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It seems insane to me to have fundamental education that builds logic & reasoning taught by an AI that can exhibit neither of those (and this isn’t just my hot

It seems insane to me to have fundamental education that builds logic & reasoning taught by an AI that can exhibit neither of those (and this isn’t just my hot take but something easily provable if you look at the underlying architecture). Recent LLM advances are revolutionary and exciting, sure. But the hype cycle surrounding it is borderline dangerous. I see this as the same level as everyone who was claiming ChatGPT would kill Google until Bing tried it, crashed, and burned. Accuracy is important and although we’re moving towards this (I personally like Amazon’s knowledge graph and evidence based approach) we aren’t there yet.

fullshark 4y ago
Bing crashed and burned?
cypress66 4y ago
> and this isn’t just my hot take but something easily provable if you look at the underlying architecture Show your proof then
- famouswaffles 4y ago
  He obviously neither understands the architecture (and what that really means in relation to an objective function ) or what it means to understand and reason. LLMs don't stop reasoning because people proclaim so loudly. Because you can very clearly show output demonstrating understanding and reasoning, this nonsensical argument always devolves into "true understanding" TM. This so called special distinction that is so important but yet can't actually be tested for. Lol
  - qlm 4y ago
    LLMs don't "understand" things because they can't generate new theories. Imagine you trained an LLM a ton of text describing what it means for something to be true, and for something to be false. It would never independently come up with, say, propositional calculus. It could tell you everything in the world about what it means to be true and false. Would never come up with anything new. Does this matter for education? Well, I don't know. A student is unlikely to come up with a question that GPT-4 has never seen before. The idea of kids being taught by a chat bot is just depressing, though. What are we doing to ourselves? Why do we want this? To be more "efficient"?
    - famouswaffles 4y ago
      Language models can generate novel functioning protein structures that adhere to a specified purpose. Structures that didn't exist before nevermind in the training set. https://www.nature.com/articles/s41587-022-01618-2 https://www.nature.com/articles/s41587-022-01618-2 Sorry but the idea that they can't generate new things is blatantly false.
      - qlm 4y ago
        This isn't generating new theories. I'm not saying LLMs aren't useful, or that they aren't extremely impressive in many ways. Almost nothing GPT-4 produces was in the training set. I'm not saying they can't produce new sentences. From the linked paper: "a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences" Nobody is disputing that LLMs can generate grammatically and semantically correct sentences, and from the sounds of things this task is analogous to generating sentences. This isn't the same as generating new theories.
        famouswaffles 4y ago
        If it can take natural language purpose descriptions and generate new functioning protein then due to it's training, it evidently understood the relationship between purpose and protein structure. Meaning all the little models it builds as a result of training are concrete. They can produce new things and new information and it's just matter of scale and intelligence. "akin to generating grammatically and semantically correct natural language sentences" is just an indicator of how it is synthesizing and producing this information. To say it is analogous to generating sentences in the abstract is like saying generating new theorems is analogous to generating sentences. Well yeah, that's what the language model is doing. The power lies in what it is generating. What they are telling you is that they skipped the folding process entirely and just let the model figure out function to structure directly. Either your dog fetches what you throw at it or not. The idea of "pretend fetching" is unbelievably silly. Doubly so when no one seems to be able to actually define this act. A distinction that can't be tested for is not a distinction. Science is concerned with results not vague and ill defined assertions.
  - YeGoblynQueenne 4y ago
    You're interested in reasoning? That's so cool. Here ya go, something I've been reading earlier today because I need to add some references about deductive, inductive and abductive reasoning, particularly in the context of logic programming (you know, reasoning with computers) to my thesis: https://www.researchgate.net/publication/321596989_Abduction_and_Induction_Essays_on_their_Relation_and_Integration https://www.researchgate.net/publication/321596989_Abduction... Don't let the Researchgate link discourage you. It will pretend to ask you to login but it will open the pdf anyway. Why am I posting this? Because all you guys keep talking about "reasoning" this and "reasoning" that, and I don't think that word means, what you think it means. Reasoning is a very specific thing and we know very well how it works. Can ChatGPT reason? I'm afraid you'll just have to read up and figure it out yourself.
gowld 4y ago
> It seems insane to me to have fundamental education that builds logic & reasoning taught by an AI that can exhibit neither of those Books don't exhibit those either.
- mecsred 4y ago
  That's right. The authors of those books exhibit those qualities. You can't educate a human by pouring generated text on them, you need something that's capable of teaching to moderate the content. LLMs may allow teachers to achieve more with custom prompts, but it's a far cry from "personal Aristotle" and still requires 1-1 engagement with teachers and student.
- gameman144 4y ago
  Totally agreed, which is why we carefully curate the books we use within educational curricula to make sure they actually contain useful and accurate material.
deleted 4y ago
[deleted]
anonyfox 4y ago
actually bing is growing massively while google is shifting into irrelevance everywhere (except android) - even laypeople now prefer to NOT use google left and right

fullshark 4y ago

Bing crashed and burned?

