51–60 of 61 posts
Earlier quoted context omitted.
Oh no, no, this isn't the worst AI will ever be. Way worse LLMs are yet to come once the cost cutting efforts begin.
I mean it in that this is the worst the "best currently existing AI model" will ever be
He misspelled psycho.
Ten hours is a decent amount of time, so I'm not too surprised the human won. LLMs don't really tend to improve the longer they get to chew on a problem (often the opposite in fact). The LLM was probably getting nowhere trying to improve after the first few minutes.
Remember this is the worst AI will ever be from here on out. Models are only going to get better, faster, cheaper, more accessible and more easily deployable. I think people need to realize that just because an AI model fails at one point, or some certain architecture has common failure modes, that billions of dollars are poured into correcting those failures and improving in every economically viable domain. Two yea…
> AGI will arrive. This does not follow. Your argument, set in the 1950s, would be that cars keep getting faster, therefore they will reach light speed.
The speed equivalent of AGI is way below light speed, in that the requirements for silicon to replicate the synaptic complexity of the human brain is far below the maximum compute human civilization can achieve as allowable by physics.
The more important question is whether the progress we've seen in AI is putting us on reliable track to hit AGI in the near future. My opinion is that we are, and not just because Demis, Sam, Elon and Dario say so, though they have very good reasons for believing so (yes, besides mere hype and speculation.)
Remember this is the worst AI will ever be from here on out. Models are only going to get better, faster, cheaper, more accessible and more easily deployable. I think people need to realize that just because an AI model fails at one point, or some certain architecture has common failure modes, that billions of dollars are poured into correcting those failures and improving in every economically viable domain. Two yea…
Haven't they already started to regress? I'm bullish on specific areas improving (I'm sure you could selectively train an LLM on the latest Angular version to replace the majority of front-end devs given enough time and money, it's a limited problem space and a strongly opinionated framework after all), but for the most part enshittification is already starting to happen with the general models. Nowadays even ChatGPT…
Whatever model is cheap to provide inference for free is irrelevant when it comes to discussing SOTA AI capabilities and their impact. The state of the art has been reliably improving markedly over the past 3 years. o3, Claude opus 4, gemini-2.5 all surpass their predecessors in every benchmark and indicate that improvement isn't slowing down.
If GPT-5 comes out and it's somehow worse then I'll concede to your point, but so far the claim that the latest models are getting worse is mere speculation and makes no sense given that most labs are already aware of the potential for data contamination and such and have taken measures to ensure high data quality for the models they're spending hundreds of millions to train.
Earlier quoted context omitted.
Exactly. The inability of people to extrapolate towards the future and foresee second-order effects is astounding. We've seen this in climate change and we've just seen this in COVID. The ones with foresight are warning about the massive upheaval coming. It's time for people to shake away their preconceived notions, look at the situation with fresh eyes, and deeply think about what the technology diff from 5 years ag…
> Exactly. The inability of people to extrapolate towards the future and foresee second-order effects is astounding. On a related note, many people also assume that just because something has been trending exponential that it will _continue_ to do so...
Moore's law continued on an exponential for decades. The fundamental limit in terms of transistor density are the laws of physics (uncertainty principle will eventually be a problem), but so far so many paradigms in compute improvement have emerged (especially in GPUs and AI-specific compute) that it has become super-exponential in some respects.
So the question is whether there is a fundamental barrier that AI will hit. The main issues people bring up are a lack of high quality human-generated data, fall-off in value per compute spent, and limits to autoregressive models. However it seems that pretraining has been the only paradigm beginning to show diminished returns but test-time compute and RL are still on the exponential curve.
Earlier quoted context omitted.
Oh no, no, this isn't the worst AI will ever be. Way worse LLMs are yet to come once the cost cutting efforts begin.
I mean it in that this is the worst the "best currently existing AI model" will ever be
Earlier quoted context omitted.
Yet, AI agents don't replace software engineers. Imagine a software company without a single software engineer. What kind of software would it produce? How would a product manager or some other stakeholder work with "AI agents"? How do the humans decide that the agent is finished with the job? Software engineering changes with the tools. Programming via text editors will be less important, that much is clear. But "AI…
AI agents are replacing junior software engineers now at big companies, or at least lowering the number they are hiring. Currently AI failure modes (consistency over long context lengths, multi-modal consistency, hallucinations) make it untenable as a "full-replacement" software engineer, but effective as a short-term task agent overseen by an engineer who can review code and quickly determine what's good and what's…
Regarding the potential economic gains, they're exactly the salary of software engineers. That's a decent amount but not massive.
Compare this to civil engineering, architecture, and craftsmen. None have been replaced because machines let amateurs do something resembling their job.
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Well, millions did, that's why it's a classic!
I suspect it may not be taught, anymore, though. I seem to encounter cultural milestones, that are no longer there, every day.
The majority of people don't listen to anything outside the hits of the year, and for younger people there are no community events, there's no shared platform with any variety (radio is dead, streaming plays either popular hits or your own echo-bubble), schools doesn't teach them (they're more likely to teach some song of the latest shit pop celebrity), and there are in general no mainstream institutions that keep these alive.
But before our current 60-seconds-memory-span such folk songs were part of a canon known and loved for well over a century.