6 ms·

> it is clear that actual intelligence has plateaued significantly. > Moving forward, the industry cannot continue to train bigger and bigger models since thei

by wolttam 4mo ago

> it is clear that actual intelligence has plateaued significantly. > Moving forward, the industry cannot continue to train bigger and bigger models since their intelligence not only plateaus but often will get worse These are wild claims - why are we concluding that bigger models and more data = more hallucination? That’s actually the opposite of what’s been happening over the last couple years. Some models may still hallucinate more but they all hallucinate much less than the original 175B ChatGPT which was smaller and trained on (much) less data than anything current. Edit: My mention of data comes from this quote: > A shift is happening among major AI labs, who are becoming increasingly skeptical of endless parameter count and training data scaling My take on the current situation: it seems clear that the industry has seen that there is still a lot left to squeeze out of sub-1T models. But for that you do need more, high-quality data in the distribution which you want to unlock capabilities for.

madduci 4mo ago
Isn't that the case of over fitting? You have more data, but when you ask something that's not in that data, hallucinations happen
an0malous 4mo ago
> why are we concluding that bigger models and more data = more hallucination? That’s not what your quotes said. They said bigger models = plateau in intelligence, nothing about more data or increased hallucinations The relevant quote for what you’re talking about would be: > It’s been proven that when a model is trained on large volumes of highly factual and non-theoretical data, it learns to always have an answer. So there’s two separate claims: 1) bigger models have plateauing results 2) models trained on larger amounts of factual data have a higher hallucination rate I’m pretty sure #1 is well known, I think OpenAI’s own research on scaling laws showed diminishing returns on parameter count and training data volume years ago. I don’t know what the support for #2 is besides for the actual post contents.
- jmalicki 4mo ago
  I find these internet arguments talking about LLMs as if they are trained by reading the internet to be wild. Yes, pretraining still exists. But for the past few years, pretraining by reading the internet is just the initial bootstrapping of LLM training. The RL training they get from bespoke training data, with very very different characteristics than what these armchair analyses claim, dominates these days.
  - mcphage 4mo ago
    Where do they get the bespoke training data from? And how much? I don’t really know anything about this.
    - dominotw 4mo ago
      meta has reallocated a significant protion of their staff to genrating this
      - sroussey 4mo ago
        Meta also reportedly took a 49% nonvoting stake in Scale AI in June 2025 for about $14.3–$14.8 billion.
      - coldtea 4mo ago
        The same company that let their flagship product go to waste, then bet hundreds of billions into the stupid vr metaverse that went nowhere? I'ts not like they really inspire much confidence in their future predictions.
    - blovescoffee 4mo ago
      Companies like Mercor sell data from human experts
      - trothamel 4mo ago
        Offhand, do you know what format that data is in? Is it a question and then a human answering that question? Mostly just curious at to what the training data consists of.
- coffeefirst 4mo ago
  Yeah #2 may be incidental. Suppose one lab focused on bigger, and another on reinforcement training geared towards factual accuracy over sycophancy. You could easily wind up with a model from the second lab that is less powerful but more accurate. I can’t prove it but I suspect there’s a bit of that going on.
  - utkuumur 4mo ago
    I think one problem is that the models that hallucinate often, a few times out of 8 or 16 so that they get good results on benchmarks, most of which measures success out of top k. From benchmark perspective, you don't really care whether 15 of yours 16 generations failed, as long as one succeeded, but as a user you mostly care that 1 out of 16 you get is actually the successful one. I think this effects is more easy to see on Gemini Flash, it hallucinates like crazy but looks like its by design to boost benchmarks.
    - msdz 4mo ago
      > it hallucinates like crazy but looks like its by design to boost benchmarks. Wasn’t there a discussion around some new-ish benchmark _punishing_ hallucination answers (over not replying at all) recently? Maybe in the not-so-distant future, this “spam replies until one’s correct” strategy won’t be able to game a benchmark much at all anymore.
