51–60 of 608 posts
It's the new underpaid employee that you're training to replace you. People need to understand that we have the technology to train models to do anything that you can do on a computer, only thing that's missing is the data. If you can record a human doing anything on a computer, we'll soon have a way to automate it
> 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…
AI will be a tool, no more no less. Most likely a good one, but there will still need to be people driving it, guiding it, fixing for it, etc.
All these discourses from CEO are just that, stock market pumping, because tech is the most profitable sector, and software engineers are costly, so having investors dream about scale + less costs is good for the stock price.
Humans don’t have an internal notion of “fact” or “truth.” They generate statistically plausible text. Reliability comes from scaffolding: retrieval, tools, validation layers. Without that, fluency can masquerade as authority. The interesting question isn’t whether they’re coworkers or exoskeletons. It’s whether we’re mistaking rhetoric for epistemology.
For example fact checking a news article and making sure what's get reported line up with base reality.
I once fact check a virology lecture and found out that the professor confused two brothers as one individual.
I am sure about the professor having a super solid grasp of how viruses work, but errors like these probably creeps in all the time.
Humans don’t have an internal notion of “fact” or “truth.” They generate statistically plausible text. Reliability comes from scaffolding: retrieval, tools, validation layers. Without that, fluency can masquerade as authority. The interesting question isn’t whether they’re coworkers or exoskeletons. It’s whether we’re mistaking rhetoric for epistemology.
neither are humans
> They optimize for next-token probability and human approval, not factual verification.
while there are outliers, most humans also tend to tell people what they want to hear and to fit in.
> factuality is emergent and contingent, not enforced by architecture.
like humans; as far as we know, there is no "factuality" gene, and we lie to ourselves, to others, in politics, scientific papers, to our partners, etc.
> If we’re going to treat them as coworkers or exoskeletons, we should be clear about that distinction.
I don't see the distinction. Humans exhibit many of the same behaviours.
Closer to a really capable intern. Lots of potential for good and bad; needs to be watched closely.
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.
Amplified means more work done by fewer people. It doesn’t need to replace a single entire functional human being to do things like kill the demand for labor in dev, which in turn, will kill salaries.
Unless there a limited amount of software we need to produce per year globally to keep everyone happy, then nobody wants more -- and we happen to be at that point right NOW this second.
I think not. We can make more (in less time) and people will get more. This is the mental "glass half full" approach I think. Why not take this mental route instead? We don't know the future anyway.
Earlier quoted context omitted.
Why do we care about this? Chess AI have long been solved problems and LLMs are just an overly brute forced approach. They will never become very efficient chess players. The correct solution is to have a conventional chess AI as a tool and use the LLM as a front end for humanized output. A software engineer who proposes just doing it all via raw LLM should be fired.
It's a proxy for generalized reasoning. The point isn't that LLMs are the best AI architecture for chess.
And so for I am only convinced that they have only succeeded on appearing to have generalized reasoning. That is, when an LLM plays chess they are performing Searle’s Chinese room thought experiment while claiming to pass the Turing test
It's the new underpaid employee that you're training to replace you. People need to understand that we have the technology to train models to do anything that you can do on a computer, only thing that's missing is the data. If you can record a human doing anything on a computer, we'll soon have a way to automate it
LLMs have a large quantity of chess data and still can't play for shit.
Humans don’t have an internal notion of “fact” or “truth.” They generate statistically plausible text. Reliability comes from scaffolding: retrieval, tools, validation layers. Without that, fluency can masquerade as authority. The interesting question isn’t whether they’re coworkers or exoskeletons. It’s whether we’re mistaking rhetoric for epistemology.
> LLMs aren’t built around truth as a first-class primitive. neither are humans > They optimize for next-token probability and human approval, not factual verification. while there are outliers, most humans also tend to tell people what they want to hear and to fit in. > factuality is emergent and contingent, not enforced by architecture. like humans; as far as we know, there is no "factuality" gene, and we lie to ou…