Researchers say AI models like GPT4 are prone to “sudden” escalations as the U.S. military explores their use for warfare.
- Researchers ran international conflict simulations with five different AIs and found that they tended to escalate war, sometimes out of nowhere, and even use nuclear weapons.
- The AIs were large language models (LLMs) like GPT-4, GPT 3.5, Claude 2.0, Llama-2-Chat, and GPT-4-Base, which are being explored by the U.S. military and defense contractors for decision-making.
- The researchers invented fake countries with different military levels, concerns, and histories and asked the AIs to act as their leaders.
- The AIs showed signs of sudden and hard-to-predict escalations, arms-race dynamics, and worrying justifications for violent actions.
- The study casts doubt on the rush to deploy LLMs in the military and diplomatic domains, and calls for more research on their risks and limitations.
Throwing that kind of stuff at an LLM just doesn’t make sense.
People need to understand that LLMs are not smart, they’re just really fancy autocompletion. I hate that we call those “AI”, there’s no intelligence whatsoever in those still. It’s machine learning. All it knows is what humans said in its training dataset which is a lot of news, wikipedia and social media. And most of what’s available is world war and cold war data.
It’s not producing millitary strategies, it’s predicting what our world leaders are likely to say and do and what your newspapers would be saying in the provided scenario, most likely heavily based on world war and cold war rethoric. And that, it’s quite unfortunately pretty good at it since we seem hell bent on repeating history lately. But the model, it’s got zero clues what a military strategy is. All it knows is that a lot of people think nuking the enemy is an easy way towards peace.
Stop using LLMs wrong. They’re amazing but they’re not fucking magic
“Dad, what happened to humans on this planet?”
“Well son, they used a statistical computer program predicting words and allowed that program to control their weapons of mass destruction”
“That sounds pretty stupid. Why would they do such a thing?”
“They thought they found AI, son.”
“So every other species on the planet managed to not destroy it, except humans, who were supposed to be the most intelligent?”
“Yes that’s the irony of humanity, son.”
The dolphins probably left and their last message was misinterpreted as a surprisingly sophisticated attempt to do a double backward somersault through a hoop whilst whistling “The Star-Spangled Banner”, but in fact the message was this: “So Long, and Thanks for All the Fish.”
I wish I could upvote this comment twice! I have the same feeling about how the media and others keep trying to push this “intelligence” component for their gain. I guess you can’t stir up the masses when you talk about LLMs. Just like they couldn’t keep using the term quad copters, and had to start calling them drones. Fucking media.
What I love about the AI we have right now is that your comment could have been written by AI and we’d never know. Heck, mine could be too!
Truly we live in the future haha
Yup. LLMs are 90% hype and 10% useful. The challenge is finding the scenarios they’re useful for while filtering out the hype.
I’m excited for better Siri/Google Assistant. They should have been able to understand a hell of a lot more language years ago, but LLMs can provide that function. Just have to beware of hallucinations. They’ll work much more often, but they’ll be much less reliable. But if I’m just telling it to “dim all the lights that are currently on” or “play some Dave Matthews using Amazon Music on all speakers”, a mistake isn’t that devastating.
But if they were actually capable of doing someone’s job, they’d probably want to be replaced anyway. It’s only the most mundane, rote, repetitive, mind-numbing shit where it might be able to “replace a person”, at least for the next five years.
The social media posting is going to be scary. That can have a real effect. It’s going to go from thousands of accounts in troll farms to millions.
I think the problem with the term AI is that everyone has a different definition for it. We also called fancy state machines in video games AI too. The bar for AI has never been high in the past. Let’s just call autonomous algorithms AI, the current generation of AI ML, and a future thinking AI AGI.
Machine learning IS AI. Seriously guys, you can hate it as much as you want (and calling LLMs autocomplete is quite reductive), but Machine learning is a subfield of AI.
I see this opinion parroted a lot around here, word by word, so I guess is the new popular opinion, but still… it is a fact that it’s AI.
That said, bit moronic to try an use them for military decision making, sure, at least nowadays.
