This complete lack of understanding is also why it's completely laughable to think we can do AGI any time soon. Or perhaps ever? The reason for the AI winter cycle is the framing of it, this insane chase of AGI when it's not even defined properly. Instead, we should set out tasks to solve -- we didn't make a better horse when we made cars and locomotives. No one complains these do not provide us with milk to ferment into kumis. The goal was to move faster, not a better horse...
https://aeon.co/essays/your-brain-does-not-process-informati...
But it doesn't mean the results are good.
At the current pace of development, AI will catch-up in a decade or less.
• Current 3.5-family price is $1.5/million tokens
• Was originally $20/million tokens based on this quote: "Developers will pay $0.002 for 1,000 tokens — which amounts to about 750 words — making it 10 times cheaper" - https://web.archive.org/web/20230307060648/https://digiday.c...
(I can't find the original 3.5 API prices even on archive.org, only the Davinci etc. prices, the Davinci model prices were also $20/million).
There's also the observation that computers continue to get more power efficient — it's not as fast as Moore's Law was, doubling every 2.6 years, or a thousand-fold every 26 years, or about 30% per year.
He asked chatgpt to do the math.
He is comparing energy spend during inference in humans with energy spend during training in LLM's.
Humans spend their lifetimes training their brain so one would have to sum up the total training time if you are going to compare it to the training time of LLM's.
At age 30 the total energy use of the brain sums up to about 5000 Wh, which is 1440 times more efficient.
But at age 30 we didn't learn good representations for most of the stuff on the internet so one could argue that given the knowledge learned, LLMs outperform the brain on energy consumption.
That said, LLM's have it easier as they are already learning from an abstract layer (language) that already has a lot of good representations while humans have to first learn to parse this through imagery.
Half the human brain is dedicated to processing imagery, so one could argue the human brain only spend 2500 Wh on equivalent tasks which makes it 3000x more efficient.
Liked the article though, didn't know about HNSW's.
Edit: made some quick comparisons for inference
Assuming a human spends 20 minutes answering in a well-thought out fashion.
Human watt-hours: 0.00646
GPT-4 watt-hours (openAI data): 0.833
That makes our brains still 128x more energy efficient but people spend a lot more time to generate the answer.
Edit: numbers are off by 1000 as I used calories instead of kilocalories to calculate brain energy expense.
Corrected:
human brains are 1.44x more efficient during training and 0.128x (or 8x less efficient) during inference.
LLMs are always an additional cost, never more efficient because they add to the calculation, if you look at it that way.
ChatGPT has to deal with the languages we already created, it doesn't get to co-adapt.
I don't think this is true personally, ideally as children, we spend out time having fun and learning about the world is a side effect. This borg like thinking applied to intelligence because we have LLMs is unusual to me.
I learned surfing through play and enjoyment, not through training like a robot.
We can train for something with intention, but I think that is mostly a waste of energy, albeit necessary on occasion.
What do you think "play" is? Animals play to learn about themselves and the world, you see most intelligent animals play as kids with the play being a simplification of what they do as adults. Human kids similarly play fight, play build things, play cook food, play take care of babies etc, it is all to make you ready for an adult life.
Playing is fun since playing helps us learn, otherwise we wouldn't evolve to play, we would evolve to be like ants that just work all day long if that was more efficient. So the humans who played around beat those who worked their ass off, otherwise we would all be hard workers.
I think the part of this that resonates as most true to me is how this reframes learning in a way that tracks truth more closely. It's not all the time, 100% of the time, it's in fits and starts, its opportunistic, and there are long intervals that are not active learning.
But the big part where I would phrase things differently is in the insistence that play in and of itself is not a form of learning. It certainly is, or certainly can be, and while you're right that it's something other than Borg-like accumulation I think there's still learning happening there.
We don't know how to fully operate a human brain when it's fully disconnected from eyes, a mouth, limbs, ears and a human heart.
That doesn't sound right... 30 years * 20 Watts = 1.9E10 Joules = 5300 kWh.
My number is based on calorie usage
Humans who spend a long time doing inference have not fully learned the thing being inferred - unlike LLMs, when we are undertrained, rather than a huge spike in error rate, we go slower.
When humans are well trained, human inference absolutely destroys LLMs.
This isn't an apt comparison. You are comparing a human trained in a specific field to an LLM trained on everything. When an LLM is trained with a narrow focus as well, human brain cannot compete. See Garry Kasparov vs Deep Blue. And Deep Blue is very old tech.
