My advice is: Don’t let someone else control your life.
Switch occupation every 2 years, even if you find a great environment. Don’t chase, for what you need must come to you.
Understand no one can give you the answers you seek; you have to live it.
if you change jobs despite being in a great environment, aren't you chasing something? if you follow this advice, don't you let the advice control you?
in particular for jobs, if you think that jobs are controlling you (which is not unreasonable) then switching jobs is not helping you to escape that control.
if you don't want to let someone else control your life you need give up your traditional career and become a freelancer. i did that. i had jobs too in between, but only when they helped me advance my own goals.
You can also "job hop" internally if you want to stay at the same company but want a new experience. The thing about huge companies is that they're huge! You can switch to a team that functions completely differently from the team or org that you were in before.
In my country, it would certainly be odd, so it would raise questions. Would not be a showstopper, though we would definitely carefully consider hiring.
If somehow recovering the capex expenditure is not counted, if somehow the cost of developing future models is not counted, then yes, inference costs of current leading models allow a profit.
But those things are tied together.
Even xAI, that now has a reasonably competitive model, is struggling to achieve PMF. Meta is in shambles because their models have underperformed for years now.
There are people who think knowledge discovery is just a matter of parroting past behavior and trying things at random until something sticks. I don’t.
I am thinking there’s a large category of problems that can be solved by resampling existing proofs.
It’s the kind of brute force expedition machine can attempt relentlessly where humans would go mad trying.
It probably doesn’t really advance the field, but it can turn conjectures into theorems.
I wonder if teaching an LLM how to write Prolog and then letting it write it could be a great way to explore spaces like this in the future. Other people in I wonder if teaching an LLM how to write Prolog and then letting it write it could be a great way to explore spaces like this in the future.
I only ever learned it in school, but if memory serves, Prolog is a whole "given these rules, find the truth" sort of language, which aligns well with these sorts of problem spaces. Mix and match enough, especially across disparate domains, and you might get some really interesting things derived and discovered that are low-hanging fruit just waiting to be discovered.
Indeed, can't find my old comment on the topic but that's indeed the point, it's not how feasible it is to "find" new proof, but rather how meaningful those proofs are. Are they yet another iteration of the same kind, perfectly fitting the current paradigm and thus bringing very little to the table or are they radical and thus potentially (but not always) opening up the field?
With brute force, or slightly better than brute force, it's most likely the first, thus not totally pointless but probably not very useful. In fact it might not even be worth the tokens spent.
I'm of the opinion that everything we've discovered is via combinatorial synthesis. Standing on the shoulders of giants and all that. I'm not sure I've seen any convincing argument that we've discovered anything ex nihilo.
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