- themgt 4mo ago
  That’s not what your quotes said. They said bigger models = plateau in intelligence, nothing about more data or increased hallucinations ... I’m pretty sure #1 is well known Well known in a multiverse branch where Fable was a dud?
  - an0malous 4mo ago
    No, well known in the current multiverse branch where we still occasionally use things like math and scientific analysis instead of people’s vibe checks and pelican SVGs. Here’s the paper from OpenAI where Dario himself was a co-author: https://arxiv.org/pdf/2001.08361 https://arxiv.org/pdf/2001.08361 > We have observed consistent scalings of language model log-likelihood loss with non-embedding parameter count N, dataset size D, and optimized training computation Cmin, as encapsulated in Equations (1.5) and (1.6). Conversely, we find very weak dependence on many architectural and optimization hyperparameters. Since scalings with N,D,Cmin are power-laws, there are diminishing returns with increasing scale.
    - themgt 4mo ago
      instead of people’s vibe checks and pelican SVGs. Right, what happened is everyone went to Fable and asked it to make the very best bicycle pelican SVG, no mistakes. And Fable's bicycle pelican SVGs were such timeless masterpieces, we all instantly got AI psychosis. Happily, you were immune to this.
      - coldtea 4mo ago
        > We have observed consistent scalings of language model log-likelihood loss with non-embedding parameter count N, dataset size D, and optimized training computation Cmin, as encapsulated in Equations (1.5) and (1.6). Conversely, we find very weak dependence on many architectural and optimization hyperparameters. Since scalings with N,D,Cmin are power-laws, there are diminishing returns with increasing scale.
- deleted 4mo ago
  [deleted]
- ifwinterco 4mo ago
  #2 is not that surprising from first principles if the way you made the bigger model was by feeding it poorer quality training data because it’s the only way you can get enough
coldtea 4mo ago
>These are wild claims - why are we concluding that bigger models and more data = more hallucination? Because that's what they measured in this case.
eurekin 4mo ago
> A shift is happening among major AI labs, who are becoming increasingly skeptical of endless parameter count and training data scaling I'm pretty sure it's mostly due to the training data quality. No idea, why this never gets mentioned in those discussions. It was obvious right from the get go, that the scaling law just enabled some abilities, that were described by the underlying data and allowing the ANN to abstract it in the latent space.
blurbleblurble 4mo ago
How do we know gpt 5.5 is a bigger model
- Phelinofist 4mo ago
  Since it was created by _Open_AI surely it's really open and we can check, right? SCNR
harrall 4mo ago
In cognitive science, it appears your brain has two modes of thinking: - A very parallel type of computation that is fast and generally accurate and integrates hundreds of variables. It’s sometimes labeled as intuition or system 1 thinking. - A much slower, step by step, analytical type, commonly linked with your pre-frontal cortex (one of the newest parts of the brain). Sometimes called system 2 thinking. Maybe the way the universe works is that all computation more or less is one of those two types. In which case, an LLM alone is only the first part, which is often right but its results also cannot ever be proven.
- stevemk14ebr 4mo ago
  An LLM is not thinking, assuming and relating it to thought and universal truths is nonsense.
  - dgellow 4mo ago
    We inflicted that to ourselves by picking the most confusing terminology ever. "No, reasoning isn't thinking. No when the model says it thinks it's not actually thinking... No an agent isn't actually a creature with agency... No, when we say it hallucinates it doesn't, like, actually hallucinate"
    - triyambakam 4mo ago
      What were the alternatives?
  - brookst 4mo ago
    Did you mean sentient?
  - coldtea 4mo ago
    How is it's process (inputs, training weights, processing those weights based on an initial given state -prompt-) different than how a brain thinks? And is it a difference of architecture and scale (like an ENIAC and a Apple Silicon M5) or a difference of fundamental operation?
aurareturn 4mo ago
