I'm not, but this is not a great introduction. It's handwavy and makes the assumption that AI dev tools are much farther along than they are. I have seen this a lot lately; the farther up the management chain and farther away from putting hands on code, the more confident people seem to be in the power of AI tools.
For big complex real world problems, and big complex real worlde codebases, the AIs are helpful but not yet earth shattering. And that helpfulness seems to have plateaued as of late.
I will take a lot more hand waving from the 70-something year-old Stanford professor who co-created far-up the chain management paradigms that run a good chunk of the economy. That context kinda changes things but what do I know.
That thing he created says you should take your assumptions out into the real world and validate them, ya?
So hand-waving about how easy it is to have an MVP in days w/o actually experience in doing that seems ironic.
Now, maybe he's saying this based on companies he's funded who've had great success with what he's saying. But it's curious that the only concrete example of a company mentioned is one that's six years old and not operating like that.
And in fact, many of the ways he thinks that company went wrong seem completely unrelated to AI?
> Chris is now starting to raise his first large fundraising round. In looking at his investor deck I realized that while he’s been heads down, the world has changed around him – by a lot. The software moat he built with his 5-year investment in autonomy development is looking less unique every day. Autonomous drones and ground vehicles in Ukraine have spawned 10s, if not 100s, of companies with larger, better funded development teams working on the same problem.
> While Chris has been fighting for adoption for this niche market (one that is ripe for disruption, but the incumbents still control), the market for autonomy in an adjacent market – defense – has boomed. In the last five years VC Investment in defense startups has gone from zero to $20 billion/year. His product would be perfect for contested logistics and medical evacuation. But he had literally no clue these opportunities in the defense market had occurred.
> While there’s still a business to be had (Chris’s team has done amazing system integration with an existing airborne platform that makes his solution different from most), – it’s not the business he started.
"Being heads down without paying enough attention to the market for 6 (!!) years" doesn't seem like an AI-caused issue.
Meanwhile, the core suggestion doesn't seem to fix that, it seems almost completely perpendicular.
> You can now test multiple versions of the same business at once (or simultaneously be testing different businesses). While you can be simultaneously testing five pricing models, ten messages or twenty UX flows, the “user interface” may no longer be a screen at all. Testing might be to find prompt(s) to AI Agent(s) deliver needed outcomes.
Ok, but this person didn't even seem to be doing enough paying to the market of one version already?
And while this claim about parallel development being a huge unlock is the most interesting thing, it also sounds a bit glib. Getting your foot in the door is the hardest thing early on, now you're trying to run six versions of your company at once? Each time you get a foot in the door sales-wise, are you trying to make them use all 6 versions, or are you only gonna get feedback on 1? Would you want to pay money to be a beta tester of 6 different products simultaneously, with reason to believe that 5 of them will probably evaporate over night soon?
> the ultracapitalist dystopia the US has turned into
Seriously, where do ideas like this come from? An "ultracapitalist" country that has about as much redistributive social spending as other developed economies[0]? A "dystopia" that millions of people from all over the world clamor to get into every year?
So in other words... he doesn't actually use the tools he's firmly convinced will automate the building of software.
I don't agree with the parent; I think capitalism is doing a lot of great things for us and will continue to, even with AI. But man I'm tired of these hot takes from people with limited practical experience.
The whole post should have just been this one line. He likes the sound of his own voice too much.
That said, it rings hollow. AI doomerism is rooted in Terminator style narratives, and in that narrative, the rogue Sarah Connor changes history (with a lot of violence, explosions, and special effects).
North Korea started out with a "nuclear weapon": Seoul is within artillery range of the border. Consequently the Kim regime has been able to starve and torture its own population, and yes - develop nuclear weapons - without anyone willing to stop them.
You think the problems inside North Korea are ok? Koreans are human too.
There's thirty-some-odd million people in Ukraine who very much would like to get AI weapons before the Russians do. They're coming whether you want them or not.
I actively use AI to refactor a poorly structured two million line Java codebase. A two-sentence prompt does not work. At all.
I think the OP is right; the problem is context. If you have a nicely modularized codebase where the LLM can neatly process one module at a time, you're in good shape. But two million lines of spaghetti requires too much context. The AI companies may advertise million-token windows, but response quality drops off long before you hit the end.
You still need discipline. Personally I think the biggest gains in my company will not come from smarter AIs, but from getting the codebase modularized enough that LLMs can comfortably digest it. AI is helping in that effort but it's still mostly human driven - and not for lack of trying.
I think it's too early to declare the Turing test passed. You just need to have a conversation long enough to exhaust the context window. Less than that, since response quality degrades long before you hit hard window limits. Even with compaction.
