In your example, the amino acids order is sufficient to directly model the result: the sequence of amino acids can directly generate the protein, which is either valid or invalid. All variables are provided within the data.

In the original example, we are testing weather using the previous day’s weather. We may be able to model using whatever correlation exists between the data. This is not the same as accurately predicting results, if the real-world weather function is determined by the weather of surrounding locations, time of year, and moon phase. If our model does not have this data, and it is essential to model the result, how can you accurately model?

In other words: “Garbage in, garbage out”. Good luck modeling an n-th degree polynomial function, given a fraction of the variables to train on.

>All variables are provided within the data.

electrostatic protein interaction, hydrophobic interaction, organic chemistry etc

all variables are in fact not provided within the data. Protein creation is not just _poof_ proteins. There are steps, interactions and processes. You don't need to supply any of that to get a model accurately predicting proteins. That is the main point here, not that you can predict anything with any data.

> This is not the same as accurately predicting results, if the real-world weather function is determined by the weather of surrounding locations, time of year, and moon phase.

How many have the "human intelligence" to do this? Especially more accurately than a computer (and without using any themselves) training on the same inputs and outputs?

I'm sorry in advance, but aren't proteins glorified Lego?

There's a lot more to protein sequences than legos. I think the argument is that you don't need to train a model on fundamental organic chemistry/biochemistry, electrostatic protein interaction, hydrogen bonding, hydrophobic interaction, quantum mechanics, etc... in order for it to accurately predict protein sequences.

The data that AlphaFold was trained on included all that information and more. The database they used for training included software simulations (and real world data) that accounted for atomic (quantum) interactions. The 3D structure of proteins includes all the quantum interactions.

More generally, AI models (aka very large function graphs) are trained on tuples that represent mappings of inputs to outputs (input -> output). The idea then is that whatever structure exists in those pairs/tuples/mappings is discovered by the training process with the help of gradient descent which tunes the parameters of the model/graph to optimally compress the information contained in the data. This means the model must uncover the quantum effects (or some close proxy of it) and then encode them into the parameters in a way that makes compression/prediction possible [1].

None of this is magic, compressing data requires uncovering structures and symmetries that can be used to reduce the size of the data and it turns out gradient descent with lots of parameters manages to do that for a large class of problems albeit at a very steep computational cost that requires billions of dollars for hardware and software (including nuclear power plants [2]). We are not going to get AGI with this approach but fortunately I know how to make it happen for a mere $80B.

1: https://arxiv.org/abs/2305.15614

2: https://www.cnbc.com/2023/09/25/microsoft-is-hiring-a-nuclea...

> You don't need to train on the inputs(casual processes) of anything, that's what training is there to figure out.

I mean... this is just obviously false. If the data you're training on isn't causally predictive, you may occasionally find good-enough patterns for a particular use case (i.e. you may occasionally guess better than a coin flip which direction the stock market goes) but you aren't going to accurately model anything, and certainly not well enough to create an AGI that makes intelligent decisions.

Words in sentences (and, indeed, proteins in a sequence) are causally predictive of each other - the grammar and semantics of one word tends to dictate what words are likely to surround it. So LLM's are very good at writing, and that is certainly useful! But that's just not the same as human intelligence.

When someone makes an AGI out of an LLM then I'll be proven wrong, I suppose. I'm just sharing my personal view on things.

Being "casually predictive" does not mean you have provided all the variables of your prediction in the data. Protein creation is not just _poof_ new proteins. There are steps and interactions and you don't need to train on all of that. Do you want a list of all the interactions of protein creation we are aware of ?

>When someone makes an AGI out of an LLM then I'll be proven wrong, I suppose. I'm just sharing my personal view on things.

You're going to have to define AGI first.

> Being "casually predictive" does not mean you have provided all the variables of your prediction in the data.

Not sure where I claimed this.

> Protein creation is not just _poof_ new proteins.

Not sure where I claimed this either.

> There are steps and interactions and you don't need to train on all of that.

I agree with this statement as well. Have you read what I wrote? Proteins in chains can indeed be used to predict other proteins in chains, even though you never trained the model on the biological processes of protein generation. Just like words in sentences can be used to predict other words in sentences, even though you never trained the model on the neurological processes of human speech. I'm not disputing any of that. What I'm disputing is that it will eventually become an AGI.

> You're going to have to define AGI first.

I'm using the definition provided verbatim in the linked article: "We believe superintelligence—AI vastly smarter than humans—could be developed within the next ten years."

Well first you say,

>I don't believe LLM's will ever become AGI, partly because I don't believe that training on the outputs of human intelligence (i.e. human-written text) will ever produce something equivalent to human intelligence.

Now you say

>I'm using the definition provided verbatim in the linked article: "We believe superintelligence—AI vastly smarter than humans—could be developed within the next ten years."

