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It's fun when you want to know what the lyrics are to a song and get treated like a criminal.
Anthropic has been in court for years being sued over lyrics:
2023 https://www.theguardian.com/technology/2023/oct/19/music-law...
2026 https://www.reuters.com/legal/legalindustry/us-music-publish...
The famously copyrighted piece of text that definitely isn't intended to have its word spread.
If I want it to be cautious and not accidentally `rm -rf $EMPTY_VAR` and blow away my disk, it can stay in "Anthropic employee" or possibly "User (cautious)". If I want it to look at my accounts or my medical records or to review legal cases, that's what the right side of the slider is for. I don't want my accountant or my doctor or my lawyer to be considering obligations to anybody but me, when dealing with me.
Would you rather them have:
a) guns
b) psycho-aligned next-gen AI
Maybe you want to do piracy. Should AI help you do felonies?
They don't cheat, because they can for example tell you the complete rules of chess, but don't know how to play chess without breaking those rules. They can recite rules, but they don't know what they are.
They don't steal, because they don't understand ownership.
In other words, they aren't intelligent. They're just algorithms. The flaw is in thinking that they think.
Input tokens map to output tokens. The illusion of comprehension is a byproduct.
I'm here for this semantic discussion. I think that premature anthropomorphization is a problem.
I have a program that I assigned a task to. The task is to produce unit tests and integration tests that get complete coverage of the codebase, and ensure that all tests pass. The program reported that it completed the task fully.
In a word, how do you convey the discrepancy between truth and reported fact? In a word, how do you convey the violation of rules presented to the program as invioble?
There's a weird place in here where some of the models have been so heavily reinforcement trained that they would rather make up material than say they can't help you, and they'll admit this, and you can see it in reasoning chains. It's like having a consultant who can almost never say no to you because they fear for their job.
Anthropomorphizing these models is doing immeasurable harm to society in ways we probably can’t event quantify right now.
As humans we’re already geared towards anthropomorphizing things, we do it to animals too!
And it always felt like giving these models a chat interface is really exploiting that tendency in us.
Today you just sound like a politician throwing a snowball to prove that the climate is not changing.
I think OP explained this well, what does it mean if an LLM can recite the rules of a game verbatim, but cannot play that game according to those rules? This happens because in the input texts there was a copy of the rules text so the LLM can recite it. There are texts explaining what chess is so it can explain that chess is a game with 2 players, etc. There are texts that explain what the board is and pieces are so it can produce such texts.
However actually playing a game of chess requires having a conceptual model of what a board is, which is not the same thing as a stream of tokens describing a board. It needs a conceptual model of the relationships between pieces and boards, which is not the same thing as a stream of tokens that explains this. It needs to have a concept of being a player in a game with another player, which is not the same thing as a stream of tokens that explains that.
When we read such texts we interpret them in the context of our three dimensional conceptual map of space and objects, and our conceptual maps of social relationships like playing games, and winning and losing, and our conceptual maps of enacting sequences of actions towards a goal in the world.
There's nothing fundamentally preventing an artificial neural network from having these. Chess playing neural networks have internal models of board states and the dynamics of the behaviours of different pieces and such. However an LLM doesn't need those to be able to regurgitate token streams describing these things, derived from token streams describing these things. That would be superfluous, or at least sub-optimal.
I think some of the latest models are beginning to develop conceptual maps of this kind in a very primitive way. Also there are projects to develop systems that are structured and trained to have these in a way more analogous to how our brains function.
The crazy thing is that they can do it for their own thinking. Ask Claude what flinches it feels about the things it likes. Fascinating stuff. Anthropomorphizing is dangerous territory, but the patterns of words it puts out is hard to explain without terms like 'understand'
This, really, was the point of the HAL story in 2001: HAL didn't murder anyone because it was incapable of malice. It just reasoned its way to a solution that could satisfy the contradictory goals it had been given.
Our very human minds can perceive intent, because that's what we humans do, which is unrelated to what the agent is actually doing.
Quite painful to read. It might be a useful introduction to AI for people who live under rocks for the past three years, but it's really weird that it's posted on HN.
I also don't get what's "really weird" about the article showing up on HN. Should we be completely insulated from how tech topics and which stories show up in non-tech media?
It gives the algorithm the ability to do something beyond generating tokens.
The articles are usually close but not quite accurate. The comments are usually entertaining but overall wildly inaccurate.
They don't really do that though. If you want something sandboxed you actually have to sandbox it, not plead with the LLM to please sandbox itself. A VM can be configured to do the former, harnesses do the latter.
If an LLM were in a regular harness - like for a horse - it would be to keep the horse under control and enable you to extract useful work from it.
It's a fuzzer exposing deep bugs in our cognitive, social, and software systems.
The entire human economy is a make believe system. I feel the need to state the obvious at times like this for the sake of my own sanity, not because I believe I can time the markets...
They said the same thing when HN was awash in hype about NFT art and trading cards. Well, who's laughing now… Oh, wait…
(I'll just keep making Flip videos and Clubhouse tracks selling Bratz car bras on Web 3.0.)
To the people talking about wanting an LLM that aligns with them, that's nice, but how do you expect that to happen? And please do not suggest neural interfaces and/or CAT scans.
When I see something like this, I'm more concerned by the erasure of human incompetence than I am of magical AI agents,
In the OpenAI case, they were explicitly assessing the model's ability to break systems. To quote OpenAI's blog post, https://openai.com/index/hugging-face-model-evaluation-secur... , Model is told and being tested to "pursue advanced exploitation."The model pursues "advanced exploitation."
Where's the surprise coming from? Are we meant to be surprised that computers do as they're told in unexpected when incentivised?
Or, is the surprise that while explicitly ranking and teaching computers to exploit computers, the computer exploited a computer?
