99954bb63ccc

The status page should detect the thing they are looking into. Kinda like a Check Engine light or gauge reading notifies you to investigate a problem with your engine.

I was thinking the same thing. I see, and have seen, what the poster you responded to is saying. But, I think there likely is a bit of selection bias amongst folks who would be using an LLM to learn in the first place because folks who would use an LLM to learn will likely associate with others who would do the same, esp in a workplace. Ex: cars or computers. A small subset will tinker with them to learn how they work or improve them, but the vast majority just use them to enable other goals. But, it is still important that the small subset exists and is curious enough to learn how they work. And the folks who would tinker with a car likely associate with other folks who tinker with cars, and the same with computers...

IMO it's a lot more like a new more powerful engine is now available. Sure, faster drivers/teams will run faster lap times, no doubt, and it'd be a bad idea to not use it. But, less mature teams/drivers aren't going to catch those faster teams by just slapping the new engine in and going. They are still largely held back by the myriad of other things that go into being fast on track consistently IE (driver + datalog review, and just good ol seat time), vehicle [other than engine] setup, aero tuning, pitting practice (esp fueling for endurance stuff), etc etc. All those same things that were making them slower than the fastest teams before. And even still, if you give only the slower teams the new engine, they probably still won't be competitive with the faster teams and, quite honestly, the not great drivers will probably just make the same mistakes they were making before that slowed them down, except faster.

But, even if the new engine is the golden egg, now what? You're still competing against every other team that has the golden egg... And it's right back to the driver mod, vehicle setup, etc.

That's not to say that the slower teams have any choice but to run the engine too to not be even less competitive. It's just to say that having the engine isn't gonna fix the underlying problem, no matter how much of the team you lay off to fuel the new, more power, engine.

I agree.

How many times I have hit my head against a wall trying to figure out how to get some code to work, only to come back with a clear mind and look holistically to see that it didn't need to be solved to begin with.

None of that is to imply that solving any of these problems isn't groundbreaking or helpful along with the plethora of other things AI has undoubtedly advanced, but I think the breakthroughs will be inferred by many to mean "point AI at really hard problems" is always the solution instead of continuing to use your brain on whether or not the problem _is_ the right problem in it's current form to solve.

hmmmm. Good points. esp:

> Part of the transition from student to junior to senior is learning to filter your own ideas, and to guide others in the search for ideas that are likely to be fruitful.

I wonder how that'll change over time now. I think it's pretty important to have a few instances of thinking hard to come up with ideas and being totally convinced they are correct, only to have someone else trounce them with one or two succinct thoughts of their own. Those were very big personal development moments for me. And equitable, I still have folks with a lot less exp/time come out of left field with maybe not as formulated, but amazing ideas.

Agree 100%, especially with your second paragraph. The majority of domain experts I've interfaced with in my own domain, along with things I am not an expert in, are typically experts not because of the solutions they build, but because of the things they _don't_ build. It is the cliche "a clever person solves a problem, a wise person avoids it" paraphrase.

But I think what is lost is the value in (a) person(s) who intuitively spots and avoids these problems. My hypothetical example that I have seen play out: The domain expert spots one of these problems and succinctly explains that the problem doesn't need to be solved. Maybe it's a byproduct of bad design elsewhere, or there is a much better solution that avoids it altogether. Once the rest of the team/the lead catches on, either hindsight bias or outcome bias, or a combo, takes over and they don't recognize that all the value all along was in the person being able to spot this situation. They throw that in claude and claude does it's sycophant thing (after expressing that the previous situation was flawless for however long), and they are off to the races right back to automation bias with zero pause for thought...

So, has anyone at the frontier AI labs considered doing _good_ things with these models instead of continuously proving it is capable of doing malicious things?

Instead of burning tokens doing intellectually impressive “hacks”, or sandbox escapes from which I have concerns could contain run of the mill malware, why not focus on showing people what you can fix?

Or, make software people actually use every day better.

Or, donate efforts towards medical research.

I don’t like equating AI to nuclear energy, but, it’s a lot like continually showing people how large of an explosion you can create instead of showing them how many houses/hospitals/schools/etc you can power.

Agreed, but am still scared. lol

Have they considered using their amazing new models to... improve something? THere'd probably be a whole lot less anti-AI sentiment if they used these things to actually make people's lives better.