corporate work would be enhanced by better summarization, better information dissemination, and better text extraction. Most of it is pretty boring work, but there's a lot of it. VC hypes seem to want to mostly focus on fantastical problems, though, which sound impressive at dinner parties but don't actually work well. If you're a VC, do you want to talk about your investment in a company that finds discrepancies in invoices, or one that self-writes consumer iPhone apps? Only one of those is actually doable today.
    • ThalesX 3y ago
      Summarize this for me please: https://www.nytimes.com/2024/02/25/world/europe/cia-ukraine-intelligence-russia-war.html https://www.nytimes.com/2024/02/25/world/europe/cia-ukraine-...
      • Jensson 3y ago
        HN doesn't allow posting AI content, but I tried pasting that in gemini and it did fine. Saw no errors, maybe it missed some important details but everything I checked matched the article and those details seemed like a good summary. Here is what it wrote, didn't have enough tokens for the last 20% of the article though: A Longstanding Partnership: The collaboration began in 2014 after the pro-Russian government was ousted in Ukraine. The CIA was initially cautious due to concerns about trust and provoking Russia. Building Trust: Ukrainian intelligence officials gradually earned the CIA's trust by providing valuable intel, including on Russia's involvement in the downing of MH17 and election interference. Hidden Network: The CIA secretly funded and equipped a network of 12 spy bases along the Ukrainian border used for intelligence gathering. Training and Operations: The CIA trained Ukrainian special forces (Unit 2245) and intelligence officers (Operation Goldfish) for missions behind enemy lines. Friction and Red Lines: The Ukrainians sometimes disregarded CIA restrictions on lethal operations, leading to tensions but not severing the partnership. Current Importance: This intelligence network is now crucial for Ukraine's defense, providing critical intel on Russian troop movements and enabling long-range strikes.
• ramesh31 3y ago
  > I've personally seen teams working nights and weekends, implementing solutions to never before seen problems in a few weeks, and still getting a thumbs down when they cross the finish line. This is an important lesson that all SWEs should take to heart. Nobody cares about your novel algorithm. Nobody cares about your high availability architecture. Nobody cares about your millisecond network latency optimizations. The only thing that anyone actually using your software cares about is "Does the screen with lights and colors make the right lights and colors that solve my problem when I click on it?". Anything short of that is yak shaving if your role is not pure academic R&D.
  • dukeyukey 3y ago
    I wish this were the case. The amount of time I spend trying to talk principal engineers out of massive refractors because we want to get this out soon is near criminal.
    • HarHarVeryFunny 3y ago
      Sure, there's a tendency, even among relatively senior developers, to want to rewrite things to make them better, and it's certainly faster to put a band aid on it if you need to ship something fast. The thing is though that technical debt and feature creep (away from flexibility anticipated by original design) are real, and sometimes a rewrite or refactor is the right thing to do - necessary so that simple things remain simple to add, and to able to continue shipping fast. It just takes quite a bit of experience to know when to NOT rewrite/refactor and when to do it.
  • catchnear4321 3y ago
    sounds like you could use to shave a yak.
• HarHarVeryFunny 3y ago
  Agreed, and I think that many of the problems that people think LLMs will become capable of, in fact require AGI. It may well turn out that LLMs are NOT the path to AGI. You can make them bigger and better, and address some of their shortcomings with various tweaks, but it seems that AGI requires online/continual learning which may prove impossible to retrofit onto a pre-trained transformer. Gradient descent may be the wrong tool for incremental learning.
  • renonce 3y ago
    At least in theory we can achieve incremental learning by training from scratch every time we got some new training data. There are drawbacks with this approach such as inconsistent performance for different training runs and significantly higher training cost but it's achievable. Now the problem is if there exist methods more efficient than gradient descent? I think it's very clear now that there are no other algorithm in sight that could achieve the level of intelligence without gradient descent at its core, and the problem is just how gradient descent is used.