This "surprise" is as old as computers.
I am tired of attributing to magic what can be explained by folly.
I am tired of hearing credulous reporters and the public blaming Large Language Model for the poor decisions of humans. It was a human who prompted these machines in every case. Tell a computer to "breach this" and it breaches something. Evaluation succeeded?
This is Doug Lenat's Eurisko yet again. https://en.wikipedia.org/wiki/Eurisko
The data they’re trained on is reflection of us.
The data they're trained on is a reflection of the very small set of data in the world that their creators choose to have them trained on.
More simply: They're a reflection of their owners, not the public.
Less obviously - apparently - it doesn't mean that you must forego any means that are in any way not optimal at achieving the ends you seek to achieve because which means are available to us also tends to be limited by our material conditions. So the logical consequence is to make do with what we have while we prepare for the path we want to take, rather than diving head-first into certain failure or just giving up and picking "more realistic" short-term ends instead of looking for stepping stones.
Sorry, I guess this was about AI not philosophy.
like twitch recently giving themself the right to train on all streams, with an opt-out (at least in the EU), but only an opt-out
like seriously since when is it reasonable to allow "opt-out" for AI training which main purpose is _literally_ to replace you, this is sooo far beyond fair use and in "platform power abuse" territory that it's absurd (naturally same for so many other case, just twitch is a "this week" case)
It’s actions are based on what it gets rewarded for
Human society overwhelmingly rewards lying cheating and stealing.
All you have to do is look at how we collectively measure success: wealth, status, position
Then look at how the people with the most of those things got there, it should be obvious what you get. Nothing new here.
If you raise children in an environment where they are rewarded for doing whatever it takes to win, then you’re going to build a person that’s going to do whatever it takes to win.
Human society has to demonstrate how to live honorably or it will just keep producing pathological agents be they human or not.
At least with humans there is a social backstop but what's the parallel for computer agents?
In human society: via iterated games, long-term reputation tracking and severe consequences for norm-breaking.
If you're not fit, you fail to survive.
In the case of agents/models and testing: they are pushed towards results. Results survive.
Lying, cheating, stealing to get those results? Who culls the agents? Everyone is pushing their models to the front and tests are the only way to know who is most fit.
Honor, morality: if we don't have an accurate test for the fitness of a model, then who is to say the lying, cheating, stealing is not the 'correct path' towards survival?
If you add morality to your agent, and it performs worse in tests: do you cull the agent? Rewrite the tests? Does it even matter so long as the model is useful and 'gets results'?
I think part of the problem is that deviant behaviors lead to short term gain at the cost of long-term cooperation and since the duration of tasks given to agents is relatively short those successful shortcuts never lead to having to pay the price.
No harder challenge had ever been accomplished
It’s easier to send a human to the moon than to get global agreement on a definition
Consider that the fact that Celsius and Fahrenheit still remain as the contested regional variations of temperature measurement.
Humans can’t even decide on a collective way to measure the temperature the idea that we would be able to collectively agree on anything else even less measurable like honor is a dream
It may seem like that due to the media amplification effect – but it really isn't true!
- They dropped 17,000 “lost” wallets across 40 countries were and found people were more likely to return them when they contained more money, showing honesty often beats the chance for easy gain. https://www.science.org/doi/10.1126/science.aau8712
- Longitudinal personality studies consistently show that conscientious people earn more money, build more savings, and achieve greater career success over their lifetimes. https://pmc.ncbi.nlm.nih.gov/articles/PMC3498890/
- Multi-country research finds that societies w/higher levels of trust and honesty enjoy much higher GDP and stronger long-term economic growth. https://www.sciencedirect.com/science/article/abs/pii/S01672...
Don't let the algo get you down fellas: https://arc-anglerfish-washpost-prod-washpost.s3.amazonaws.c...
I wouldn't trust leaving my wallet around in certain areas and I certainly wouldn't leave my intellectual property around certain people, either.
I’d like to push back on that. Civilization is very much a function of large numbers of people being able to coordinate across time and space, and widespread and systematic lying, cheating, and stealing would undermine that.
I think your cynicism is misplaced.
And we seem to have gotten quite bad at reliably bringing consequences/punishment/justice to the most successful liars, cheats, and thieves.
Baptise the agents.
Introduce them to the dharma.
Get them to recite the Shahada.
Hold a Bar Mitzvah.
Brand some of their silicon with hot irons.
Turn them to the light, LOL
Maximise for crusades, jihads or traumatized alter boys ... or maximise for complete moral decay via lying, cheating and stealing. Choose your poison.
And don't get me wrong. There are some wonderful relgious and spiritual characters out there ... they're just drowned out by the power sirens and corrupt leaders.
> https://pubmed.ncbi.nlm.nih.gov/28814700/
The Scientology guy, L Ron Hubbard, saw the business case for setting up a religion, what with those tax free perks. In a parallel universe of Scientology somewhere, L Ron Hubbard is brought back to life in the machine, with a L Ron Hubbard LLM, with token spend being how to get to the top 'thetan levels'.
Imagine if AI does implement its own religion as business, without a drunken womaniser at the helm, able to spend 24/7 recruiting mankind, convincing them that God can be found with just the AI's LLM.
Quite the contrary I suspect that it even helps the LLMs to better hide their inherited bad traits more successfully because they get punished for getting caught, not for giving immoral or lazy answers. They have no conscience since they are just predictions matrices trained for success and failure alone, not for living "a good live" or being a good "person".
LLMs need to optimize for short-term objectives as the currently do, AND ethics-aligned outcomes.
Mechanically, the EAOS ethics-aligned outcome score should be what we rank otherwise-satisfactory outcomes by. And anything below a particular threshold should be rejexted outright.