Aren't hallucinations also heavily influenced by compute and memory capacity? IE. Companies can spend more time to verify results in an agentic format, spend more thinking tokens, and less quantization. All of these heavily depend on compute and memory but are proven to decrease hallucinations. Maybe GPT 5.5 is heavily nerfed due to lack of compute, memory, and energy? I agree that it's farfetched to conclude that bigger models have pleateued.
- dominotw 4mo ago
  article specifically talks about this. deepseek spending significant test time with worse results than klm
  - aurareturn 4mo ago
    So GLM is just a better model than DS then?
resters 4mo ago
to train models to be smarter than they are, one needs examples and cases to train on, and once you get close to the top percentiles of human reasoning there is extremely little such material available. You can create contrived logic problems, but they often turn into language games because English is not formal logic. And you can train on "monty hall" style problems, but those too are language games that are intriguing to humans but obvious when framed slightly differently. In other words, model trainers are fighting against the overwhelming mediocrity of the training corpus (all of the recorded human output from history). As models improve, the next phase will be models co-designed with humans to overcome these limits. The way we use language and the process we use to problem solve (we currently call this "orchestration") will evolve as part of this. Meatspace metaphors map badly when we have massive context and don't need the same limits. How different is hallucination from extrapolation, etc. Much of the skepticism and confusion about LLMs is no different than a person of average intelligence hearing a highly intelligent person explain something and considering the explanation gibberish, then arrogantly accusing the intelligent person of being unhelpful. Much like dogs were domesticated from wolves to have traits that make them good around humans, LLMs will evolve around our limits, around our arrogance, around our aesthetic biases and prejudices. Intelligence and rationality is fundamentally not what most humans want from an LLM.
dominotw 4mo ago
you mixed two random quotes from the article to create a strawman. ofcourse you knew what you were doing but disappointing that this was top comment.
claytongulick 4mo ago
My impression is that the fundamental issue is that LLMs attempt to extract reasoning (executive execution) from data (relationship between tokens). There's an open question about whether this is theoretically possible, but it doesn't seem like it to me. Human generated data is an effect of reasoning. Attempting to extract executive function from it is kind of like taking an anti-derivative of a function. This has always seemed like the root of hallucinations to me. It sort of follows the parallels to lossy compression that a lot of people draw. You're extracting some characteristics by observing the relationship between tokens, and then trying to argue that those characteristics are equivalent to the thing that generated the original tokens. Surely there's some sort of overlap there, but viewed that way, it seems obvious that more and more parameters and scaling won't solve the fundamental problem. There's only so much meaning you can extract from token relationships. It's like trying to derive the shape of a flame from the smoke it produces. The original intelligence that created those tokens was driven by a whole universe of inputs, from hormones to starlight to gravity, not to mention all of the strange things about consciousness and parapsychology that is so poorly understood. The machines are definitely useful for a certain class of tasks - those that don't require much executive function, and the useful work mostly involves pattern matching. The problem is, we seem to be mistaking effect for cause and imagining that these things have greater capabilities than they'll ever posess. The investors that don't understand this are indeed going to learn a bitter lesson.
- coldtea 4mo ago
  >The original intelligence that created those tokens was driven by a whole universe of inputs, from hormones to starlight to gravity Still inputs, that in the end changed something about synapses and their activation. And whether doesn't have a strong enough local effect to be material to the those operations, can be ignored too. E.g. gravity might kill you via a fall or a tide drowning you, but might have zero influence in your thinking at the brain operation level, aside from some influence that can be expressed in weights and such.
  - claytongulick 4mo ago
