Mike_12345
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?
You can find studies showing that even pure THC is neurotoxic. It depends on dosage and frequency.
Are you autistic? You seem incredibly dense. I was talking about my opinion and direct personal experience. I explained this multiple times.
You can't demand a study/citation for someones personal experience. That is absurd.
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.
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.
Try reading the article before commenting.
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.
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.
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.
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 seen in their training set.
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?
> 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.
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.
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.
> 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."
He received a Turing Award for his work that was foundational to the current state of the art.
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.
> 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.
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!"
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.
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?
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.