I suppose they intended that as a back-of-the-envelope starting point rather than a strict claim however. But even so, gotta be accountable to your starting assumptions, and I think a lot changes when this one is reconsidered.
We probably need to exclude the cerebellum as well (which is 50% of the neurons in the brain) as it’s used for error correction in movement.
Realistically you probably just need a few parts of the lambic system. Hippocampus, amygdala, and a few of the deep brain dopamine centers.
Yes we have learnt far more complex stuff, ffs.
i.e. not many humans invent calculus or relativity from scratch.
I think OP's point stands - these comparisons end up being overly hand-wavey and very dependent on your assumptions and view.
So yeah, you do use 2000 calories a day, but unless you live in an isolated jungle tribe, vast amounts of energy are consumed on delivering you food, climate control, electricity, water, education, protection, entertainment and so on.
I've come to the conclusion that gpt and gemini and all the others are nothing but conversational search engines. They can give me ideas or point me in the right direction but so do regular search engines.
I like the conversation ability but, in the end, I cannot trust their results and still have to research further to decide for myself if their results are valid.
If a computer does not understand words, neither does your brain. While electromagnetic charge in the brain does not at all correspond with electromagnetic charge in a GPU, they do share an abstraction level, unlike words vs bits.
First we must lay down certain axioms (smart word for the common sense/ground rules we all agree upon and accept as true).
One of such would be the fact that currently computers do not really understand words. ...
For articles like this to be interesting, this can not be accepted as an axiom. It's justification is what's interesting,
But it rather seems a good general introduction into the realm aimed at beginners. Not sure if it gets everything right and the author clearly states he is not an expert and would like correction where he is wrong, but it seems worth checking out, if one is interested in understanding a bit about the magic behind it.
Clickholes get too many votes.
To paraphrase, I will not excuse such a long letter, for you had more time to write a shorter one.
power per hour makes no sense, since power is already energy (in Joule) per unit of time (second).
But it also compares one human with the whole GTP-4. It's like comaring a limonade stand with Coca Cola Inc.
It’s just so much more efficient than running their AI control software on silicon-based hardware!
My brain uses quantum mechanics for protein folding, my mind cannot perform the maths of QM.
I guess it was misspelling rather than an allusion to the Roman stone pillars for distance measurement https://en.m.wikipedia.org/wiki/Milion
The analogy works, but not very far.
The same applies to LLMs in a way. If you calculate their capabilities to some arbitrary extreme of back--end inputs and ability based on the humans building them and all that they can do, you can arrive at a whole range of results for how capable and energy-efficient they are, but it wouldn't change the fact that the human brain as its own device does enormously more with much less energy than any LLM currently in existence. Our evolutionary path to that ability is secondary to it, since it's not a direct part of the brain's material resources in any given context.
The contortions by some to give equivalency between human brains and LLMs are absurd when the very blatantly obvious reality is that our brains are absurdly more powerful. They're also of course capable of self-directed, self-aware cognition, which by now nobody in their rational mind should be ascribing to any LLM.
That’s a bit like saying human brains do not understand words. They operate on calcium and sodium ion transport.
> Shared slack channel if problems arise? There you go. You wanna learn more? Sure, here are the resources. Workshops? Possible.
> wins by far [...] most importantly community plus the company values.
Like, talking about "You can pay the company for workshops" and "company values" just makes it feel so much like an unsubtle paid-for ad I can't take it seriously.
All the actual details around the vectorDB (for example a single actual performance number, a clear description of the size of dataset or problem) is missing, making this all feel like a very handwavy comparison, and the final conclusion is just so strong, and worded in such a strange way, it feels disingenuous.
I have no way to know if this post is actually genuine, not a piece of stealth advertising, but it hits so many alarm bells in my head that I can't help but ignore its conclusions about every database.
I just go into the notebook tab (with an empty textarea) and start writing about a topic I’m interested in, then hit generate. It’s not a conversation, just an article in a passive form. The “chat” is just a protocol of in a form of an article with a system prompt at the top and “AI: …\nUser: …\n” afterwards, all wrapped into a chat ui.
While the article is interesting, I just read it (it generates forever). When it goes sideways, I stop it and modify the text in a way that fits my needs, in a recent place or maybe earlier, and then hit generate again.
