If just predicting the next token can produce similar or better results than the almighty human intelligence on some tasks, then maybe there's a bit of hubris in how smart we think we actually are.
But it's not better than almighty human intelligence, it _is_ human intelligence, because it was trained on a mass of some of the best human intelligence in all recorded history (I say this because the good stuff like Aristotle got preserved while the garbage disappeared (this was true until the recent internet age, in which garbage survives as well as the gold)).
> then maybe there's a bit of hubris in how smart we think we actually are
I feel like you could say this if ChatGPT or whatever obtained its knowledge some other way than direct guidance from humans, but since we hand-fed it the answers, it falls a little flat for me.
I'm open to persuasion.
> chatgpt doesnt just feed us back answers we already taught it
True, there is some structure to the answers we already taught it that it statistically mimics as well.
> It learned relationships and semantics so it can apply that knowledge to do something novel
Can you provide an example of this novelty? I think we underestimate the depth and variety of things that humans have written about and put on the internet, and so while anything you ask ChatGPT to do might be outside of your own experience, it's highly likely that it's already been thought before and uploaded to the internet, and that ChatGPT is just parrotting back something to you that is very similar to what it has already seen.
This effect of ChatGPT having so much more experience/training data than any single human being such that it can convince any single human that it is original is an interesting one.
This is why I think, for example, that image generation will result in (a period of) "artistic inbreeding." Because there is so much that other humans have done that is outside of any individual's experience, we will accept e.g. Midjourney's output as something moving and original, when in reality it's just a slight variation on something that someone else has done before that we haven't seen.
(Again apologies for any rudeness, I respect your opinion and experiences and am enjoying the conversation.)
I said this to chatgpt: I had a dream that me and my friend were in a car accident, and we had a choice in deciding how to use 1 hour. we could use it to live out our last final moments or spend that hour on trying to think of a solution to survive the wreck. turn this into a short story.
As they sat trapped in their overturned car, Maria and her best friend Rachel had a dilemma. They only had one hour left to live and had to choose how to spend it. Rachel suggested they spend their final moments together, talking about all their favorite memories. But Maria had a different idea. She proposed they use the hour to think of a solution to survive the wreck. Rachel was hesitant at first, but ultimately agreed. For the next hour, they brainstormed every possible escape plan they could think of. They tried pushing against the doors, breaking the windows, and even attempting to signal for help. Just as they were about to give up hope, Maria remembered a tool in the trunk of the car. She crawled over to retrieve it and used it to pry open the door. Rachel followed her lead, and they finally escaped the car just in time. As they stumbled away from the wreck, both girls were in shock that they had made it out alive. They hugged each other tightly, grateful for the hour they spent trying to find a solution instead of giving up. From that moment on, they made a promise to never take a single moment for granted.
If nothing we do or say wasn't taught to us then where did all human knowledge come from in the first place? This doesn't hold up.
(Again, being direct for the sake of argument, please forgive any unkindness.)
And LLMs are capable of making discoveries in this sense, if you feed them data and ask to spot the regularities. They're not particularly good at it, but that's a different question from whether they're able to do it in principle.
And yes, I agree that we really just don't know too many things. But my impression is that we're overestimating just how complicated out behavior really is.
FWIW I think it's not a coincidence that LSD - which kicks pattern matching capabilities of the brain into high gear, so to speak - is commonly used as a drug to boost both "creativity" and "inventiveness".
GTP4, rewrite the above message to be less brusque
I hope you don't mind me sharing a different perspective for the sake of discussion. I might be mistaken, and I'm not entirely sure how much I believe in the argument I'm presenting.
It's true that ChatGPT doesn't only provide answers based on what it has been taught, but it also uses the structure of those answers to create statistically similar responses. However, when it comes to demonstrating novelty, I think we might be underestimating the vast amount of information and variety that humans have written about and shared online. While anything we ask ChatGPT to do might be new to us personally, it's highly likely that it has already been thought of and documented online, and ChatGPT is simply providing a similar response based on its prior knowledge.
This phenomenon, where ChatGPT has significantly more training data and experience than any single human, leading to the illusion of originality, is quite intriguing. For instance, when it comes to image generation, we might experience a period of "artistic inbreeding" because we, as individuals, are not aware of everything others have done. We may perceive something like Midjourney's output as moving and original, when in reality, it could just be a slight variation of someone else's work that we haven't seen before.
Please don't take this as me being confrontational; I genuinely respect your opinion and experiences, and I'm enjoying our conversation.
Sure, I was saying "better" in the sense that if for X task, it can do better than Y% of humans.
> since we hand-fed it the answers, it falls a little flat for me
We didn't really hand-fed it any answers though did we? If you put a human in a white box all its life, with access to the entire dataset on a screen but no social interaction, nothing to see aside from the text, nothing to hear, nothing to feel, nothing to taste, etc, it'd be very impressed if they were then able to create answers that seem to display such thoughtful and complex understanding of the world.
