321–330 of 608 posts
[dead]
> That's not augmentation, that's a completely different game Not saying that this comment is ai written, but this phrasing is the em-dash of 2026.
Earlier quoted context omitted.
[flagged]
Ok, I'll bite. Let's assume a modern cutting edge model but even with fairly standard GQA attention, and something obviously bigger than just monosemantic features per neuron. Based on any reasonable mechanistic interpretability understanding of this model, what's preventing a circuit/feature with polysemanticity from representing a specific error in your code? --- Do you actually understand ML? Or are you just parro…
Earlier quoted context omitted.
You’re committing the classic fallacy of confusing mechanics with capabilities. Brains are just electrons and chemicals moving through neural circuits. You can’t infer constraints on high-level abilities from that.
This goes both ways. You can't assume capabilities based on impressions. Especially with LLMs, which are purpose built to give an impression of producing language. Also, designers of these systems appear to agree: when it was shown that LLMs can't actually do calculations, tool calls were introduced.
The same goes for a lot of bugs in code. The best prediction is often the correct answer, being the highlighting of the error. Whether it can "actually find" the bugs—whatever that means—isn't really so important as whether or not it's correct.
Earlier quoted context omitted.
Nothing you've said about reasoning here is exclusive to LLMs. Human reasoning is also never guaranteed to be deterministic, excluding most correct solutions. As OP says, they may not be reasoning under the hood but if the effect is the same as a tool, does it matter? I'm not sure if I'm up to date on the latest diffusion work, but I'm genuinely curious how you see them potentially making LLMs more deterministic? The…
The way I imagine greedy sampling for autoregressive language models is guaranteeing a deterministic result at each position individually. The way I'd imagine it for diffusion language models is guaranteeing a deterministic result for the entire response as a whole. I see diffusion models potentially being more promising because the unit of determinism would be larger, preserving expressivity within that unit. Additi…
There's an undertone of self-soothing "AI will leverage me, not replace me", which I don't agree with especially in the long run, at least in software. In the end it will be the users sculpting formal systems like playdoh. In the medium run, "AI is not a co-worker" is exactly right. The idea of a co-worker will go away. Human collaboration on software is fundamentally inefficient. We pay huge communication/synchroniz…
> I would rather a single human (for now) architect with good taste and an army of agents than a team of humans. A human might have taste, but AI certainly doesn't.
There's an undertone of self-soothing "AI will leverage me, not replace me", which I don't agree with especially in the long run, at least in software. In the end it will be the users sculpting formal systems like playdoh. In the medium run, "AI is not a co-worker" is exactly right. The idea of a co-worker will go away. Human collaboration on software is fundamentally inefficient. We pay huge communication/synchroniz…
- Jensen Huang, February 2024
https://www.techradar.com/pro/nvidia-ceo-predicts-the-death-...
I like this. This is an accurate state of AI at this very moment for me. The LLM is (just) a tool which is making me "amplified" for coding and certain tasks. I will worry about developers being completely replaced when I see something resembling it. Enough people worry about that (or say it to amp stock prices) -- and they like to tell everyone about this future too. I just don't see it.
The more likely outcome is that fewer devs will be hired as fewer devs will be needed to accomplish the same amount of output.
Earlier quoted context omitted.
The next thing after agent swarms will be swarm colonies and people will go "it's been a month since agentic swarm colonies, give it a month or two". People have been moving the goal posts like that for a couple years now, it's starting to grow stale. This is like self driving cars which were going to be workingin 2016 and replace 80% of drivers by 2017, all over again. People falling for hype instead of admitting th…
I generally agree, but counterpoint: Waymo is successfully running robocabs in many cities today.
Earlier quoted context omitted.
> We pay huge communication/synchronization costs to eek out mild speed ups on projects by adding teams of people. Something Brooks wrote about 50 years ago, and the industry has never fully acknowledged. Throw more bodies at it, be they human bodies or bot agent bodies.
The point of the mythical man month is not that more people are necessarily worse for a project, it's just that adding them at the last minute doesn't work, because they take a while to get up to speed and existing project members are distracted while trying to help them. It's true that a larger team, formed well in advance, is also less efficient per person, but they still can achieve more overall than small teams (…
> We're thinking about AI wrong. And this write up is not an exception. Why even bother thinking about AI, when Anthropic and OpenAI CEOs openly tell us what they want (quote from recent Dwarkesh interview) - "Then further down the spectrum, there’s 90% less demand for SWEs, which I think will happen but this is a spectrum." So save thinking and listen to intent - replace 90% of SWEs in near future (6-12 months accor…
Where is this "90% less demand for SWEs" going to come from? Are we going to run out software to write? Historically when SWEs became more efficient then we just started making more complicated software (and SWE demand actually increased).
In times of uncertainty and things going south, that changes to we need as little SWEs as possible, hence the current narrative, everyone is looking to cut costs.
Had GPT 3 emerged 10-20 years ago, the narrative would be “you can now do 100x more thanks to AI”.