    • HarHarVeryFunny 3y ago
      The obvious alternative to gradient descent here would be Bayes Formula (probabalistic Bayesian belief updates), since this addresses the exact problem that our brains evolved to optimize - how to utilize prediction failure (sensory feedback vs prediction) to make better predictions - better prediction of where the food is, what the predator will do, how to attract a mate, etc. Predict next word too (learn language), of course. I don't think pre-train for every update works - it's an incredibly slow and expensive way to learn, and the training data just isn't there. Where is the training data that could train it how to do every aspect of any job - the stuff that humans learn by experimentation and experience? The training data that is available via text (and video) is mostly artifacts - what someone created, not the thought process that went into creating it, and the failed experiments and pitfalls to avoid along the way, etc, etc. It would be nice to have a generic college-graduate pre-trained AGI as a starting point, but then you need to take that and train it how to be a developer (starting at entry level, etc), or for whatever job you'd like it to do. It takes a human years to practice to get good at jobs like these, with many try-fail-rethink experiments every day. Imagine if each of those daily updates took 6 months and $100M to incorporate?! We really need genuine online learning where each generic graduate-level AGI instance can get on-the-job training and human feedback and update it's own "weights" continually.
• reissbaker 3y ago
  Agreed on the lack of value for 13.86% correctness — I noticed that too. This reminds me a little of last year's hype around AutoGPT et al (at around the same time of year, oddly enough); it's very promising as a measure of how far we've come since just a few years ago when that metric would be 0%, but it doesn't seem super usable at the moment. That being said, something is definitely coming. 50% correctness would probably be well worth using — simple copy/paste between my editor and GPT4 has been useful for me, and that's much less likely to completely solve an issue in one shot — and not only will small startups doing finetunes be grinding towards better results... The big labs will be too, and releasing improved foundation models that the startups can then continue finetuning. I don't think a new AI winter is on the horizon yet; Meta has plenty of reason to keep pushing out better stuff, both from a product perspective (glasses) and from an efficiency perspective (internal codegen), and OpenAI doesn't seem particularly at risk of stopping since Microsoft is using them both to batter Google on search (by having more people use ChatGPT for general question answering than using Google search), and to claw marketshare from Amazon in their cloud offerings. Similarly, some AI products have already found product/market fit; Midjourney bootstrapped from 0 to $200MM ARR (!) for example, purely on the basis of monthly subscriptions, by disrupting the stock image industry pretty convincingly.
• HarHarVeryFunny 3y ago
  > It's worth pointing out that on their eval set for "issues resolved" they are getting 13.86%. While visually this looks impressive compared to the others, anything that only really works 13.86% of the time, when the verification of the work takes nearly as much time as the work would have anyway, isn't useful. Yeah, I remember speech recognition taking decades to improve, and being more of a novelty - not useful at all - even when it was at 95% accuracy (1 word in 20 wrong). It really had to get almost perfect until it was a time saver. As far as coding goes, it'd be faster to write it yourself and get it right first time rather than have an LLM write it where you can't trust it and still have to check it yourself.
  • dukeyukey 3y ago
    Even now, automatic speech recognition is a big timesaver, but you _need_ a human to look through the transcript to pick out the obviously wrong stuff, let alone the stuff that's wrong buy could be right in context.
    • pooya93 3y ago
      but* just wanted to mention the error because the comment was about speech recognition errors
  • mgummelt 3y ago
    You can't compare the accuracy of speech recognition to LLM task completion rates. A nearly-there yet incomplete solution to a Github issue is still valuable to an engineer who knows how to debug it.
    • oytis 3y ago
      > A nearly-there yet incomplete solution to a Github issue is still valuable to an engineer who knows how to debug it. Not sure if I can agree. There would definitely be a value in looking at what libraries the solution uses, but otherwise it may be easier to write it oneself, especially when the mistakes are not humanlike.
    • 3y ago
• larodi 3y ago
  It’s like talking to zip file sometimes. Very difficult to make it actually do something you don’t expect it already to can do. Like a smarty index decorated with language.
• andythedev 3y ago
  as much as this response can seem comforting, i feel like its also very easy to under estimate just how quickly an ML model can learn. I'm genuinely concerned that in the next 10 years this will replace the average software engineer
• DinoDad13 3y ago
  That 13.68% is given with no context. Which issues, specifically did Devin solve? Any 3rd party verification?