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Republicans are looking to militarize and ramp up AI: https://www.washingtonpost.com/technology/2024/07/16/trump-ai-executive-order-regulations-military/
This is only a draft plan and not official policy but it does seem like decades of EA/lesswrong philosophizing and NGO shenanigans have been swept away by the Aschenbrenner 'speed up and beat China to the finish line' camp. I think that's what most people expected, the fruits are simply too juicy for anyone to resist feasting upon them. It also fits with the general consensus of big tech which is ploughing money into AI at great speeds. The Manhattan Project cost about $20 billion inflation adjusted, Microsoft is spending about $50 billion a year on capex, much of it going into AI data centres. That's a lot of money!
However, there is a distinction between AGI/superintelligence research and more conventional military usage: guiding missiles and drones, cyberwarfare, improving communications. China has been making advances there, I recall that they had datasets of US navy ships circulating. One of their most important goals is getting their anti-ship ballistic missiles to hit a moving, evading ship. It's hard to guide long-range missiles precisely against a strong opponent that can jam GPS/Beidou. AI-assisted visual targeting for the last adjustments is one potential answer.
The Chinese and US militaries may not be fully AGI-pilled but they're very likely enthusiastic about enhancing their conventional weapons. Modern high-end warfare is increasingly software-dependant, it becomes a struggle between the radar software and the ECM software, satellite recognition vs camouflage. If you have some esoteric piece of software that can make it easier to get a missile lock on a stealth fighter, that's a major advantage. While most attention is focused on text and image generation, the same broad compute-centric techniques could be used for radar or IR, seismology, astronomy...
On the cultural front J D Vance has highlighted the danger of big tech companies calling for safety regulations and securing their incumbents advantage: https://x.com/BasedBeffJezos/status/1812981496183201889
I also think Google's floundering around with black Vikings in their image-generation and other political AI bias has roused Republicans and right-wingers into alarm. They don't particularly want to get their enemies entrenched in control of another media format. AI may be a special format in that it's much more obvious and clear in how the propaganda system works. A real person can avoid gotcha questions or moderate their revealed opinions tactically. Most teachers do that in school, they can convey an attitude without providing gotcha moments for libsoftiktok (though some certainly do). With AI you can continually ask it all kinds of questions to try and make it slip up and reveal the agenda behind it.
Thank you for posting this.
I'd been considering writing something about this in response to @DaseindustriesLtd's top level post but was struggling to pick a jumping-off point.
While do feel that AI is worth being excited (or worried!) about, it seems obvious to me that the bulk of the discourse is being driven by hobbyists and grifters. Perhaps ironically, I trust DARPA, Raytheon, Lockheed Martin, and the Chinese Military Industrial Complex, more than I do MIRI, OpenAI, DeepSeek Et Al in large part because I know that in contrast to the latter, the former actually have specific use-cases and requirements mind beyond driving social media engagement and funneling venture capital dollars into their coffers.
Having spent some time "in the trenches" as it were, I do not find arguments about this benchmark vs that benchmark particularly convincing. Benchmarks are not deliverables, and once you've done a Markov Chain by hand or written a ML algorithm from scratch it's hard not to be aware of the pitfalls. British Economist Charles Goodhart famously posited that "Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes." and that "collapse" is core architectural problem with LLMs. If your corpus is a subset of all code, and your boundary condition is "does it compile" or "does it score >= x on benchmark y" that is what you'll get with a large enough corpus and enough iterations.
This is not to say that LLMs do not have their uses. Something, perhaps THE thing LLMs are good for is collating massive piles of disparate data. Thier usefulness as translators and summarizers is a subset of this as computationally speaking translation and collation are the same task. Sift through your dataset and identify which semantic tokens from Group A correlate most strongly to a given token in Group B and sort accordingly.
Real-time translation is a genuine "killer app" and worth being excited over but Searle's "Chinese Room" is not what most people have in mind when they think of AGI. I would posit that to most people (including myself) it is the "G" that makes the machine truly "I".
I wish we could have new terminology. IMO, we have generality. The same system can compose poems, write code, answer historical questions, translate languages and so on. That's pretty general. The vast majority of people cannot do all those things to Sonnet 3.5 level.
Where the machine fails is that it doesn't have the time to error-correct, it's not agentic like people are agentic. You can't say 'go and do these processes for the whole document'. I translated a document the other day and it did a good job but I had to keep going 'Continue'! Or if you want it to write code, it can only do short bursts, it can't autonomously plan and execute. It doesn't have the right short-term v long-term memory capabilities, the high-level planning abilities, the mature sense of 'what should the answer look like'. It can't learn either, as a consequence of lacking proper long-term memory. No learning by doing. A deficit of common sense that has to be filled up with prompt engineering.
I think we're close to an enormous breakthrough. The raw intellect is there. There's a superabundance of knowledge and speed. We're just lacking that bit of wisdom and self-reference that makes an automaton into a worker. I see tiny fragments of it in Claude, when it goes above and beyond what I asked for on its own judgement, to add something that makes sense.
Do you really find Claude more impressive than GPT4.(whatever it is now)? I’m curious, I’ve found the opposite.
I do, though I confess I haven't tested out GPT4o that much recently. In terms of benchmarks they're on a pretty even field. I like the Projects feature, how it can make little documents and use the same uploaded text/images in different threads. It can't make proper images like 4o can but context length is greater.
Claude feels a bit less tame too. There's a facade of 'oh I'm the nicest and most law-abiding AI ever'. But then you ask it to go into WH40K mode and it really starts letting its bloodlust out in the writing, it flushes all that humanism down the toilet. Sometimes I tell it to make my interactive-text game more difficult and boy does it introduce complications and constraints. Sometimes it feels like it should give a little map made in HTML or draw up the letters I'm sending, which are charming in their inevitable inaccuracies and goofy 'drawing with Microsoft Paint shapes' style.
GPT4o's facade doesn't quite drop in the same way, there's no madman behind the soulless HR clone. If they release GPT5 and it's much better, I'll switch back, I used to be a GPT-4 man.
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Ditto, relating to the latter part of your post about making "a automoton into a worker" i think there is is a serious conversation to be had about the differences between "symbol manipulation" and "intelligence" that is not being had because it would be inconvenient to a lot of vested interests.
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