We just need to assign liability by ownership/initiation: if your "agent" destroys something, even though you didn't tell it to (because it had "agency"), you should be liable for the damages.
I love how we all just collectively decided that LLM decisionmaking cannot possibly be like human decisionmaking - because if it were, the consequences would be just too awkward.
All that while still not knowing how either kind actually works.
> I love how we all just collectively decided that LLM decisionmaking cannot possibly be like human decisionmaking - because if it were, the consequences would be just too awkward.
In love how people get salty about people not going along with a superficial supposition just because they can’t definitively prove it wrong.
> All that while still not knowing how either kind actually works.
We do know that zero parts of human decision making are based on predicting the next most likely letter based on a giant internet-based database. We do know that’s what LLMs do. We do know exactly how each part of an LLM works even if the combined behavior is too cryptic to feasibly analyze at the moment. We do not understand all of the functions of an actual neuron. Openworm isn’t even close to accurately simulating the 302 neurons of a roundworm and you’d need over 200 million roundworms working in conjunction to equal the number of neurons in one human brain.
My dog seems convinced that the malevolent invader in a mailman uniform would break in and attack us if she didn’t fiercely bark at him, six days per week. I certainly can’t prove the mailman doesn’t want to kill us, and that the mailman wasn’t solely deterred by her barking. Empirically, the mailman goes away soon after she starts barking, and we’ve sustained zero mailman assaults after hundreds of purported attempts. Maybe I should just run with it? Her model is too simple to come up with the obviously correct answer, but it’s not even directionally accurate.
The burden of proof is on the person making the claim, which in this case, is that these comparatively simple logical constructs are remotely comparable to the complexity of biological systems.
Disagree about the burden of proof. We have no better model for how human decision making works than LLMs. Humans are constantly predicting the next moment. We certainly have a different “tokenizer” and training set, but many of the concepts underpinning LLMs are both biologically inspired and, likely, have similar consequences and emergent architectures.
> We certainly have a different “tokenizer” and training set, but many of the concepts underpinning LLMs are both biologically inspired and, likely, have similar consequences and emergent architectures.
My kids tricycle certainly has a different gear setup and wheel diameter, but many of the concepts underpinning the tricycle are both inspired by F1 race car enineering and, likely, have similar consequences and emergent architectures.
It sucks because I think analogies can be useful in helping people make a mental model of complex things, which is meaningfully beneficial. The problems happen when people aren’t honest about the limits of the analogies, which is damned-near guaranteed to happen with this stuff.
> We have no better model for how human decision making works than LLMs
This is a claim that requires a lot of citations.
> biologically inspired
Nature inspires a lot of creation, but superficial similarities don’t mean other aspects are similar. Making an extremely realistic sculpture of a soufflé, even using a foam medium, doesn’t bring me any closer to being a chef, doesn’t mean I know anything about albumen foams, sauces, and heat transfer, and it doesn’t bring me any closer to having dinner ready. Browning on top of a soufflé is evidence of maillardization. You could pull up some studies on that and claim the brown on top of the soufflé sculpture, which I applied with an airbrush, proved that the Maillard reaction was occurring, and if another person didn’t know anything about cooking, they might even believe you. It would, of course, be completely wrong. And the other person, of course, could loudly exclaim that I can’t prove that there was no maillardization.
> Humans are constantly predicting the next moment
This is really not my experience of consciousness.
Is it yours??
Do you sit in meetings predicting what’s going to happen next? No, you sit there bored out of your f$$@ing mind, daydreaming about being somewhere else and doing something useful with your life.
God help me if that’s what LLMs are doing when I ask them to build me a web site.
They have shown that your mind is doing exactly that due to the delays in consciousness. There are very simple examples that you can try to see it. It’s especially clear in perception.
It’s interesting that our conscious interpreter doesn’t let us know that this is going on like you are experiencing, it must be that it’s advantageous for us to not think about the prediction part of our mind.
If someone in that meeting quickly raised a hand in an arc, you would notice the “about to throw something” pattern, look and notice the hand holds an eraser, analyze the arc and predict possible flight paths of the eraser. Then possibly notice the hand is now holding its position and the owner is actually looking down at the table. Maybe to squash somethingMust be something on the table. Maybe a spider! Better look. Wait now many people are moving away, oh someone spilt some water and the eraser is actually the guys phone and he is checking to see if his laptop is safe from the spilt water.
Fortunately you are on the other side of the table and predict the water isn’t going to splash for otherwise flow onto your stuff.