Neuroplasticity is hard to simulate in a few hundred thousand tokens.
I think for a while the test was passed. Then we learned the hallmark characteristics of these models, and now most of us can easily differentiate. That said -- these models are programmed specifically to be more helpful, more articulate, more friendly, and more verbose than people, so that may not be a fair expectation. Even so, I think if you took all of that away, you'd be able to differentiate the two, it just might take longer.
Right. I think the modern LLMs are quite good at mimicking human words, but we were initially taken in like we were in the 1960s by ELIZA. It’s a (increasingly sophisticated) magic trick, but it’s just a trick.
It's weird, I don't know how normally pedantic comp sci. people let this meme that the Turing test is beaten by LLMs to spread so unchallenged. As far as I'm aware, there is no restriction in the Turing test that demands that the interrogator be ignorant of the latest state-of-art in computing (and AI tech), nor is there a strict time limit enforced for the questioning?
Given these conditions, it should be relatively easy for the interrogator to expose the AI in this current day and age.
>Consider first the more accurate form of the question. I believe that
in about fifty years' time it will be possible, to programme computers, with
a storage capacity of about 10^9, to make them play the imitation game so
well that an average interrogator will not have more than 70 per cent chance
of making the right identification after five minutes of questioning. (Turing 1950)
That was the test as discussed by Turning - five minutes, <70% chance of getting it right.
It's not that demanding. The test you mention could maybe be called an enhanced Turing test but the original one is pretty much passed.
He was a bit off on the time taken and memory used. I think more like 75 years and 50 GB rather than 50 years and 125 MB.
For as rigorous of a Turing test as you present, I believe many (or even most) humans would also fail it.
How many humans seriously have the attention span to have a million "token" conversation with someone else and get every detail perfect without misremembering a single thing?
Response quality degrades long before you hit a million tokens.
But sure, let's say it doesn't. If you interact with someone day after day, you'll eventually hit a million tokens. Add some audio or images and you will exhaust the context much much faster.
However, I'll grant you that Turing's original imitation game (text only, human typist, five minutes) is probably pretty close, and that's impressive enough to call intelligence (of a sort). Though modern LLMs tend to manifest obvious dead giveaways like "you're absolutely right!"
How do you propose to do a Turing test on a human (in a sense that is different from a machine simply passing the Turing test)?
Like failing to pick out all the motorcycles in a captcha, or a turing test where you have a guy chat with two people without knowing that one of them could be a computer, and the interrogator, unprompted, suggesting one of them might be a computer?
There are a lot of difference kinds of LLMs. 0 of the ones I've encountered are good writers, in fact all of them are horrible at it.
But I wonder if there's one out there that I don't know about with a different kind of training that actually is good at writing and fun to talk to for a long time. (granted somepeople love talking to gpt 4, but also some people loved talking to ELIZA so clearly some people have a super high tolerance for slop.)
I don't know. Practically, LLMs are already better conversation partners on any topic compared to the average human I have access to. This also holds in reverse, of course - if someone wants me to explain something, usually they'd be better off asking an LLM.
* The people responsible for murdering ten thousand protesters are now dead.
* The IRGC's military capability is significantly degraded.
* Their nuclear program is likely set back even further. It's hard to get real information here but we should assume that supporting facilities were high on the target list.
That's not nothing. From a strict utilitarian perspective, it's probably "worth it". Which sucks, but I haven't heard a better plan.
i dont think those are nearly as clearcut as suggested.
some of the iranian side for events that resulted in a bunch of death have been killed... while also killing a bunch mkre iranians, but have the americans/israelis that armed the protestors into terrorists and incided them to violence been killed?
i think theres enough police, mossad, and cia folks left to do that again and again until the protestors are all gone.
similarly, its blatantly obvious for everyone that the US destoryed the iranian capabilities that dont matter. iran is still capable enough to seter both putting american ships in the strait, and boots on the ground, so that degradation is not significant. optimization without profiling.
from a strict utilitarian perspective, definitely not worth it. the costs were extraordinarily expensive and havent been fully paid yet, and the profits for the US is a worse position than they started it
theres some light benefits to the gulf and ukraine in that the gulf realizes that they can spend much less on defense by buying from ukraine, but that pales in comparison to the costs paid in destroyed oil infrastructure and interceptors that could have gone to ukraine
For big complex real world problems, and big complex real worlde codebases, the AIs are helpful but not yet earth shattering. And that helpfulness seems to have plateaued as of late.
I am extremely skeptical of posts like this.
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