cypress66 4y ago

> and this isn’t just my hot take but something easily provable if you look at the underlying architecture Show your proof then

famouswaffles 4y ago
He obviously neither understands the architecture (and what that really means in relation to an objective function ) or what it means to understand and reason. LLMs don't stop reasoning because people proclaim so loudly. Because you can very clearly show output demonstrating understanding and reasoning, this nonsensical argument always devolves into "true understanding" TM. This so called special distinction that is so important but yet can't actually be tested for. Lol
- qlm 4y ago
  LLMs don't "understand" things because they can't generate new theories. Imagine you trained an LLM a ton of text describing what it means for something to be true, and for something to be false. It would never independently come up with, say, propositional calculus. It could tell you everything in the world about what it means to be true and false. Would never come up with anything new. Does this matter for education? Well, I don't know. A student is unlikely to come up with a question that GPT-4 has never seen before. The idea of kids being taught by a chat bot is just depressing, though. What are we doing to ourselves? Why do we want this? To be more "efficient"?
  - famouswaffles 4y ago
    Language models can generate novel functioning protein structures that adhere to a specified purpose. Structures that didn't exist before nevermind in the training set. https://www.nature.com/articles/s41587-022-01618-2 https://www.nature.com/articles/s41587-022-01618-2 Sorry but the idea that they can't generate new things is blatantly false.
    - qlm 4y ago
      This isn't generating new theories. I'm not saying LLMs aren't useful, or that they aren't extremely impressive in many ways. Almost nothing GPT-4 produces was in the training set. I'm not saying they can't produce new sentences. From the linked paper: "a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences" Nobody is disputing that LLMs can generate grammatically and semantically correct sentences, and from the sounds of things this task is analogous to generating sentences. This isn't the same as generating new theories.
      - famouswaffles 4y ago
        If it can take natural language purpose descriptions and generate new functioning protein then due to it's training, it evidently understood the relationship between purpose and protein structure. Meaning all the little models it builds as a result of training are concrete. They can produce new things and new information and it's just matter of scale and intelligence. "akin to generating grammatically and semantically correct natural language sentences" is just an indicator of how it is synthesizing and producing this information. To say it is analogous to generating sentences in the abstract is like saying generating new theorems is analogous to generating sentences. Well yeah, that's what the language model is doing. The power lies in what it is generating. What they are telling you is that they skipped the folding process entirely and just let the model figure out function to structure directly. Either your dog fetches what you throw at it or not. The idea of "pretend fetching" is unbelievably silly. Doubly so when no one seems to be able to actually define this act. A distinction that can't be tested for is not a distinction. Science is concerned with results not vague and ill defined assertions.
- YeGoblynQueenne 4y ago
  You're interested in reasoning? That's so cool. Here ya go, something I've been reading earlier today because I need to add some references about deductive, inductive and abductive reasoning, particularly in the context of logic programming (you know, reasoning with computers) to my thesis: https://www.researchgate.net/publication/321596989_Abduction_and_Induction_Essays_on_their_Relation_and_Integration https://www.researchgate.net/publication/321596989_Abduction... Don't let the Researchgate link discourage you. It will pretend to ask you to login but it will open the pdf anyway. Why am I posting this? Because all you guys keep talking about "reasoning" this and "reasoning" that, and I don't think that word means, what you think it means. Reasoning is a very specific thing and we know very well how it works. Can ChatGPT reason? I'm afraid you'll just have to read up and figure it out yourself.

gowld 4y ago

> It seems insane to me to have fundamental education that builds logic & reasoning taught by an AI that can exhibit neither of those Books don't exhibit those either.

mecsred 4y ago
That's right. The authors of those books exhibit those qualities. You can't educate a human by pouring generated text on them, you need something that's capable of teaching to moderate the content. LLMs may allow teachers to achieve more with custom prompts, but it's a far cry from "personal Aristotle" and still requires 1-1 engagement with teachers and student.
gameman144 4y ago
Totally agreed, which is why we carefully curate the books we use within educational curricula to make sure they actually contain useful and accurate material.

deleted 4y ago

[deleted]

anonyfox 4y ago

actually bing is growing massively while google is shifting into irrelevance everywhere (except android) - even laypeople now prefer to NOT use google left and right