591–600 of 622 posts
This "running out of data" thing suggests that there is something fundamentally wrong with how things are working. A new driver does not need to experience 8000 different rabbit-on-road situations from all angles to know to slow down when we see one on the road. Similarly we don't need 10,000 addition examples to learn how to add. It is as though there is no generalization in the models - just fundamentally search.
In other news, Altman said AGI is coming next year https://www.tomsguide.com/ai/chatgpt/sam-altman-claims-agi-i...
It sounds a bit sci-fi, but since these models are built on data generated by our civilization, I wonder if there's an epistemological bottleneck requiring smarter or more diverse individuals to produce richer data. This, in turn, could spark further breakthroughs in model development. Although these interactions with LLMs help address specific problems, truly complex issues remain beyond their current scope. With my…
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> I do believe LLM is a game changer, but I'm not convinced it is designed to be public-facing. I think that, too, is a UX problem. If you present the output as you do, as simple text on a screen, the average user will read it with the voice of an infallible Star Trek computer and be irritated by every mistake. But if you present the same thing as a bunch of cartoon characters talking to each other, users might not o…
To be fair they usually have "ChatGPT can make mistakes. Check important info" type disclaimers.
The next wave won’t be monolithic but network-driven. Orchestration has the potential to integrate diverse AI systems and complementary technologies, such as advanced fact-checking and rule-based output frameworks. This methodological growth could make LLMs more reliable, consistent, and aligned with specific use cases. The skepticism surrounding this vision mirrors early doubts about the early internet fairly concis…
You are definitely on to something here, but the difference is that the fundamental process was proven. It "just" needed to scale. That's hard and complex, but on a different level. I don't think anyone doubted the nature of the technology. The bits were being sent. It's not like we were unsure of the fundamental possibility of transmitting information. The potential was shown very, very early on (Mother of all demos…
Your point about skepticism being warranted when viewing this linearly is well taken. But this isn’t a linear path. The Internet, at its core, was about connecting computers to unlock the value of those connections—a transformative but relatively straightforward concept.
What we’re dealing with now is the training of cognitive digital intelligence. This is an inherently dynamic and breakthrough-oriented process, one that evolves in ways far less predictable or constrained than simple network effects. While the metaphor of connectivity is useful, it doesn’t fully capture the parallel, multi-dimensional approaches at play here.
Pessimism, in my view, is deeply unwarranted, especially given the history of technological progress. Time and again, advancements have proven to be far more impactful and beneficial than even the most optimistic predictions. Consider the projections for AI in 2017—most futurists undershot its actual progress by an order of magnitude.
This research clearly illuminates a path forward:
https://ekinakyurek.github.io/papers/ttt.pdf
Deeply appreciate your thoughtful comment.
AI winter is here. Almost.
Based on recent rumblings about AI scaling hitting a wall, of which this article is perhaps the most visible - and in a high-reach financial publication, I'm considering increasing my estimated probability we might see a major market correction next year (and possibly even a bubble collapse). (example: "CONFIRMED: LLMs have indeed reached a point of diminishing returns" https://garymarcus.substack.com/p/confirmed-llm…
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Good point! I'm wondering wether it would count, if one would extend it with an external program, that gives it feedback during inference (by another prompt) about the correctness of it's output. I guess it wouldn't, because these RAG tools kind of do that and i heard no one calling those self aware.
> if one would extend it with an external program, that gives it feedback If you have an external program, then by defining it's not self -awareness ;). Also, it's not about correctness per se, but about the model's ability to assess its own knowledge (making a mistake because the model was exposed to mistakes in the training data is fine, hallucinating isn't).
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I have 3 remote employees whose job is consistently as bad as LLM. That means employees who use LLM are, on average, recognizably bad. Those who are good enough, are also good enough to write the code manually. To the point I wonder whether this HN thread is generated by OpenAI, trying to create buzz around AI.
1. The person I'm replying to is hypothesising about a future, not yet existent, version, GPT5. Current quality limits don't tell you jack about a hypothetical future, especially one that may not ever happen because money. 2. I'm not commenting on the quality, because they were writing about something that doesn't exist and therefore that's clearly just a given for the discussion. The only thing I was adding is that…
Their models have tons of use cases, but OpenAI and Anthropic are now in a product/commercial play.
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Since this is a comparison, what has been made comparatively cheaper?
We aren't talking about skilled knowledge work in Silicon Valley campuses. We are talking about work that might already have been outsourced so some cube-farm in the Philippines. Our routine office work that probably could already have been automated away by a line of business app in the 1980s, but is still done in some small office in Tulsa because it doesn't make sense to pay someone to write the code when 80% of t…
Well, I can see the direction you are going. I am unconvinced though - it hasn't thread the needle.
Reason being
1) They are doing both in cube farms in the PHP, RTO + replacement by GenAI.
2) In high tech, they are also trying achieve these contradictory goals. RTO + Increased GenAI capability to reduce manpower needs.
I can see a desire to reduce costs. I cant see how RTO to improve team work sits with using LLMs to do human work.