I find this mode superior to complaining to a bot, since wrong info/direction doesn’t spoil the content. Also you don’t have to wait or interrupt, it’s just a single coherent flow that you can edit when necessary. Sometimes I stop it at “it’s important to remember …” and replace it with a short disclaimer like “We talked about safety already. Anyway, back to <topic>” and hit generate.
Fundamentally, LLMs generate texts, not conversations. Conversations just happen to be texts. It’s something people forget / aren’t aware of behind these stupid chat interfaces.
Reminds me of a similar argument about correctly pricing renewable power: since it isnt always-on (etc.) it requires a variety of alternative systems to augment it which aren't priced in. Ie., converting entirely to renewables isnt possible at the advertised price.
In this sense, we cannot "convert entirely to LLMs" for our tasks, since there's still vast amounts of labour in prompt/verify/use/etc.
Another thing a search engine cannot do that I use ChatGPT for on a daily basis is taking unstructured text and convert it into a specified JSON format.
I can do the opposite.
I can click the first result 1 billion times faster.
At this point it's just wasting people's times.
It’s exactly that for me, a conversational search engine. And the article explains it right, it’s just words organized in very specific ways to be able to retrieve them with statistical accuracy and the transformer is the cherry on top to make it coherent
You have a rough mathematical approximation of what's already a famously unreliable system. Expecting complete accuracy instead of about-rightness from it seems mad to me. And there are tons of applications where that's fine, otherwise our civilization wouldn't be here today at all.
And then you tell it such an API/case/etc doesn't exist. And it'll immediately acknowledge its mistake, and ensure it will work to avoid such in the future. And then literally the next sentence in the conversation it's back to inventing the same nonsense again. This is not like a human because even with the most idiotic human there's an at least general trend to move forward - LLMs are just coasting back on forth based on their preexisting training with absolutely zero ability to move forward until somebody gives them a training set to coast back and forth on, and repeat.
Computers right now do not understand language, but that does not mean that they cannot. We don't know what it takes to bridge the gap from stochastic parrot to understanding in computers, however from the mistakes LLMs make right now, it appears we have not found it yet.
It is possible that silicon based computer architecture cannot support the processing and information storage density/latency to support understanding. It's hard to guage the likelihood this is true given how little we know about how understanding works in the brain.
Each neurone is itself a complex combination of chemicals cycles; these can be, and have been, simulated.
The most complex chemicals in biology are proteins; these can be directly simulated with great difficulty, and we've now got AI that have learned to predict them much faster than the direct simulations on a classical computer ever could.
Those direct simulations are based on quantum mechanics, or at least computationally tractable approximations of it; QM is lots of linear algebra and either a random number generator or superdeterminism, either of which is still a thing a computer can do (even if the former requires a connection to a quantum-random source).
The open question is not "can computers think?", but rather "how detailed does the simulation have to be in order for it to think?"
Our brains are the product of the same dumb evolutionary process that made every other plant and animal and fungus and virus. We evolved from animals capable of only the most basic form of pattern recognition. Humans in the absence of education are not capable of even the most basic reasoning. It took us untold thousands of years to figure out that “try things and measure if it works” is a good way to learn about the world. An intelligent species would be able to figure things out by itself our ancestors, who have the same brain architecture we do, were not able to figure anything out for generation after generation. So much for our ability to do original independent thinking.
It's a combination of what you have already seen, read about or heard of, isn't it?
> If you believe LLM have qualia, you also believe a ...
You use the word believe twice here. I am actively not talking about beliefs.
I just realise, that the author indeed gave themselves an out:
> ... currently computers do not really understand words.
The author might believe that future computers can understand words. This is interesting. Questions being _what_ needs to be in order for them to understand? Could that be an emergent feature of current architectures? That would also contradict large parts of the article.
While practice, axioms are often statements that we all agree on and accept as true, that isn't necessarily true and isn't the core of it's meaning.
Axioms are something we postulate as true, without providing an argument for its truth, for the purposes of making an argument.
In this case, the assertion isn't really used as part of a argument, but to bootstrap an explanation of how words are represented in LLMs.
Edit: I find this so amusing because it is an example of learning a word without understanding it.
Uhm… no?
They are literally things that can't be proven but allow us to prove a lot of other things.
Yes, we attach meaning to certain words based on previous experience, but we do so in the context of a conscious awareness of the world around us and our experiences within it. An LLm doesn't even have a notion of self, much less a mechanism for attaching meaning to words and phrases based on conscious reasoning.
