It drives me a little bonkers that the UK already tried implementing age verification in 2019, with an approach that would have been easy to make verifiably anonymous: buying a single-use code from a newsagent who checks your age with ID [1], but can't connect the code to you specifically
That attempt officially failed because the UK failed to inform the EU about it, but I suspect it was also much harder to sell people on having to buy "porn passes" than on "just" kicking kids off phones
I'm not opposed to AI generated text in principle. But not knowing how it was written is problematic, because it can change the meaning of the text. Take this paragraph for instance:
"From my experience building and scaling teams in fintech and high-traffic platforms, I can tell you that role expansion without clear boundaries always leads to the same outcome: people try to do everything, nothing gets done with the depth it requires, and burnout follows."
This reads like a first person account of someone's experience. Is it though? If it's nobody's experience then it robs this text of its meaning. If it is somebody's experience and that person used AI to improve their style then that's absolutely fine with me.
The opportunity is disproportionately greater as well though.
Unfortunately that doesn't change the fact even a small miscalculation could have an enormous impact. We are approaching levels of risk comparable in size to the subprime crisis of 2008.
Is it? AI isn't going to be a winner take all market. Competition between American AI labs and even Chinese ones have seen to that.
The winners for AI will be the product companies, because soon enough the top-tier models are all going to have good enough performance that companies can just pick the cheapest. It'll be a race to the bottom for inference and OpenAI is very poorly placed to compete in that kind of thing.
Anthropic doesn't object to fully autonomous AI use by the military in principle. What they're saying is that their current models are not fit for that purpose.
That's not the same thing as delivering a weapon that has a certain capability but then put policy restrictions on its use, which is what your comparison suggests.
The key question here is who gets to decide whether or not a particular version of a model is safe enough for use in fully autonomous weapons. Anthropic wants a veto on this and the government doesn't want to grant them that veto.
Let me put it this way–if Boeing is developing a new missile, and they say to the Pentagon–"this missile can't be used yet, it isn't safe"–and the Pentagon replies "we don't care, we'll bear that risk, send us the prototype, we want to use it right now"–how does Boeing respond?
I expect they'll ask the Pentagon to sign a liability disclaimer and then send it anyway.
Whereas, Anthropic is saying they'll refuse to let the Pentagon use their technology in ways they consider unsafe, even if Pentagon indemnifies Anthropic for the consequences. That's very different from how Boeing would behave.
Why are we gauging our ethical barometer on the actions of existing companies and DoD contractors? the military industrial apparatus has been insane for far too long, as Eisenhower warned of.
When we're entering the realm of "there isn't even a human being in the decision loop, fully autonomous systems will now be used to kill people and exert control over domestic populations" maybe we should take a step back and examine our position. Does this lead to a societal outcome that is good for People?
The answer is unabashedly No. We have multiple entire genres of books and media, going back over 50 years, that illustrate the potential future consequences of such a dynamic.
* private defense contractor leverages control over products it has already sold to set military doctrine.
The second one is at least as important as the first one, because handing over our defense capabilities to a private entity which is accountable to nobody but it's shareholders and executive management isn't any better than handing them over to an LLM afflicted with something resembling BPD. The first problem absolutely needs to be solved but the solution cannot be to normalize the second problem.
>It's like you assigning to humans divine capabilities :) . Hyperbolizing a little, humans also only copy and mix - where do you think originality comes from? Granted, AI isn't at the level of humans yet, but they improve here.
Humans do that a lot but it's not all we do. Go to a museum that has modern(ish) art. It's pretty incredibly how diverse the styles and ideas are. Of course it's not representative of anything. These works were collected and curated exactly because they are not average. But it's still something that humans made.
I think what people can do is have conceptual ideas and then follow the "logic" of those ideas to places they themselves have never seen or expected. Artists can observe patterns, ask how they work and why they have the effect they do and then deliberately break them.
I'm not sure current genAI models do these sorts of things.
> I'm not sure current genAI models do these sorts of things.
You might be right here. Two points though - first, we don't know if current AI is actually incapable of something in particular; we didn't find this, didn't prove it. Second, we might have a different AI approach, which would actually be capable of these things you mention. To me, it's way too early to dismiss AIs - at least in principle - regarding all of this.
The price of tools isn't determined by how much money they make or save the user. That's just the price cap. The price floor (in the long run) is the cost of making the tool. The actual price will be somewhere in between depending on competition.
>But I'm not super bullish on "proofs" being the thing that keeps AI in line.
But do we have anything that works better than some form of formal specification?
We have to tell the AI what to do and we have to check whether it has done that. The only way to achieve that is for a person who knows the full context of the business problem and feels a social/legal/moral obligation not to cheat to write a formal spec.
Code review, tests, a planning step to make sure it's approaching things the right way, enough experience to understand the right size problems to give it, metrics that can detect potential problems, etc. Same as with a junior engineer.
If you want something fully automated, then I think more investment in automating and improving these capabilities is the way to go. If you want something fully automated and 100% provably bug free, I just don't think that's ever going to be a reality.
Formal specs are cryptic beyond even a small level of complexity, so it's hard to tell if you're even proving the right thing. And proving that an implementation meets those specs blows up even faster, to the point that a lot of stuff ends up being formally unprovable. It's also extremely fragile: one line code change or a small refactor or optimization can completely invalidate hundreds of proofs. AI doesn't change any of that.
So that's why I'm not really bullish on that approach. Maybe there will be some very specific cases where it becomes useful, but for general business logic, I don't see it having useful impact.
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