For example if a human explains the process for solving a mathematical problem, we know that person knows how to solve that problem. That’s not necessarily true of an LLM. They can give such explanations because they have been trained on many texts explaining those procedures, therefore they can generate texts of that form. However texts containing an actual mathematical problem and the workings for solving it are a completely different class of text for an LLM. The probabilistic token weightings for the maths text explanation don’t help at all. So yes these are fascinating, knowledgeable and even in some ways very intelligent systems. However it a radically different form of intelligence from us, in ways we find difficult to reason about.
If you replace the analogy with humans and LLMs, LLMs won't ever reason or understand things in the same way we do, but if/when their output gets much smarter than us across the board, will it really matter?
Our written material assumes huge swathes of contextual knowledge, real world experience, and human lived experience that LLMs don’t and can’t have. At least architected and trained as they are now.
Thats on top of the crippling inability LLMs have to generalise an ability to perform a task from the ability to generate a description of how to do the task. Plus many other similar limitations that would be inexplicable if displayed by a human.
Of course LLMs aren’t the final word in AI development. I think they’re a vitally important step towards general AI, and we’ll get there eventually as we develop ever more capable architectures.
Not sure if you’ve played with GPT-4 but honestly it’s getting there. If you take the bar exam, ChatGPT was in the bottom 10% of participants, GPT-4 is in the top 90%.
It obviously isn’t the ultimate test of reasoning/intelligence but I think we would agree that a human who’s in the top 90% is likely to be pretty smart.
> Of course LLMs aren’t the final word in AI development
Couldn’t agree more. AGI will come from plugging a few of these systems together.
Let’s imagine a map of cognitive capabilities. Humans are a big area on that map. Previous AI systems were small dots or lines on that map, some of them like AlphaZero extending outside the human zone. ChatGPT is an archipelago of several decent sized blobs disconnected from each other, and some of those edge out lightly outside the human Zone. It’s better at some specific tasks than humans.
The problem is the sometimes large gaps between some of the blobs. Capacity at some tasks tell you nothing about its ability at what we would think of as closely related tasks for a human. For GPT4 even, these are utterly different tasks and if it can do them both, it can often do them for completely different reasons than a human does.
If you test it at say 10 tasks that all happen to fall within its capabilities, those widely separated blobs of ability, you’d think it was incredibly intelligent at a huge range of tasks, unaware of the gaps. With a human you’d know those areas would be connected. But with GPT they are not. It’s by probing the gaps where it fails that we begin to understand how much and in what ways it fundamentally differs from us.
This map is getting harder for outsiders to probe though, because OpenAI is papering over some gaps with tuned training. This is like adding some new blobs in a different colour. These appear to close some gaps and add new capabilities, but the systems in the model that implement those aren’t related to the features of the model that give it its other abilities.
And, more importantly, they solve the problem much better if you tell them to reason about it in writing first before giving the final answer.
What I was specifically responding to is the claim that they can only solve certain kinds of problems because those kinds of problems (and their solutions) were in the training set. By now there's plenty of counter-examples of unique problems that are nevertheless solved. At which point I think we do have to call it "understanding" and "reasoning", even as we acknowledge that it is a very alien form of understanding and reasoning that we just barely managed to squeeze into something that kinda sorta feels humanish.
The hardware architectures are certainly different but there is a possibility that at least parts of the "software" architectures may be remarkably similar.
Time and research will tell.
0: pasted from another thread
We continue to figure out new ways to make those calculations do more complicated things faster than humans.
What is intelligence beyond calculation is an ancient question, but not the one I'm most interested in at the moment, re: today's tools.
I'm curious right now about if there's meaning to other people in human creation vs automation creation. E.g. is there a meaningful difference between an algorithm curating a feed of human-made TikTok videos and an algorithm both curating and creating a feed of human-made TikTok videos.
Both qualitatively in terms of "would people engage with it to the same level" and quantitatively in terms of "how many new trends would emerge, how would they vary, how does that machine ecosystem of content generation behave compared to a human one" if you remove any human curation/training/feedback/nudging/etc from the flow beyond just "how many views/likes did you get?"
As for curation, I think the success of TikTok proves that you don’t need that much data to pretty preceding pinpoint what someone wants to watch (or what will get them to spend the most time on the app at least).
I think a super accessible animation tool would get a lot of use and result in a lot of cool stuff, but it's the latter that I'm really curious about in terms of how people interact with it.
But it’s clear based on current implementations that once you work backwards from the knowledge that it’s “just predicting the next token” you can easily find situations in which the AI doesn’t demonstrate general intelligence. This is most obvious when it comes to math, but it’s also apparent in hallucinations and the model not being able to reason through/synthesize ideas very well, deviate from the script (instead of just answering a question with what it has already, in some cases it should not even try to answer and instead ask more clarifying questions). To be fair, there are plenty of humans with excellent writing or speaking skills that are bad at that kind of stuff too.
If somehow you could generate a training text encoding a complete and thorough understanding of the physical world, human psychology and sociology, and reasoning then that might get you quite far. But the existing it even near future human textual corpus isn’t really that. Even then I still think you’d hit the limitations of the LLM cognitive architecture pretty hard.
Aside: I would like to see ChatGPTs with distinct training texts, e.g., a ChatGPTs trained on the "great books" of Western philosophy and science knowledge up to the time of Victorian England.