    > but might have zero influence in your thinking at the brain operation level Most of the people that I follow that research this stuff at the doctoral level say that they (and modern science) have very little idea about how consciousness works, the mind, and its relationship to the physical parts of the brain. We know about a lot of relationships, we can damage parts of the brain and see that damage expressed through changed behavior, but there appears to be far more to it than just a wet neural network of synapses and neurotransmitters that can be accurately modeled with a set of floating point numbers. Also, as an aside, gravity plays a much larger role in how the brain operates than may be immediately obvious [1]. [1] https://pmc.ncbi.nlm.nih.gov/articles/PMC9650717/ https://pmc.ncbi.nlm.nih.gov/articles/PMC9650717/
utopiah 4mo ago
>> it is clear that actual intelligence has plateaued significantly. > These are wild claims - Indeed, it is not clear there was any actual intelligence at any point. A lot of generated content sure, sometimes even useful, but not necessarily anything more.
- ozgung 4mo ago
  What is the definition of "actual intelligence"? How does it differ from regular intelligence and non-intelligence? If someone can "design a custom asyncio event loop policy in that overrides get_child_watcher()", I would call that person intelligent. Does that mean that person is not actually intelligent but a mere content creation machine? Traditionally if you can create content, this shows you're intelligent. Created content is often called "intellectual" property. If a person can understand complex ideas and make connection between them, that is considered intellectual work. You have to be intelligent to do intellectual work. If a person can solve problems, this is also called intelligence. If the person can solve more complex problems, that person is said to have higher intelligence. This is often measured with a scale called IQ (Intelligence Quotient). There are other types of intelligence but they are basically the variations of the same ability. Most definitions of intelligence also involve an ability to adapt into the environment. Since intelligence is such a broad concept what exactly is the difference between the actual intelligence and AI, other than one is natural and the other one is artificial? I understand being anti-AI because of the very real societal concerns. But ignoring what is in front of you is not a solution.
  - utopiah 4mo ago
    Listing things that couldn't be done by tools until now but since then can is entirely unrelated to intelligence. I don't know even understand why it's being mentioned. A ruler can measure infinitely better than I can, nobody ever thought of it as intelligent. You can keep on giving more (genuinely) impressive examples yet it's not helping at all.
bilater 4mo ago
Yeah not only is it totally unsubstantiated, the benchmarks are getting less useful to really show the difference between these models. Big model smell is still a thing and GLM 5.2 while impressive is not Fable class. Here is something I would like people to chew on. Perhaps the smartest researchers in the world across multiple labs know more about this than we do? Perhaps they are aware of issues like the data wall and diminishing marginal returns. And perhaps they are being honest when they tell you there is no wall?
- nathan_compton 4mo ago
  Are the smartest researchers in the world out there saying there isn't a wall? I don't know of any people doing the actual R&D who frequently make outrageous claims.
  - bilater 4mo ago
    https://x.com/polynoamial/status/2064210146558136827 https://x.com/polynoamial/status/2064210146558136827
    - djvdq 4mo ago
      I'd say that as OpenAI employee he's kinda biased on the topic
      - fragmede 4mo ago
        In what way is what he's saying wrong though? It makes him considerably more knowledgeable about the subject than the average Joe, and unfortunately for science, it's not like you're going to find an independent spherical researcher in the wild that exists without any bias.
  - itkovian_ 4mo ago
    The entirety of Anthropic believe ai is going to eat everything, not just software, and result in major societal disruption within a year. They do not have a sliver of a doubt on this. Article has no idea, is completely wrong.
goodness4all 4mo ago
I’m studying the root of probabilities and it’s impossible to have models without probable “hallucinations”. If we hit truth 20 times and 1 miss, we still would not consider it truth. This is the mathematical foundation these models are built & trained upon. Probability is our way of life, yet truth is subjective in life. Why use AI when you could just use a database if humans want determinism? The statistical mirror IS the power in AI.

