Mike_12345

LOL you actually sound like ChatGPT. Are you a LLM?

Your racist comment is too idiotic to be authentic. Are you a spy working for the CCP?

The US big tech companies are already filled with Indian and Chinese workers. Are you trying to influence tech companies to hire more CCP agents so the technology can be siphoned off to your CCP overlords?

Go to Google Scholar and search for "neurotoxicity of cannabis"

You can find studies showing that even pure THC is neurotoxic. It depends on dosage and frequency.

Very interesting! I'd love to learn more. Please show us the data supporting this assertion.

You can't demand a study/citation for someones personal experience. That is absurd. You are being an absolutely illogical clown.

I was talking about my opinion and direct personal experience. I explained this multiple times but you are incapable of understanding this basic concept.

"Objective" means observable and measurable. Are you genuinely so clueless that you believe no one can perceive anything objective about their own performance and abilities and actions in the external world?

Yes, price = quality because they require supercomputing resources to train. GPT-3 required hundreds of Tesla GPUs running for several weeks. That's millions of dollars just for hardware, not including power (the GPUs cost $15k each)

Ask ChatGPT

Where is the objectivity?

Objectively worse performance in life and in mentally taxing work. It really isn't that complicated.

Go smoke a massive bong hit then do a calculus exam and compare your score.

"Basic common sense" is not a synonym for "science".

I explained this was my opinion based on experience multiple times so you're just arguing with a strawman. You are absolutely wasting your time here and completely missed the point.

Try reading the article if you want to see a study showing scientific evidence of the neurotoxicity of weed.

No, it isn't. It's medicine, and remarkably harmless recreation.

Try reading the article before commenting.

How do you measure neurotoxicity on yourself?

If you consume it, and it subjectively and objectively harms your mental health and cognitive abilities, then basic common sense and reasoning tells you it is harmful to the brain (neurotoxic).

This is my opinion based on my experience. If you smoked this shit and it gave you an extra 20 IQ point boost, then congratulations.

All smoke is carcinogenic, because it is smoke.

That's the point. Neurons don't get healthier from carcinogens.

That's not a settled debate; not remotely.

In my opinion it is neurotoxic, based on studies I have seen (such as this one that we are commenting on) plus my own direct personal experience. Even without the psychoactive components, the smoke is carcinogenic (like tobacco) and toxic to all cells, including neurons. Go take a massive bong hit, then tell me your mind and lungs feel healthy. Cannabis is blatantly toxic filth.

because you can't make the assumption that people would "never have become psychotic without the drug"

Somehow you are ignoring the entire point of the article/study that we are commenting on.

Isn't that the entire point of the article and study that we are commenting on?

I firmly believe that psychedelics are triggers and not the cause of mental disorders

If a neurotoxic drug such as cannabis causes psychosis in someone who would otherwise never have become psychotic without the drug, then whether you call it a cause or a trigger is quibbling over semantics.

if memorized every single result of of a multiplication between two numbers up to the maximum a human can possibly think of, then do I understand multiplication?

No, ChatGPT is not simply regurgitating. It actually understands the data. It is one of the largest deep neural networks ever constructed.

Consider AlphaZero, the neural net that learned how to play Go and became the world champion. It started with nothing but the rules of the game. Deep Learning systems build a generalized model and reason about it. They are not simply regurgitating what they have already seen in their training set.

Here's a prompt: "If there is a bob outside, and a bib inside, and the bib cannot see the bob, can the bob see the bib?"

Answer:

"If the bib cannot see the bob due to being inside, it is likely that the bob, which is outside, also cannot see the bib. The reason for the bib not being able to see the bob is presumably due to some kind of barrier, such as a wall or a window, which would likely obstruct the view for both the bib and the bob."

Prompt:

"What type of reasoning did you just apply to solve that task?"

Answer:

"In solving the task, I applied abductive reasoning. Abductive reasoning involves inferring the most likely explanation for a set of observations or statements. In this case, I inferred that a barrier (e.g., a wall or window) is the most likely explanation for why the bib cannot see the bob, and thus, the bob likely cannot see the bib either."

ChatGPT solves the problem by a mechanical reasoning process, not a "party trick". It's a machine, not a person at a party. Surely you don't believe that it has seen that exact sequence of questions and replies in its training set and is simply regurgitating. You can argue about whether its actually using "abductive reasoning" or not, but that is besides the point that it involves some mechanical reasoning process over an interpretation of the prompt. It's not a simple regurgitation.

AlphaZero learned to play Go starting with nothing but the rules of the game. What is it regurgitating there?

Alright so deep learning, the state of the art of AI, is a "party trick". AlphaZero is likewise a party trick. No "true" reasoning involved.

Like actual reasoning.

You're relying on intuition and personal beliefs of what constitutes "true" reasoning instead of formal rigorous mathematical definitions of reasoning. The general concept of reasoning includes what the language models are doing when they solve natural language understanding tasks, by definition.

It just sounds like a No True Scotsman fallacy.

Part of my whole point is that LLMs are to NLPs as rockets are to airplanes.

Yes it is deep learning applied to NLP. Makes the old designs obsolete

the data is text

It is not randomly generated text. There are patterns in that text. It was trained to model the semantics or "meaning" in the text. There is a structure in the text which the machine has recognized.