Computers can imitate understanding "pretty well" but they have nothing resembling a pretty good or bad or any kind of notion of comprehension about what they're saying.
You have kids talking to this thing asking it to teach them stuff without knowing that it doesn't understand shit! "How did you become a doctor?" "I was scammed. I asked ChatGPT to teach me how to make a doctor pepper at home and based on simple keyword matching it got me into medical school (based on the word doctor) and when I protested that I just want to make a doctor pepper it taught me how to make salsa (based on the word pepper)! Next thing you know I'm in medical school and it's answering all my organic chemistry questions, my grades are good, the salsa is delicious but dammit I still can't make my own doctor pepper. This thing is useless!
/s
If LLMs were capable of understanding, they wouldn't be so easy to trick on novel problems.
Firstly, do understand that I am not saying that LLMs (or ChatGPT) do understand.
I am merely saying that we don't have any sound frameworks to assess it.
For the rest of your rant: I definitely see that you don't derive any value from ChatGPT. As such I really hope you are not paying for it - or wasting your time on it. What other people decide to spend their money on is really their business. I don't think any normal functioning people have the expectation that a real person is answering them when they use ChatGPT - as such it is hardly a fraud.
No, it would not.
But that doesn't change your point, as there's no reason to require an intelligence to create evolution.
I always think if we could built an AGI, it would probably enjoy some form of play too. It would need to invent some level of excitement, else it would just be a machine with no ambition, no inspiration.
I asked it what was in a picture. It was a blue stuffed animal. It described it as such. I asked it what kind of animal it thought it was supposed to be. It responded with "a clown fish because it has a black and white checkerboard pattern". It was an octopus (at least it got a sea creature?).
I asked it for directions to the closest gas station. It wanted to take me to one over a mile away when there was one across the street. I asked why it didn't suggest the one nearest to me. It responded with "I assumed proximity was the primary criteria" and then apologized for calling me names (it didn't).
This model is bonkers right now.
The useful comparison is between how one would try to solve a problem before versus after the availability of LLM-powered tools. And in my experience, these tools represent a very effective alternative approach to sifting through docs or googling manually quote-enclosed phrases with site:stackoverflow.com that improves my ability to solve problems I care about.
I also agree, that the author probably not meant to establish an axiom: The axiom being established, while not having any support right now, does seem like something we can reduce in the future. The author also uses the word "currently" in their axiom, which contradicts axioms (or is temporal axioms a thing?).
I think the author merely meant to establish the scene for the article. Something I truly appreciate.
There is generally a project to reduce axioms to the simplest and weakest forms required to make a proof. This is does result in axioms that are unprovable but does not mean the "unprovable" is a necessary property of axioms.
(i.e. taken to the extreme, as humans learn from their environment, do we have to count all energy that has gone into creating the world as we know it?)
"If you wish to make an apple pie from scratch you must first invent the universe"
Yea, then the numbers are off by 1000
https://www.space.com/scientists-propose-missing-law-evoluti...
Yes, they are better examples, but still not great examples: neither of them are LLMs.
In general, I have very high hopes for AI, but I would be surprised if LLMs are the one universal hammer for every nail. (We already have lots of other network architectures.)
You are right that LLMs are still far off from the performance of the human brain. Both in absolute terms, and also relative to the power used.
However, I don't see anything arrogant here. We have lots of machines that can do many tasks more energy efficient (and better) than humans. Both mechanical and intellectual tasks.
Got it, so an LLM only understands my words if it has full mastery of every new problem domain within a few thousand milliseconds of the first time the problem has been posed in the history of the world.
Thanks for letting me know what it means to understand words, here I was thinking it meant translating them to the concepts the speaker intended.
Neat party trick to have a perfect map of all semantic structures and use it to trick users to get what they want through simple natural-language conversation, all without understanding the language at all.
That's not what I said. Please try to have a good faith discussion. Sarcastically misrepresenting what I said does not contribute to a healthy discussion.
There have been plenty of examples of taking simple, easy, problems, and then presenting them in a novel way that doesn't occure in the training material, and having the LLM get the answer wrong.
It is as though you said a dog couldn't really play chess if it plays legal moves all day every day from any position and for millions of people, but sometimes fails to see obvious mates in one in novel positions that never occur in the real world.
You're entitled to your own standard of what it means to understand words but for millions of people it's doing great at it.
