You don't need to kill all humans, you just need to get better than them at zero sum games like resource extraction, and outcompete them. Then, you protect what's legally yours.
AI labs are working very hard at making them good at making them richer, and at making them respect the property rights of corporations.
This sounds like a great foundation for an adtech startup.
If you provide free chatbot services, but sell advertisers bids on which steering vectors to use to bias towards products, based on an embedding of the prompt, I bet you'd make a ton of money. For example, Coca Cola would bid on prompts about drinks, and bias towards mentioning Coke products.
I wonder if you could also use a similar method to do product placement in GenAI images and videos, and whether ad revenue would be enough to offset the price of generation. Some ad bids can go pretty high...
> Not to mention you just can't trust a model's judgement if the highest bidder chooses what it thinks
But you already can't trust a model's judgement, and there's an entire industry around "GEO" or "AEO", which is basically poisoning training data so that AI mentions your products. The post above is the owner of the model taking a cut of that.
But ultimately this is just an engineering problem, no?
Yes, if you just hack steering into a model it's going to hurt performance, because doing so takes the model out of the regime it was trained for and validated in. But if that steering were to be accounted for (e.g. by rearchitecting the training process) there's no reason why it couldn't work. Diffusion-based image generation models, for example, 'by default' just generated random images out of the noise; steering (i.e. the user prompt) was added on as a secondary input, which models had to be re-trained in order to use.
It might be easier, but I experimented a bit, and the prompted writing always felt a bit heavy handed; it tended to leak that mentioning the product was prompted. You could probably get it to work well, but it's trickier than it should be. For ads, I think you want something a bit like Golden Gate Claude, if anyone remembers that experiment:
Makes sense. The kids can see that education isn't really going to be a sellable skill when AI ends up coming into full force.
AI already writes most of the articles I see posted here, is doing math at the level of the best mathematicians, and is writing a ton of the code out there. Given another decade to develop, the kids will be hitting the workforce with very stiff competition. It's not a shock they're putting their energy elsewhere, and I can't say they're wrong.
They may be the first generation who grow up fully catered to by AI.
> is doing math at the level of the best mathematics
Terence Tao a few days ago [1]:
> When we point the AIs at really difficult problems where none of the standard techniques apply, they are still very, very bad, I mean they're just randomly guessing.
Please don't reply saying they'll be proving the Collatz conjecture next week. It adds nothing.
Aren't the best mathematicians the best at solving the hardest problems? I don't see how you can argue this without assuming an arbitrary scale for being "good" at something.
Let's see you solve some open problems before you dismiss LLM capabilities. They're working at a level far beyond all but the best of the best.
And we're only a couple of years in. Their capabilities aren't going to be getting worse over time. These kids will be hitting the workforce after AI has another decade of improvement put into it.
Sure, if these kids can all perform at Tao's level, they could likely be able to keep up with the AI capabilities from a decade before they would enter the workforce.
I was clear on what I was responding to, and quoted it: "is doing math at the level of the best mathematics". This is misinformation and a public perception that will harm maths as it continues to spread. For some actual insight on the relationship between human and LLM maths (including on why thinking of it in terms of "levels" is misguided), see the video I linked.
Your broader point is based on a future extrapolation of LLM capabilities that is far from guaranteed to play out, so it sounds like poor risk management to me, even accepting your framing of it purely in terms of value in the workforce (which I don't).
> Makes sense. The kids can see that education isn't really going to be a sellable skill when AI ends up coming into full force.
If true, this is a disastrously foolish thing to believe. Education is only going to become more important as supervising increasingly advanced AI agents becomes more difficult. We need people who understand all different domains of human endeavor at the deepest level possible.
I mean, what's the alternative? AI does everything for me and I play Fortnite all day? Might as well plug yourself into the Matrix.
The situation will probably be like with airline pilots. The plane is mostly an automated system, but we simply don't let automation to work through exceptional situations, without human review. Therefore, lots of pilot training (and their presence) is still required.
But it's also hard to guess what exactly skill should the pilots have. It's clear that lots of things from manual aircraft do not translate well to automated.
Children of the Magenta Line seems very topical, especially wrt AI
>In 1997, American Airlines captain Warren VanderBurgh gave a lecture warning about the dangers of autopilot. He titled it "children of the magenta line," after the magenta coloured course line the flight computer draws across a cockpit display. He noticed that pilots were so used to following that line, to managing the automation rather than actually flying the plane, that they lost their own skills to manually fly the plane. This wasn’t a problem until it was a problem.
>VanderBurgh’s concern was not about autopilot itself but rather the complete dependency on autopilot at the expense of the internal knowledge of how it works and the loss of the judgment to know when to use it. In 2009, his warning came true in the very worst way possible over the Atlantic. When Air France 447’s sensors iced over, the autopilot handed a perfectly good airliner back to its crew, the pilots, confused and out of practice, stalled it into the sea.
Just like kids eventually take over most of the daily tasks for their parents (for people in countries where you don't have any kind of retirement fund and assisted living, etc.). Same will happen here - on one hand because the human population keeps growing older without any replacement and on the other because all the new tech will be too complicated for humans to understand.
It seems unlikely that we'll be able to keep up with AI in any capacity. Your alternative seems like the inevitable outcome of AI succeeding, and it doesn't sound so bad.
Dunno man, the articles ai writes are empty cardboard crap, and the code it produces is hot garbage. “Cleaner upper of AI messes” might be a good future skill to have.
I don't think you can dismiss the output quality so easily.
Give yourself a few hours to write out some code in the old way. Can you really tell me that you could not have done a better job with AI in the same time?
Eg, implement chess or poker games.
Or write a component like a Swiss table.
It's not obvious to me that AI produces bad work at all.
That’s the thing - many times it’s not obvious. But go check that chunk of code that, sure, works, and you’ll find subtleties that just don’t look right. But hey - you do you if that’s the kind of quality you’re happy with.
Not unless it’s written by a total novice. But hey - as in my previous reply, if you’re happy with AI-written code and honestly can’t see the difference, that’s ok. Do whatever floats your boat.
I hope people recognize this account doom posts what amounts to misanthropic propaganda at a very high rate. There’s never any question how the comment is going to be framed.
It’s always, “give up little humans, your robot overlords are here.”
The answer is mostly "do nothing, the model will figure it out", with a side of "ask the model to check its work".
This matches my experience, where the job of the engineer is mostly copy pasting requirements, letting the model do the thinking, and then manually testing the results.
They hold your tls keys and can decrypt all your traffic. They're MITM as a service, by definition. They have to be able to in order to cache and forward appropriately.
And the best way to get people to let you do bad things, is to offer them something good, that uses the same mechanism. If I want to MITM the whole internet, what better way than offering free caching and bot blocking?
I even get to charge the bots extra to bypass the block, and then charge the customers extra to block the bots that are paying extra to not be blocked!
Also to do DDoS mitigation. Being able to see the HTTP request, at least headers and path, greatly helps with distinguishing attackers from legitimate traffic.
It's a tragedy that there's no standard to allow partial decryption/nested encryption in HTTP, which would allow intermediate proxies like Cloudflare to e.g. only validate a first-level authentication token and rate-limit access to a given endpoint, but not decrypt the actual request body, backend authentication token, or response.
Also desperately missing: Authenticated static file caching (think: cdn.foo.com serves files authenticated/signed by foo.com). Subresource integrity only works for HTML use cases and is clearly not ergonomic enough to make a difference.
AI labs are working very hard at making them good at making them richer, and at making them respect the property rights of corporations.
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