It automatically learned a model of many concepts without any of those concepts being explicitly programmed into it. That's the entire point of machine learning.

But not adequately, IMO.

It is adequate for some things and not adequate for other things.

It seems that all you are saying is that GPT is not AGI and doesn't have human level of understanding and reasoning. No one disagrees with that.

Here is an example of an emergent ability of ChatGPT that you can try yourself right now.

Give it this prompt: "Write a short play where "Karen" (who behaves as the Karen meme) is on a romantic date with Hunter S. Thompson. They are eating at a Chinese restaurant."

It is able to script their interaction and dialog in a way that makes sense in the context of the setting and their personalities, including an absurd meme character. That is an emergent ability that it was not trained to do and is certainly not in its training set.

Try it. Add different characters. Ask it to rewrite the play using pirate metaphors. You can go deep into its "mind" and see the emergent abilities at play. Just apply some creativity and skip the boring arithmetic problems, as that's a well accepted weakness of this type of model.

It was a straightforward response to "I simply mean his track record of predicting the future trajectory of AI is not great."

What do you mean we don't have evidence? It has already been presented to you. You choose to reject it for reasons I truly don't understand. Search on Google Scholar if you want a more academic explanation. You can try it out on ChatGPT right now and see it yourself.

The level of naievete around this stuff is quite incredible.

"Emergent properties" has a formal definition in the literature.

In machine learning we don't explicitly program the machine to understand anything. It automatically learns patterns in the data. That's the entire point of machine learning. With such a large neural network and training set obviously it's hard to predict all of its capabilities due to the sheer scale of it all. Of course we cannot predict exactly how it will model things.

Take this for example. No one programmed it to understand Go. It learned by itself and became the world champion. That's what deep learning is capable of.

https://en.wikipedia.org/wiki/AlphaGo_Zero

"The neural network initially knew nothing about Go beyond the rules."

Well he bet on neural networks in the early days when it was unpopular, and that turned out to be the right trajectory.

He received a Turing Award for his work that was foundational to the current state of the art.

Everyone knows it has limitations. You have to work within the limitations of the model. No one has claimed that GPT is AGI. Doesn't mean it's incapable of any degree of reasoning. Yes the prompt actually matters. It was trained a specific way to solve specific tasks, and can generalize to solve tasks it has not seen before.

Try this prompt: "Taking into account the r-value of adobe, I live in a location with heavy clay soil, and plan to build an adobe home. Will it be well-insulated?"

These edge case "gotchas" are missing the point.

Also

reasoning in a traditional sense.

We are talking about reasoning in a general sense. There are many types of reasoning in AI which I'm sure you know how to look up and read about. "Traditional" is not one of the categories.

(follow up to adjacent comment)

So what I'm saying is, GPT "knows" what a cat is. It "knows" what an orange is. It has inferred these concepts from the data set.

Imagine approaching someone who is tripping on LSD and demanding they immediately solve a 10 digit multiplication problem, then saying "AHA! You cannot solve it, therefore you are incapable of any reasoning whatsoever!"

But LLMs do not solve natural language understanding in any of the meanings that the phrase meant before LLMs.

Are you saying that NLP as a field of research did not exist before LLMs? This is a continuation of research that has been in progress for decades.

But there is no world representation inside an LLM, only text (words, letters) representations, so nothing the LLM does can be based on reasoning in a traditional sense.

Not true. The model has learned a representation of semantic relationships between words and concepts at multiple levels of abstraction. That is the entire point. That's what is was trained to do.

It's a vast and deep neural network with a very high dimensional representation of the data. Those semantic/meaning relations are automatically learned and encoded in the model.

His opinion obviously does matter because he is a founder of the field. No one believes that he is prescient. You are exaggerating and creating a strawman argument, infantilizing the readers here. We don't worship him or outsource our thinking.

Solving natural language understanding tasks requires reasoning, by definition. I think you are sticking to a narrow definition of reasoning that is not very technical. Formally there are many types of reasoning in AI.

I am all about emergent properties of neural networks, but I absolutely do not believe that LLMs have them, specifically because of the way they are designed.

Even when faced with evidence that contradicts your beliefs and proves that you are wrong?

LLMs are a type of neural network. What fundamentally prevents LLMs from having emergent abilities while other neural networks do have them?

How do you explain the emergent abilities that we have actually observed in LLMs?

This is going off topic, but originally I was replying to this comment: "The modern era has dramatically reduced civilian casualties compared previous generations... Precision munitions contribute heavily to this reduction.". I was trying to explain that the cause of excessive civilian casualties in WW2 was not due to technological limitations. It was fully intended. It was the goal.

You are so narrowly fixated on this one specific domain which is an edge case with LLMs.

ChatGPT was trained specifically to solve "natural language understanding tasks".

You are missing the forest for the trees and ignoring everything else it's good at solving outside of its training set.

The emergent properties of neural networks should not be so casually dismissed.

You're arguing that since its not perfect at arithmetic reasoning then its not capable of any degree of reasoning in any domain. That is an oversimplification and just doesn't make logical sense.