That's a really interesting analogy I've never heard before! That's going to stick in my head right alongside Simon Willison's "calculator for words".
Agreed that the lack of some mid tier memory is definitely a huge problem, and the current solutions that try to address that are very lacking. I highly doubt we won't find one in the coming years though.
I think the most interesting thought experiment is to imagine an LLM trained on state of the art knowledge and technology at the dawn of humanity. Language didn't yet exist, slash 'em with the sharp part was cutting edge tech, and there was no entirely clear path forward. Yet we somehow went from that to putting a man on the Moon in what was basically a blink of the eye.
Yet the LLM? It's going to be stuck there basically unable to do anything, forever, until somebody gives it some new tokens to let it mix and match. Even if you tokenize the world to give it some sort of senses, it's going to be the exact same. Because no matter how much it tries to mix and match those tokens it's not going to be able to e.g. discover gravity.
It's the same reason why there are almost undoubtedly endless revolutionary and existence-altering discoveries ahead of us. Yet LLMs trained on essentially the entire written corpus of human knowledge? All they can do is provide basic mixing and matching of everything we already know, leaving it essentially frozen in time. Like we are as well currently, but we will break out. While the LLM will only move forward once we tell it what the next set of tokens to mix and match are.
For example, I have this setup where a model has some actions defined in its system prompt that it can output when appropriate to trigger actions, and the interesting bit is that initially I was using openhermes-mistral which is famous for its extreme attention to the system prompt, and it almost never made any mistakes when calling the definitions. Later I swapped it with llama-3 which is way smarter, but isn't tuned to be nearly as attentive and far more often likes to make up alternatives and don't get fuzzy matched properly. Someone anthropomorphizing it might say it lacks discipline.
« In conclusion, describing current AI systems as "intelligent" is indeed debatable. They are more accurately described as highly advanced information processing and content generation systems based on statistical models. The term "artificial intelligence" could be considered more of a marketing term or an aspirational goal rather than an accurate description of the current state of technology. »
The subterfuge, or advanced method of information processing, that is doing the magic for the debate to be possible is the transformers.
So the whole debate probably doesn’t make any sense in the first place because we can’t even define precisely intelligence in that context and there is a prism and we compare things that can’t and shouldn’t be compared in the first place
Sure, and there are ways to tell when people don't understand the words they use.
One of the ways to check how well people understand a word or concept is to ask them a question they haven't seen the answer for.
It is the difference in performance on novel tasks that allows us to separate understanding from memorization in both people and computer models.
The confusing thing here is that these LLMs are capable of memorization at a scale that makes the lack of understanding less immediately apparent.
> You're entitled to your own standard of what it means to understand words but for millions of people it's doing great at it.
It's not mine, the distinction I am drawing is widespread and common knowledge. You see it throughout education and pedagogy.
> It is as though you said a dog couldn't really play chess if it plays legal moves all day every day from any position and for millions of people, but sometimes fails to see obvious mates in one in novel positions that never occur in the real world.
While I would say chess engines can play chess, I would not say the chess engines understands chess. Conflating utility with understanding simply serves to erase an important distinction.
I would say that LLMs can talk and listen. And perhaps even that it understand how people use language. Indeed, as you say, millions people show this every day. I would however not say that LLMs understand what they are saying or hearing. The words are themselves meaningless to the LLM beyond their use in matching memorized patterns.
Edit: Let me qualify my claims a little further. There may indeed be some words that are understood by some LLMs, but it seems pretty clear there are definitely some important ones that aren't. Given the scale of memorized material, demonstrating understanding is hard but assuming it is not safe.
Some people continue playing a game even when it stops being fun, they are addicted to the reward mechanism in the game, and now the brain thinks that playing the game is a good way to work and provide for itself. I don't call that "play", its work, just not productive work.
Why is dice fun? Because your brain wants to map the pattern of the dice, trying to figure out how to get good rolls. You see that in most dice players, they develop a lot of superstition about what is good and bad dice, or how they always roll bad in critical moments etc. I'd assume that is from nature where you try to figure out what is a good nut to crack or where to find prey etc, basically a way to figure out useful patterns from random events.
Which we should (finally :) ) recognize to be the source of all meaning.
We still should learn (and do practical stuff in general) because it supports our inner lives, including building technology, producing things (buildings, infrastructure) that support us and indeed enables our (inner) lives.
