Did nobody really question the usability of language models in designing war strategies?
Did nobody really question the usability of language models in designing war strategies?
Correct, people heard “AI” and went completely mad imagining things it might be able to do. And the current models act like happy dogs that are eager to give an answer to anything even if they have to make one up on the spot.
LLM are just plagiarizing bullshitting machines. It’s how they are built. Plagiarism if they have the specific training data, modify the answer if they must, make it up from whole cloth as their base programming. And accidentally good enough to convince many people.
To be fair they’re not accidentally good enough: they’re intentionally good enough.
That’s where all the salary money went: to find people who could make them intentionally.
GPT 2 was just a bullshit generator. It was like a politician trying to explain something they know nothing about.
GPT 3.0 was just a bigger version of version 2. It was the same architecture but with more nodes and data as far as I followed the research. But that one could suddenly do a lot more than the previous version, so by accident. And then the AI scene exploded.
It was the same architecture but with more nodes and data
So the architecture just needed more data to generate useful answers. I don’t think that was an accident.
It kind of irks me how many people want to downplay this technology in this exact manner. Yes you’re sort of right but in no way does that really change how it will be used and abused.
“But people think it’s real AI tho!”
Okay and? Most people don’t understand how most tech works and that doesn’t stop it from doing a lot of good and bad things.
I’ve been through a few AI winters and hype cycles. It made me very cynical and convinced many overly enthusiastic people will run into a firewall face first.
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If that’s really how they work, it wouldn’t explain these:
https://notes.aimodels.fyi/researchers-discover-emergent-linear-strucutres-llm-truth/
https://adamkarvonen.github.io/machine_learning/2024/01/03/chess-world-models.html
Yes. There is self organization and possibility to self reflection going on in something that wasn’t designed for it. That’s going to spawn a lot more research.
I will read those, but I bet “accidentally good enough to convince many people.” still applies.
A lot of things from LLM look good to nonexperts, but are full of crap.
https://poke-llm-on.github.io/
Reinforcement learning. Cool project. Still no need to “know” anything. I usually play this type of have with short rules and monitoring the current state.
https://adamkarvonen.github.io/machine_learning/2024/01/03/chess-world-models.html
However, this only worked for a model trained on a synthetic dataset of games uniformly sampled from the Othello game tree. They tried the same techniques on a model trained using games played by humans and had poor results. To me, this seemed like a major caveat to the findings of the paper which may limit its real world applicability. We cannot, for example, generate code by uniformly sampling from a code tree.
Author later discusses training on you data versus general datasets.
I am out of my depth, but does not seem to provide strong evidence for the modem not just repeating information that shows up a lot for the given inputs.
https://arxiv.org/abs/2310.02207
2 author paper with interesting evidence. Again, evidence not proof. Wait for the papers that cite this one.
A cool paper. Using the LLM to judge value of new inputs.
I am always skeptical of summaries of journal articles. Even well meaning people can accidentally distort the conclusions.Still LLM is a bullshit generator that can check bullshit level of inputs.
https://notes.aimodels.fyi/researchers-discover-emergent-linear-strucutres-llm-truth/
References a 2 author paper. I am not an expert in the field, but it is important to read the papers that reference this one. Those papers will have criticisms that are thought out. In general, fewer authors means less debate between the authors and easier to miss details.
Would you like to play a game?
How about a nice game of chess?
It’s better than you at chess:
It’s better than you at chess
Did you actually watch the video? It only “played” good during the opening, where there were still existing games. Then it proceeded to make some illegal moves and completely broke down in the endgame. Also, all the explanation it gave for its moves made no sense.
I did, it played very well in the middle game, already out of book
Here is an alternative Piped link(s):
https://piped.video/watch?v=wJzSHRNyspg
Piped is a privacy-respecting open-source alternative frontend to YouTube.
I’m open-source; check me out at GitHub.
I see we have 5 GMs who disagree
Here is an alternative Piped link(s):
https://www.piped.video/watch?v=NHWjlCaIrQo
Piped is a privacy-respecting open-source alternative frontend to YouTube.
I’m open-source; check me out at GitHub.
Of course, LLM is simply copying the behavior of most people, and most people would resort to that as well.