AGI (Artificial General Intelligence) is Super Intelligence. I've never seen posts moved so fast in my life. So are you not a General Intelligence then ?

This is the problem with these discussions. Everyone so sure of something they can't even properly articulate.

I'm asking you what a Language Model needs to do to be considered AGI and it needs to be something every human can do, else it's not a test of general intelligence.

Calm down, buddy. Read what I wrote a bit more charitably rather than trying to score points.

Obviously, a prerequisite to becoming more intelligent than a human, is to become equally intelligent as a human. I don't believe LLM's will ever be equal in intelligence to humans, ergo I also don't believe they will become superior in intelligence to human (which is how the linked article defines "AGI").

>Calm down, buddy

I'm not agitated.

>Obviously, a prerequisite to becoming more intelligent than a human, is to become equally intelligent as a human. I don't believe LLM's will ever be equal in intelligence to humans, ergo I also don't believe they will become superior in intelligence to human (which is how the linked article defines "AGI").

You have still not answered my question. saying "equivalent to human intelligence" is easy. The real question is equivalent how ? What are you saying it needs to do ? or is this just a vague "i'll know it when i see it" assertion ?

If it's so easy to say "GPT-4 is not agi" then it should be very easy to say what it can't do that disqualifies it

what is it that you're looking for it to do to be called one ?

> or is this just a vague "i'll know it when i see it" assertion

No, it's actually exceptionally easy to quantify. Take a look at the current leaderboard for LLM reasoning capabilities: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderb...

The state of the art model isn't even on that list.

Okay, you've at least given me numbers. You're still not answering my question. Which number signals agi ?

Let's look at the top model in that list (which again isn't close to the best performing LLM) that you say isn't agi. so are you telling me that every human can do those tests and perform better than every model on that list. Is that what you are saying ? because i can tell you right now you're wrong.

Now lets see how GPT-4 performs.

ARC - 96.3%, MMLU - 86.4%, HellaSwag - 95.3%, WinoGrande - 87.5%, GSM-8K - 92.0%, TruthfulQA - 60%

Ave - 86.25%

Is this worse than every human that can take these tests. Is this even worse than most ? I can tell you it's not. So again, why is GPT-4 not agi ?

> so are you telling me that every human can do those tests and perform better than every model on that list

This is a strange bar to require clearing. Every human is not even capable of reading, or speaking, or thinking at all. But those humans who can't are not the benchmark of general human intelligence.

Let's make this simple - the average human IQ is around 100. Will an LLM ever have an IQ of 100? No, I don't believe they'll ever even be close.

How can we gauge this abstract reasoning ability? Lots of ways. Try to teach them mathematics. Try to teach them physics. Try to explain to an LLM the rules to a card game and have it actually play the game with you accurately.

>Every human is not even capable of reading, or speaking, or thinking at all. But those humans who can't are not the benchmark of general human intelligence.

It's simple. If you're going to disqualify a potential general intelligence because of a particular task solving ability, it better be something every general intelligence can do.

Now let's forget every comparison with the blind, disabled and so on. Let's compare with humans who according to you are "capable of reading, or speaking, or thinking at all ", what's the magic number? Do you really think every human capable of doing all three will hit 86% or more average?, because again, you are wrong. Numerous replies and you still won't tell me what number will signify agi. That says a lot.

>Let's make this simple - the average human IQ is around 100. Will an LLM ever have an IQ of 100? No, I don't believe they'll ever even be close.

Well you are wrong

https://arxiv.org/abs/2212.09196

What's the next post ?

>Try to teach them mathematics.

Ok. https://arxiv.org/abs/2211.09066

https://arxiv.org/abs/2308.00304

>Try to explain to an LLM the rules to a card game and have it actually play the game with you accurately.

Have you tried to do this with GPT-4 ?

> Is this worse than every human that can take these tests. Is this even worse than most ? I can tell you it's not. So again, why is GPT-4 not agi ?

https://chat.openai.com/share/4a92c752-b5bb-4a07-beed-f57786...

Love it when people can only demonstrate tasks handicapped by Tokenization. Shows how far we've come.

That said, By that test, I guess some dyslexics aren't general intelligences then.

Tokenization is not the problem in this case.

https://chat.openai.com/share/90c7a9e2-77d0-4049-8eff-4312ca...

You see the difference?

Then how does ChatGPT end up providing a better/equivalent medical diagnosis than doctors (even though they are the "masters" of the causal pathways)?

Weather and stock market are both chaotic systems.

Increasing evidence suggests that AGI will not be attainable solely using LLMs/transformers/current architecture, as LLMs can't extrapolate beyond the patterns in their training data (according to a paper from DeepMind last month):

"Together our results highlight that the impressive ICL abilities of high-capacity sequence models may be more closely tied to the coverage of their pretraining data mixtures than inductive biases that create fundamental generalization capabilities."[1]

1. https://arxiv.org/abs/2311.00871