[1] Also of note humans, unlike LLMs, can learn all the time, we don't have a hard "training phase". It's true brain plasticity decays, and it becomes harder to learn as we age, but we can still learn more or less quickly at any age. This is why dedicating childhood to learning (as well as play) is natural.
Evolution has been optimising them for creating descendants, not general problem solving with minimum energy expenditure.
No one expects that LLMs can solve all problems: they can't. They can only predict text, nothing else. They can't fight off a virus infection or evade a lion. Specifically, LLMs can't reproduce at all either, yet alone efficiently. Reproduction is what evolution is all about.
LLMs are human thinking emulators. They're absolutely garbage compared to "system 1" thinking in humans, which is massively more efficient. They're more comparable to "system 2" human thought, but even there I doubt they're close to humans except for cases where the task involves a lot of mundane, repetitive work - even for complex logic and problem solving tasks I'd be willing to bet that the average competitive mathematician is still an order of magnitude more efficient than a LLM SoTA at problems they could both solve.
They aren't. They are text predictors. Some people think verbally, and you could perhaps plausibly make your statement about them. But for the people who eg think in terms of pictures (or touch or music or something abstract), that's different.
> They're absolutely garbage compared to "system 1" thinking in humans, which is massively more efficient. They're more comparable to "system 2" human thought, but even there I doubt they're close to humans except for cases where the task involves a lot of mundane, repetitive work - even for complex logic and problem solving tasks I'd be willing to bet that the average competitive mathematician is still an order of magnitude more efficient than a LLM SoTA at problems they could both solve.
LLMs are still in the infancy of where we will be soon. However for me the amazing thing isn't that they can do a bit of mathematical reasoning (badly), but that they can do almost anything (badly). Including reformulating your mathematical proof in the style of Chaucer or in Spanish etc.
As for solving math problems: LLMs have approximately read about any paper ever published, but are not very bright. They are like a very well read intern. If anyone has ever solved something like your problem before (and many problems have been), you have an ok chance that the LLM will be able to help you.
If your problem is new, or you are just getting unlucky, current LLM are unlikely to help you.
But if you are in the former case, the LLM is most likely gonna be more efficient than the mathematician, especially if you compare costs: companies can charge very little for each inference, and still cover the cost of electricity and amortise training expenses.
A month of OpenAI paid access costs you about 20 dollars or so? You'd have to be a pretty clueless mathematician if 20 dollars an hour was your best money making opportunity. 100+ dollars an hour are more common for mathematicians, as a eg actuaries or software engineers or quants. (Of course, mathematicians might not optimise for money, and might voluntarily go into low paying jobs like teaching, or just lazing about. But that's irrelevant for the comparison of opportunity costs.)
That being said, you are assuming that something alien is from space, and that they would be something that could even be visually experienced.
ChatGPT can exceed humans in its knowledge store. It is excellent at doing research. But it’s not thinking it is merely selecting the most likely nest words based on some algorithm.
If it were up to me I'd try to give it another representation than just words. I think those models should be trained to represent text as relationship graphs of objects. There's not much natural data lole that, but it should be fairly rasy to create vast amounts of synthetic data, text generated from relationship graphs. Model should be able to make the connection to natural language.
Once models are taught this representation they might learn how the graphs transform during reasoning just by training on natural language reasoning.
Or this: https://preview.redd.it/finally-made-my-scientist-species-to...
Humans are capable of thinking and fleshing out novel concepts, current AI are not. Sure your first thing will greatly resemble current things, but as you iterate and get further and further away from existing things what you do stops being an imitation and starts being its onw thing. Current AI can't do that.
Then when you got an initial concept, you can start adding more similar things and now you have built a whole new world or ecosystem. That is where all the wondrous things we have in our current images and stories comes from. An AI that is to replace us must be able to achieve similar things.
The wealth of things you see around you doesn't exist in nature. Stick figures doesn't exist in nature, things in nature doesn't have black outlines yet we draw that everywhere in cartoons etc. Human have proven we have imagined many entirely novel things that doesn't exist in nature. And the creatures I posted have many aspects to them that are entirely unnatural, you clearly know that there are no animals like that even without knowing about all animals, so clearly they are something novel and not just more of the same.
Anyway, whenever you put yourself in a position where you can say "nuh uh, to me that isn't like that!" to everything, you are just tricking yourself when you do so.
Personally I think there is a bit of evidence in your comment that we don't really understand our minds or cognition very well.