And they probably trained it on Civ, and Gandhi was chosen as the role model.
Makes a lot of sense AI would nuke disproportionately. For an AI, if you do not set a value for something, it is worth zero. This is actually the base problem for AI: Alignment.
For a human, there’s a mushy vagueness about it but our cultural upbringing says that even in war, it’s bad to kill indiscriminately. And we value the future humans who do not yet exist, we recognize that after the war is over, people will want to live in the nuked place and they can’t if it’s radioactive. There’s a self-image issue where we want to be seen as a good person by our peers and the history books. There is value there which is overlooked by programmers.
An AI will trade infinite things worth 0 for a single thing worth 1. So if nukes increase your win percentage by .1%, and they don’t have the deterrence of being labeled history’s greatest monster, they will nuke as many times as they can.
That explanation is obviously based on traditional chess AI. This is about role-playing with chatbots (LLMs). Think SillyTavern.
LLMs are made for text production, not tactical or strategic reasoning. The text that LLMs produce favors violence, because the text that humans produce (and want) favors violence.
Especially if its training material included comments from the early 00s. There was a lot of “nuke it from orbit” and “glass parking lot” comments about the Middle East in the wake of 911.
And with the glorified text predictors that LLMs are, you could probably adjust the wording of the question to get the opposite results. Like, “what should we do about the Middle East?” might get a “glass parking lot” response, while “should we turn the middle East into a glass parking lot?” might get a “no, nuking the middle East is a bad idea and inhumane” because that’s how those conversations (using the term loosely) would go.
The text that LLMs produce favors violence, because the text that humans produce (and want) favors violence.
That’s not necessarily true, there is a lot of violent fiction.
For AGI, sure, those kinds of game theory explanations are plausible. But an LLM (or any other kind of statistical model) isn’t extracting concepts, forming propositions, and estimating values. It never gets beyond the realm of tokens.
Get Matthew Broderick on the horn!
AI is Civilization’s Gandhi.
…how shocking
It’s a WAR GAME. Emphasis on war and game. Do you chuckle fucks think wargame players should emphasize kumbaya sing dance or group therapy sessions in their games?
If the goal is to win and overwhelming force is an option, that option will always win. On the contrary, in the modern world, humans tend to try to find non-violent means in order to bring an end to wars. The point is that AI doesn’t have humanity but is still being utilized by militaries (or at least that’s what I think)
And a language model, absolutely unsuited for this task, just as much as a lawnmower or a float needle.
Ever heard of skynet anybody ?
How about WOPR?
DUN DUN DUN - DUN DUNN
I am shocked—shocked!—to find out that a technology performs poorly when applied to a task it’s completely unsuited for!
Did nobody really question the usability of language models in designing war strategies?
They got some nice clickbait out of it. And that’s how dumb af ideas turn into smart career moves.
I hope no one is coming away with the idea that this about something the military is actually doing.
whaaat? Robots don’t just have their own inherent sense of morality for whatever reason???
She’s just like me!
Violence in a war game?! Oh my!
Whenever we have disrupting technological advancements, DARPA looks at it to see if it can be applied to military action, and this has been true with generative AI, with LLMs and with sophisticated learning systems. They’re still working on all of these.
They also get clickbait news whenever one of their test subjects does something whacky, like kill their own commander in order to expedite completing the mission parameters (in a simulation, not on the field.) The whole point is to learn how to train smart weapons to not do funny things like that.
So yes, that means on a strategic level, we’re getting into the nitty of what we try to do with the tools we have. Generals typically look to minimize casualties (and to weigh factors against the expenditure of living troops) knowing that every dead soldier is a grieving family, is rhetoric against the war effort, is pressure against recruitment and so on. When we train our neural-nets, we give casualties (and risk thereof) a certain weight, so as to inform how much their respective objectives need to be worth before we throw more troopers to take them.
Fortunately, AI generals will be advisory to human generals long before they are commanding armies, themselves, or at least I’d hope so: among our DARPA scientists, military think tanks and plutocrats are a few madmen who’d gladly take over the world if they could muster a perfectly loyal robot army smart enough to fight against human opponents determined to learn and exploit any weaknesses in their logic.