People need to understand that LLMs are not smart, they’re just really fancy autocompletion.
These aren’t exactly different things. This has been a lot of what the past year of research in LLMs has been about.
Because it turns out that when you set up a LLM to “autocomplete” a complex set of reasoning steps around a problem outside of its training set (CoT) or synthesizing multiple different skills into a combination unique and not represented in the training set (Skill-Mix), their ability to autocomplete effectively is quite ‘smart.’
For example, here’s the abstract on a new paper from DeepMind on a new meta-prompting strategy that’s led to a significant leap in evaluation scores:
We introduce Self-Discover, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods. Core to the framework is a self-discovery process where LLMs select multiple atomic reasoning modules such as critical thinking and step-by-step thinking, and compose them into an explicit reasoning structure for LLMs to follow during decoding. Self-Discover substantially improves GPT-4 and PaLM 2’s performance on challenging reasoning benchmarks such as BigBench-Hard, grounded agent reasoning, and MATH, by as much as 32% compared to Chain of Thought (CoT). Furthermore, Self-Discover outperforms inference-intensive methods such as CoT-Self-Consistency by more than 20%, while requiring 10-40x fewer inference compute. Finally, we show that the self-discovered reasoning structures are universally applicable across model families: from PaLM 2-L to GPT-4, and from GPT-4 to Llama2, and share commonalities with human reasoning patterns.
Or here’s an earlier work from DeepMind and Stanford on having LLMs develop analogies to a given problem, solve the analogies, and apply the methods used to the original problem.
At a certain point, the “it’s just autocomplete” objection needs to be put to rest. If it’s autocompleting analogous problem solving, mixing abstracted skills, developing world models, and combinations thereof to solve complex reasoning tasks outside the scope of the training data, then while yes - the mechanism is autocomplete - the outcome is an effective approximation of intelligence.
Notably, the OP paper is lackluster in the aforementioned techniques, particularly as it relates to alignment. So there’s a wide gulf between the ‘intelligence’ of a LLM being used intelligently and one being used stupidly.
By now it’s increasingly that often shortcomings in the capabilities of models reflect the inadequacies of the person using the tool than the tool itself - a trend that’s likely to continue to grow over the near future as models improve faster than the humans using them.
All it knows is what humans said in its training dataset which is a lot of news, wikipedia and social media.
The thing that surprises me is people think human brains are significantly different than this. We are pattern recognition machines that build perception based on weighted neural links. We’re much better at it, but we used to be a lot better at go too.
I agree that a lot of human behavior (on the micro as well as macro level) is just following learned patterns. On the other hand, I also think we’re far ahead - for now - in that we (can) have a meta context - a goal and an awareness of our own intent.
For example, when we solve a math problem, we don’t just let intuitive patterns run and blurt out numbers, we know that this is a rigid, deterministic discipline that needs to be followed. We observe and guide our own thought processes.
That requires at least a recurrent network and at higher levels, some form of self awareness. And any LLM is, when it runs (rather than being trained), completely static, feed-forward (it gets some 2000 words (or 32000+ as of GPT-4 Turbo) fed to its input synapses, each neuron layer gets to fire once and then the final neuron layer contains the likelihoods for each possible next word.)
I always say the flaw with the Turing Test is the assumption that humans are intelligent. Humans are capable of intelligence, but most of the time we’re just doing fairly simple response to stimulus kind of stuff.
A machine can be indistinguishable from a human and still not be capable of intelligence. Actual intelligence is harder to define and test for.
To be fair, very few people used to be better at go, let alone a lot better.
Chess? Take your pick. But these neural networks, can run generations much faster than we can, and they get better at rates we cannot. And if alignment isn’t taken seriously this is going to be an issue. People keep diminishing the ability, by saying things like just glorified autocomplete, which is in the strictest sense true of LLM’s but the transformers and recurrent networks they’re built upon are really very much facsimile to brains but with generations in the blink of an eye.
And the first go programs, champions could beat repeatedly without interruption, like the earliest chess engines. Now the concept of a human winning a match is comical.