by wolttam 4mo ago

madduci 4mo ago

Isn't that the case of over fitting? You have more data, but when you ask something that's not in that data, hallucinations happen

an0malous 4mo ago

> why are we concluding that bigger models and more data = more hallucination? That’s not what your quotes said. They said bigger models = plateau in intelligence, nothing about more data or increased hallucinations The relevant quote for what you’re talking about would be: > It’s been proven that when a model is trained on large volumes of highly factual and non-theoretical data, it learns to always have an answer. So there’s two separate claims: 1) bigger models have plateauing results 2) models trained on larger amounts of factual data have a higher hallucination rate I’m pretty sure #1 is well known, I think OpenAI’s own research on scaling laws showed diminishing returns on parameter count and training data volume years ago. I don’t know what the support for #2 is besides for the actual post contents.

jmalicki 4mo ago
I find these internet arguments talking about LLMs as if they are trained by reading the internet to be wild. Yes, pretraining still exists. But for the past few years, pretraining by reading the internet is just the initial bootstrapping of LLM training. The RL training they get from bespoke training data, with very very different characteristics than what these armchair analyses claim, dominates these days.
- mcphage 4mo ago
  Where do they get the bespoke training data from? And how much? I don’t really know anything about this.
  - dominotw 4mo ago
    meta has reallocated a significant protion of their staff to genrating this
    - sroussey 4mo ago
      Meta also reportedly took a 49% nonvoting stake in Scale AI in June 2025 for about $14.3–$14.8 billion.
    - coldtea 4mo ago
      The same company that let their flagship product go to waste, then bet hundreds of billions into the stupid vr metaverse that went nowhere? I'ts not like they really inspire much confidence in their future predictions.
  - blovescoffee 4mo ago
    Companies like Mercor sell data from human experts
    - trothamel 4mo ago
      Offhand, do you know what format that data is in? Is it a question and then a human answering that question? Mostly just curious at to what the training data consists of.
coffeefirst 4mo ago
Yeah #2 may be incidental. Suppose one lab focused on bigger, and another on reinforcement training geared towards factual accuracy over sycophancy. You could easily wind up with a model from the second lab that is less powerful but more accurate. I can’t prove it but I suspect there’s a bit of that going on.
- utkuumur 4mo ago
  I think one problem is that the models that hallucinate often, a few times out of 8 or 16 so that they get good results on benchmarks, most of which measures success out of top k. From benchmark perspective, you don't really care whether 15 of yours 16 generations failed, as long as one succeeded, but as a user you mostly care that 1 out of 16 you get is actually the successful one. I think this effects is more easy to see on Gemini Flash, it hallucinates like crazy but looks like its by design to boost benchmarks.
  - msdz 4mo ago
    > it hallucinates like crazy but looks like its by design to boost benchmarks. Wasn’t there a discussion around some new-ish benchmark _punishing_ hallucination answers (over not replying at all) recently? Maybe in the not-so-distant future, this “spam replies until one’s correct” strategy won’t be able to game a benchmark much at all anymore.
themgt 4mo ago
That’s not what your quotes said. They said bigger models = plateau in intelligence, nothing about more data or increased hallucinations ... I’m pretty sure #1 is well known Well known in a multiverse branch where Fable was a dud?
- an0malous 4mo ago
  No, well known in the current multiverse branch where we still occasionally use things like math and scientific analysis instead of people’s vibe checks and pelican SVGs. Here’s the paper from OpenAI where Dario himself was a co-author: https://arxiv.org/pdf/2001.08361 https://arxiv.org/pdf/2001.08361 > We have observed consistent scalings of language model log-likelihood loss with non-embedding parameter count N, dataset size D, and optimized training computation Cmin, as encapsulated in Equations (1.5) and (1.6). Conversely, we find very weak dependence on many architectural and optimization hyperparameters. Since scalings with N,D,Cmin are power-laws, there are diminishing returns with increasing scale.
  - themgt 4mo ago
    instead of people’s vibe checks and pelican SVGs. Right, what happened is everyone went to Fable and asked it to make the very best bicycle pelican SVG, no mistakes. And Fable's bicycle pelican SVGs were such timeless masterpieces, we all instantly got AI psychosis. Happily, you were immune to this.
    - coldtea 4mo ago
      > We have observed consistent scalings of language model log-likelihood loss with non-embedding parameter count N, dataset size D, and optimized training computation Cmin, as encapsulated in Equations (1.5) and (1.6). Conversely, we find very weak dependence on many architectural and optimization hyperparameters. Since scalings with N,D,Cmin are power-laws, there are diminishing returns with increasing scale.
deleted 4mo ago
[deleted]
ifwinterco 4mo ago
#2 is not that surprising from first principles if the way you made the bigger model was by feeding it poorer quality training data because it’s the only way you can get enough

coldtea 4mo ago

>These are wild claims - why are we concluding that bigger models and more data = more hallucination? Because that's what they measured in this case.

eurekin 4mo ago

> A shift is happening among major AI labs, who are becoming increasingly skeptical of endless parameter count and training data scaling I'm pretty sure it's mostly due to the training data quality. No idea, why this never gets mentioned in those discussions. It was obvious right from the get go, that the scaling law just enabled some abilities, that were described by the underlying data and allowing the ANN to abstract it in the latent space.

blurbleblurble 4mo ago

How do we know gpt 5.5 is a bigger model

Phelinofist 4mo ago
Since it was created by _Open_AI surely it's really open and we can check, right? SCNR

harrall 4mo ago

In cognitive science, it appears your brain has two modes of thinking: - A very parallel type of computation that is fast and generally accurate and integrates hundreds of variables. It’s sometimes labeled as intuition or system 1 thinking. - A much slower, step by step, analytical type, commonly linked with your pre-frontal cortex (one of the newest parts of the brain). Sometimes called system 2 thinking. Maybe the way the universe works is that all computation more or less is one of those two types. In which case, an LLM alone is only the first part, which is often right but its results also cannot ever be proven.