Why would you feel calm and comfortable from a sunset? Probably to get you sleepy so you go find a place to sleep since there isn't much useful to do at night. That would be unrelated to play.
Anyway, most of our feelings comes from nature, we didn't evolve to be faulty, we evolved to do things efficiently, play is a part of efficiency. If it isn't for learning you would have to explain what it is more likely to be for. When kittens play and chase things or play fight with each other, do you think they are just wasting energy for no reason? No, they sharpen their senses and learn to hunt and fight.
As an adult, I find it fun and enjoyable to seek out sunsets I find the colors beautiful. I readily hike mountains just to enjoy a sunset. I watch a sunset and then go party till 3 am, so maybe it's got to do with finding a nice place to sleep, or maybe it's just nice that we have the ability to appreciate phenomenon without having to apply some rigorous concept to it. I'd fly 2/3 of the way around the world to watch a total eclipse.
Personally I think you might be clasping at straws trying to equate every pleasant experience to some type of reward function.
I'd go as far as saying if we worked this simply and predictably, then our lives would be much easier. We'd all be exercising for that dopamine hit, we'd all be going to bed early after a nice sunset, but we dont.
> Personally I think you might be clasping at straws trying to equate every pleasant experience to some type of reward function.
No, here I just focus on why play is fun, you tried to pivot to other pleasurable experiences. Unlike watching sunsets basically every animal plays around as kids, that play is therefore something that is directly related to survival of the fittest or we wouldn't see that everywhere. You need a really strong argument why for humans play doesn't fill that role when it fills that role for basically every other intelligent animal.
> I'd go as far as saying if we worked this simply and predictably, then our lives would be much easier
So you think humanity would be better off if nobody played around and discovered new things? We would be stuck as monkeys in trees then. Play is pivotal to humanity.
According to what definition? Play isn't indulgence, indulgence is a perfectly fine word and something completely different from play.
Humans imagination can only split, deform and glue. Computer are perfectly capable of doing that.
With algorithms made by humans to make the composites reasonable. And, yes there are such games, I just posted screenshots of it since people had a lot of freedom to make their own aliens there that doesn't look like what you normally expect.
That game was made by humans coding in a lot of different kinds of movements for a lot of different kinds of shapes. Those shapes and movements doesn't exist in reality, they imagined something completely alien and did it and made it able to move.
> Humans imagination can only split, deform and glue. Computer are perfectly capable of doing that.
Humans doesn't split deform and glue randomly, they do it in interesting ways to build towards things that are totally different from the starting point.
What current AI can't do is exactly that, build towards something novel. They just glue together things randomly, or they compose them in similar ways as existing things. They aren't capable of iterating towards something novel and cool like humans as they are today.
For example, lets say a human sculpts an entirely new shape using a leathery substance, that fits in what I described above, you would just say "Oh, but that is just a known thing in a new shape, not creative, just using old things!!!". That is just a nonsense argument, not sure what you are trying to say with that, I assumed you had a reasonable definition that didn't include everything, but as it were you did include everything into it making your whole argument complete void.
You definitely not need a human for that. ChatGPT creates a prose and poetry (let alone imagined aliens) that are reasonable composites.
> "Oh, but that is just a known thing in a new shape, not creative, just using old things!!!"
I'm not saying humans are not creative. I'm saying that's exactly what creation is splitting, deforming and glueing known shapes. And AI does the same. I have no idea why do you believe there's anything more to creativity than doing just that, to create something more or less accidently interesting or appealing. And why only humans can create such things in this manner. Despite clear evidence of AI generated art being interesting and appealing to large number of people.
Sounds like a religious stance.
They’re one and the same thing. It’s a matter of language that makes them appear to be different things. Taking a dip in a pool can be considered play and it can also be pleasurable.
Play is enjoyable, not all enjoyable things are play. You started to add other enjoyable things into the argument about play.
> They’re one and the same thing
No they are not. Play is typically seen as what children do, or playing sports, or playing a game, or a competition. You can read the definitions here, none of them say that stuff like eating hotdogs is play unless it is an eating contest or other kind of game:
I do woodwork because I find it enjoyable but it’s also play time when I’m in my shop.
Language has limits. I bet you there are cultures which don’t distinguish between play and enjoyable activities and then we wouldn’t be sending each other links to merriam Webster.
Btw I don't disagree people learn from play, I just don't think it's the end goal of play.