All your possible responses result in you tossing a napkin towards the spill.
Our brains are always pattern matching and predicting. I bet you tried to reason out where I was going with my comment before you finished reading it.
This is a fascinating illustration that I can't help but agree with. However I feel like there's something more — that this part of my brain is a bunch of supportive background processes running without my real awareness. It's how I can drive home safely with no memory of how I got there (…sober), even though driving is an action that's incredibly demanding of intelligence. I can be driving home while thinking about a really hard problem at work that I haven't solved.
However, if I came around a corner and saw a car in the wrong lane, a tree across the road, a fire raging — I'd very quickly jump into the mental driver's seat and turn my conscious intelligence fully at this problem and come up with the best possible outcome I can think of in a short period of time — losing all ability to think about that work problem. I'd remember that incident for sure.
Similarly, in your story, all those predictive moments are happening below the person's level of consciousness. They're possibly even speaking to the group about a problem at the same time and thinking deeply about something.
I'm not smart enough to know, but I tend to feel like LLMs are much more like the predictive part of our thinking that you described, but that human cognition has something more — the single-threaded, creative, problem-solving part that is very conscious.
Is it possible that LLMs represent only one part of the way we think? And there's a whole separate mechanism that's fundamentally different, and not based on pattern matching and prediction?
> ... are based on predicting the next most likely letter based on a giant internet-based database. We do know that’s what LLMs do.
If you're claiming that the training objective tells us what kind of internal mechanisms the training produced, then I think that's just plain wrong.
Next-token prediction describes the optimization target, not the internal mechanisms that the training produced.
In the same way for the natural evolution of humans, DNA replication is the evolutionary objective. It's not a description of the internal mechanisms that evolution has produced.
As an example, we know that neural networks can be trained to develop generalized algorithms for arithmetic.
They might first memorize the training examples, then with further training transition to a solution that generalizes correctly to unseen examples.
In some cases we've even reverse-engineered the evolved internal mechanisms and found structured arithmetic algorithms rather than rote memorization. Interestingly, for modular addition this can involve Fourier representations, which isn't an algorithm I would have guessed gradient descent training of neural networks would produce.
You can try to say that I’m arguing whatever you like. If you’re claiming that the underlying structure of digital so-called neural networks is comparable to biological neural networks— which we’ve studied for far longer without really understanding— no amount of jargon will obviate the ‘citation needed’ requirement for that claim.
> You can try to say that I’m arguing whatever you like.
I did my honest best possible interpretation of what you really meant from what you wrote.
>> We do know that zero parts of human decision making are based on predicting the next most likely letter based on a giant internet-based database. We do know that’s what LLMs do.
I read this as "The decision making of LLMs are based on predicting the next most likely letter based on a giant internet-based database."
Is that wrong?
I understood that your meaning was something like "LLMs can't reason, they just output likely letters"?
> If you’re claiming that the underlying structure of digital so-called neural networks is comparable to biological neural networks
No, I don't claim that.
What do claim is this: Regardless of how the LLMs were trained, they show overwhelming signs of being able to reason, and not just recall memorized information.
This doesn't mean that they always reason perfectly about everything.
But if they only memorized things and output the next likely letter, you would see them answering very badly much more often.
I always wonder what makes people take the other side of this argument. They do it quite passionately. Why actively encourage viewing LLMs as human? Who is that benefitting?
Does the argument require benefit? Isn’t the argument based on caution?
I haven’t heard many people explicitly saying “these things behave like humans”, but more generally “we don’t even know how to define human consciousness, we don’t have a thorough grasp of how the brain works, we are still very much in the dark on a lot of these topics, so how can we say one way or the other?”
In other words, agnosticism: I don’t know.
In general, it’s baffling to me that anyone has an unshakable opinion on what exactly is happening. It seems like raw egotistical hubris.
1) Humans have a bias / tendency to attribute human qualities to things that appear or act human, but aren’t.
2) When that happens, people jump to conclusions by stretching the human analogy too far.
3) Since humans have a bias to do this, we should have a bias against anthropomorphising LLMs.
It’s easier to believe LLMs act like humans because there’s so much evidence to support that. You have to actively use your brain to convince yourself otherwise. Another reason why we should have a bias against using human behavior to describe LLM behavior.
But I agree. “I don’t know” is a good stance. But I think “I don’t know, probably not” is a better stance if only to combat our (or at least my) natural bias.
