Hopefully Google won't hamstring this by forcing you to enable "install apps from any source" setting to use non-Google app stores. If I use an alternate app store I don't want to let apps from just anywhere be installed, I only want apps from that app store to be installed. Apps from anywhere else I want to continue to block.
Yeah, there's two main problems:
1) People entering the wrong fields. For example, medicine really needs workers, at all levels, but not enough people are going into it.
2) Certain manual labour fields, like field work and home construction, because... well, I think we all know why there's a shortage of workers in those fields.
Both Putin and Trump have started wars that they cannot win, and from which they cannot withdraw without an enormous loss of face.
The only difference is that Putin's war only effects Russia's economy, while Trump's war effects the whole world.
Or let's put this another way. Show of hands - how many of you "spicy autocorrect" / "stochastic parrot" people had "AI will start mass-solving Erdos problems" on your forecast list a couple years back? Huh, none of you? Fascinating!
Take some time to reassess your priors. And while you do so, understand that, yes, they are doing logic / reasoning.
They weren't discovered by an LLM. They were known conjectures that were proven by an automated solving language that was linked to an LLM.
I'll take "Things That Didn't Happen For $200", Alex.
Only a handful of meaningful proofs have ever been done by automated formal theorem solvers (the Four Colour Theorem being the most noteworthy example - but its proof is so long that humans can't verify it). By contrast, AI tools have been solving Erdos problems en masse. The majority of them just bog-standard commercial models. In case you need help, the only ones on that list that were hybrid (AI / non-AI) in the actual solving phase are:
1) AlphaProof / DeepMind Prover Agent / AlphaProof Nexus
2) Aristotle (Harmonic)
3) Seed Prover / Seed Prover 1.5 (ByteDance)
4) AxiomProver (Axiom Math)
In each of the above, LLMs come up with the lemmas / strategies but then use Monte Carlo search ("brute force") or likewise to investigate what they came up with. These are a minority. In the "AI Standalone" category, these "hybrid" tools made up only ~20% of attempts and successful proofs. Hybrid tools actually made more of a contribution in the "AI Alongside Literature" (related literature found afterward) and even more of the "AI Building On Literature" (related literature known beforehand) categories, which is the opposite of what people like you expect.
And even with the hybrid tools, it's still the AI doing the heavy lifting when it comes to strategy. Non-AI theorem solvers, again, don't have a spectacular record for churning out novel proofs to unsolved problems. Tools like Lean are more about mathematical rigour - a passive environment that requires a driver (a human or AI) to feed it actual strategies, lemmas, and proof steps. And no, you cannot brute force "strategy" in the vast majority of cases, which is, again, why automated theorem solvers don't have much of a track record with unsolved mathematical problems.
Let's take a random example: the disproof of the unit distance conjecture. It was solved purely by a general purpose commercial GPT model, not custom-trained to mathematics, with no external tools. Read what the various mathematicians reviewing / commenting on it have to say (sections #3 and onward). Seriously, don't skip reading them, actually read them. This was one of Erdos's favourite problems. He mentioned it commonly in his lectures. Essentially every mathematician working in complex geometry has thought about this problem. The approach that the model came up with was highly novel approach, based on CM-fields and class field towers.
I know you don't want to accept this reality, but it is the reality, so you better improve your ability to accept it,. The field of mathematics is already doing so.
Dang, link didn't post.
Ah, good 'ol "Model Collapse" theory that people have been pounding on for years now, predicting an imminent collapse in model capabilities. How has that been working out for you?
Except what the US is actually facing, at least in the near term, is just the opposite, a worker shortage.
You can't pay royalties to the entire internet. That's not realistic.
What is practical are things like taxes to fund public benefits, or requirements of returning things to the commons (for example, open models).
That's actual stock, not options. Incentive stock options (the kind executives receive) don't count as income until you sell the stock after exercising the options. Take out a loan against that stock and you've got dollars in your bank account you can spend but no income to report. Now, suppose the loan's structured so that, as long as the stock is worth enough to cover the principal plus accumulated interest, you don't have to make payments on the loan. Do this through a trust so your heirs don't have to worry about repayment either. Every year now you get the full dollar value of your stock awards in cash without having to declare any income. Possibly even without having to exercise the options, depending on how friendly you are with the lender.
Any router running SNMP or a web interface configured to listen on the external (WAN) port should be considered defective and replaced (or reflashed to sane non-braindead firmware). Home networks don't need SNMP at all, and business networks not using enterprise-grade equipment probably don't need it with write enabled. The only access to the router from the WAN side should be SSH using public-key authentication, and that only if you absolutely need it (you probably don't). That solves the vast majority of problems.
Russia, China and Iran trying to inflame the AI data-center debate is like pouring 5 gallons of gasoline on an oil-refinery fire: technically you did make it worse but by such a small amount nobody's going to notice.
Scientists will study your brain to learn more about your distant cousin, Man.