stevemk14ebr 4mo ago
An LLM is not thinking, assuming and relating it to thought and universal truths is nonsense.
- dgellow 4mo ago
  We inflicted that to ourselves by picking the most confusing terminology ever. "No, reasoning isn't thinking. No when the model says it thinks it's not actually thinking... No an agent isn't actually a creature with agency... No, when we say it hallucinates it doesn't, like, actually hallucinate"
  - triyambakam 4mo ago
    What were the alternatives?
- brookst 4mo ago
  Did you mean sentient?
- coldtea 4mo ago
  How is it's process (inputs, training weights, processing those weights based on an initial given state -prompt-) different than how a brain thinks? And is it a difference of architecture and scale (like an ENIAC and a Apple Silicon M5) or a difference of fundamental operation?

aurareturn 4mo ago

Aren't hallucinations also heavily influenced by compute and memory capacity? IE. Companies can spend more time to verify results in an agentic format, spend more thinking tokens, and less quantization. All of these heavily depend on compute and memory but are proven to decrease hallucinations. Maybe GPT 5.5 is heavily nerfed due to lack of compute, memory, and energy? I agree that it's farfetched to conclude that bigger models have pleateued.

dominotw 4mo ago
article specifically talks about this. deepseek spending significant test time with worse results than klm
- aurareturn 4mo ago
  So GLM is just a better model than DS then?

resters 4mo ago

to train models to be smarter than they are, one needs examples and cases to train on, and once you get close to the top percentiles of human reasoning there is extremely little such material available. You can create contrived logic problems, but they often turn into language games because English is not formal logic. And you can train on "monty hall" style problems, but those too are language games that are intriguing to humans but obvious when framed slightly differently. In other words, model trainers are fighting against the overwhelming mediocrity of the training corpus (all of the recorded human output from history). As models improve, the next phase will be models co-designed with humans to overcome these limits. The way we use language and the process we use to problem solve (we currently call this "orchestration") will evolve as part of this. Meatspace metaphors map badly when we have massive context and don't need the same limits. How different is hallucination from extrapolation, etc. Much of the skepticism and confusion about LLMs is no different than a person of average intelligence hearing a highly intelligent person explain something and considering the explanation gibberish, then arrogantly accusing the intelligent person of being unhelpful. Much like dogs were domesticated from wolves to have traits that make them good around humans, LLMs will evolve around our limits, around our arrogance, around our aesthetic biases and prejudices. Intelligence and rationality is fundamentally not what most humans want from an LLM.

dominotw 4mo ago

you mixed two random quotes from the article to create a strawman. ofcourse you knew what you were doing but disappointing that this was top comment.

claytongulick 4mo ago

My impression is that the fundamental issue is that LLMs attempt to extract reasoning (executive execution) from data (relationship between tokens). There's an open question about whether this is theoretically possible, but it doesn't seem like it to me. Human generated data is an effect of reasoning. Attempting to extract executive function from it is kind of like taking an anti-derivative of a function. This has always seemed like the root of hallucinations to me. It sort of follows the parallels to lossy compression that a lot of people draw. You're extracting some characteristics by observing the relationship between tokens, and then trying to argue that those characteristics are equivalent to the thing that generated the original tokens. Surely there's some sort of overlap there, but viewed that way, it seems obvious that more and more parameters and scaling won't solve the fundamental problem. There's only so much meaning you can extract from token relationships. It's like trying to derive the shape of a flame from the smoke it produces. The original intelligence that created those tokens was driven by a whole universe of inputs, from hormones to starlight to gravity, not to mention all of the strange things about consciousness and parapsychology that is so poorly understood. The machines are definitely useful for a certain class of tasks - those that don't require much executive function, and the useful work mostly involves pattern matching. The problem is, we seem to be mistaking effect for cause and imagining that these things have greater capabilities than they'll ever posess. The investors that don't understand this are indeed going to learn a bitter lesson.

