If you don’t prioritize, everything seems urgent and important. If you define the single most important task for each day, almost nothing seems urgent or important.
I suspect that some of the labs' willingness to put money into the social sciences stems from the realization of senior people that the practical build out of AI is highly unpopular in the U.S and their urgent desire to figure out how to sugarcoat the pill so that it will get swallowed
I have long said that even Anthropic is not prioritizing safety, even to the extent that doing so would maximize their medium term (e.g. 3-12 months) business interests.
And vibe coding, if we define it here, is you tell an agent to build software for you. You do not look at the implementation. That, to me, is what separates vibe coding from programming or, let's say, agent-accelerated development.
it's more important to invest in trains that can get more riders than just the railfans; taking a line from 0% to 5% modal split is less important than taking it from 20% to 40%
Right now: llama3.2:3b appears to be the model for: is this email urgent?qwen3:8b appears to be the model for: summarize this 5000-word article. llama3.3:70b(q8) appears to be the model for: let’s write or debug some computer code.
if you're truly ambitious, burn your resume. If you and if you define your ambition in the eyes of your consumer, not your peers, you're not trying to win awards and respect from your peers.
I would prioritize trying to index, but just given how fast AI could, you know, hit the world. But, um I definitely wouldn't just rely on that because like it could the the sort of um messy middle type cases or the just a long timelines cases on which like you we don't get it anything like AGI all that soon.
It was already clear a while ago that Anthropic mostly do not care about climate, environment or air pollution in any material way, even if they care a little bit more about bad PR than Elon Musk does.
The only lasting path to satisfaction, happiness, skin in the game, maximizing learning, is to really study everybody's game, define your own, and then play it to the best of your ability.
do not define an objective which is designed to serve human beings without considering psychological factors because you might be able to solve your problem very very cheaply and efficiently by changing the psychology not by changing the technology
I approached Echoes of October with trepidation. A graphic novel about violence and grief isn’t easy terrain. But it succeeds in a haunting, urgent way.
What the Apple paper shows, most fundamentally, regardless of how you define AGI, is that LLMs are no substitute for good well-specified conventional algorithms.
A unique lens on work in the late 90s that still resonated when I read it in 2018. It's a soulful exploration of how to maintain your humanity, creativity and inner fire in environments that often prioritize conformity and efficiency.
Roberts reflects on the wild problems we face in our lives and how to navigate them—like career changes, marriage, or children—that can't be solved on a spreadsheet.
So, taking also any benchmark that is derived from competition and saying this is where we should be is also so dangerous because it might not even be applicable depending on how you define the metric.
Increasing the incentive and technical ability of AI companies to have good security is a very high priority, and in my view even more urgent than safety (though that’s also very important).
So, there is a period of collaboration, and that’s what propaganda tries to define him by, or there is a Division Galizien by 20,000 people, and somehow it makes irrelevant the experience of 2-3 million people.
I do think that we might be in the middle of an extinction right now if you define it by the number of species that are getting killed off. It’s subtle, but it’s a complex system.
So generally, I think we obviously want to prioritize civilizational risks over things that are painful and tragic on a local level, but not civilizational.
To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans.
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