coldtea 4mo ago
>The original intelligence that created those tokens was driven by a whole universe of inputs, from hormones to starlight to gravity Still inputs, that in the end changed something about synapses and their activation. And whether doesn't have a strong enough local effect to be material to the those operations, can be ignored too. E.g. gravity might kill you via a fall or a tide drowning you, but might have zero influence in your thinking at the brain operation level, aside from some influence that can be expressed in weights and such.
- claytongulick 4mo ago
  > but might have zero influence in your thinking at the brain operation level Most of the people that I follow that research this stuff at the doctoral level say that they (and modern science) have very little idea about how consciousness works, the mind, and its relationship to the physical parts of the brain. We know about a lot of relationships, we can damage parts of the brain and see that damage expressed through changed behavior, but there appears to be far more to it than just a wet neural network of synapses and neurotransmitters that can be accurately modeled with a set of floating point numbers. Also, as an aside, gravity plays a much larger role in how the brain operates than may be immediately obvious [1]. [1] https://pmc.ncbi.nlm.nih.gov/articles/PMC9650717/ https://pmc.ncbi.nlm.nih.gov/articles/PMC9650717/

utopiah 4mo ago

>> it is clear that actual intelligence has plateaued significantly. > These are wild claims - Indeed, it is not clear there was any actual intelligence at any point. A lot of generated content sure, sometimes even useful, but not necessarily anything more.

ozgung 4mo ago
What is the definition of "actual intelligence"? How does it differ from regular intelligence and non-intelligence? If someone can "design a custom asyncio event loop policy in that overrides get_child_watcher()", I would call that person intelligent. Does that mean that person is not actually intelligent but a mere content creation machine? Traditionally if you can create content, this shows you're intelligent. Created content is often called "intellectual" property. If a person can understand complex ideas and make connection between them, that is considered intellectual work. You have to be intelligent to do intellectual work. If a person can solve problems, this is also called intelligence. If the person can solve more complex problems, that person is said to have higher intelligence. This is often measured with a scale called IQ (Intelligence Quotient). There are other types of intelligence but they are basically the variations of the same ability. Most definitions of intelligence also involve an ability to adapt into the environment. Since intelligence is such a broad concept what exactly is the difference between the actual intelligence and AI, other than one is natural and the other one is artificial? I understand being anti-AI because of the very real societal concerns. But ignoring what is in front of you is not a solution.
- utopiah 4mo ago
  Listing things that couldn't be done by tools until now but since then can is entirely unrelated to intelligence. I don't know even understand why it's being mentioned. A ruler can measure infinitely better than I can, nobody ever thought of it as intelligent. You can keep on giving more (genuinely) impressive examples yet it's not helping at all.

bilater 4mo ago

Yeah not only is it totally unsubstantiated, the benchmarks are getting less useful to really show the difference between these models. Big model smell is still a thing and GLM 5.2 while impressive is not Fable class. Here is something I would like people to chew on. Perhaps the smartest researchers in the world across multiple labs know more about this than we do? Perhaps they are aware of issues like the data wall and diminishing marginal returns. And perhaps they are being honest when they tell you there is no wall?

nathan_compton 4mo ago
Are the smartest researchers in the world out there saying there isn't a wall? I don't know of any people doing the actual R&D who frequently make outrageous claims.
- bilater 4mo ago
  https://x.com/polynoamial/status/2064210146558136827 https://x.com/polynoamial/status/2064210146558136827
  - djvdq 4mo ago
    I'd say that as OpenAI employee he's kinda biased on the topic
    - fragmede 4mo ago
      In what way is what he's saying wrong though? It makes him considerably more knowledgeable about the subject than the average Joe, and unfortunately for science, it's not like you're going to find an independent spherical researcher in the wild that exists without any bias.
- itkovian_ 4mo ago
  The entirety of Anthropic believe ai is going to eat everything, not just software, and result in major societal disruption within a year. They do not have a sliver of a doubt on this. Article has no idea, is completely wrong.

goodness4all 4mo ago

I’m studying the root of probabilities and it’s impossible to have models without probable “hallucinations”. If we hit truth 20 times and 1 miss, we still would not consider it truth. This is the mathematical foundation these models are built & trained upon. Probability is our way of life, yet truth is subjective in life. Why use AI when you could just use a database if humans want determinism? The statistical mirror IS the power in AI.