4 ms·

I would like to read a book on how the heck machine-learning "comprehends" and follows Balance empathy with candor "empathy" would have to be emulated li

by ck2 5mo ago

I would like to read a book on how the heck machine-learning "comprehends" and follows Balance empathy with candor "empathy" would have to be emulated like a sociopath, to a lesser extent "candor" but then also "balance" requires a grasp of the weight of each, even if mathematically? BTW what on earth happens internally when you ask another "AI" to evaluate the prompt of another "AI"

bauldursdev 5mo ago
This is much much more complex than a traditional program, which can be followed line by line. Trying to understand every bit of the literal logic is like trying to understand a person by thinking about the neurons fired in their brain to make them say or do something. Unfortunately you have to learn to let go, and say, "I'll never be able to keep this all in my head", and learn to think about it in terms of of the outputs/inputs and how you can create a model capable of efficiently modeling your problem and how parameters can be nudged to get an output which is kinda shaped how you want. Maybe some really genius savant could keep it all in their head but I doubt it, like I said it'd be like trying to understand a person by reasoning about their neural pathways.
otabdeveloper4 5mo ago
> I would like to read a book on how the heck machine-learning "comprehends" and follows It matches with Reddit posts that have statistically similar words and starts generating the next statistically likely token.
- ck2 5mo ago
  I think "AI" code has evolved to be more sophisticated than that beginning level but that still requires it to recognize the concept of "empathy" and "candor" in words of others even if it is just pattern matching on a massively parallel scale, it still seems beyond simple logic if you told "AI" to comb a reddit sub and find only posts that are empathetic, how on earth is that evaluated?
  - esseph 5mo ago
    > If you told "AI" to comb a reddit sub and find only posts that are empathetic, how on earth is that evaluated? It was done before LLMs existed via sentiment analysis. Here's some info on how it is done with LLMs. https://blog.jetbrains.com/pycharm/2024/12/how-to-do-sentiment-analysis-with-large-language-models/ https://blog.jetbrains.com/pycharm/2024/12/how-to-do-sentime...
dcrazy 5mo ago
Each turn of the conversation, the entire history of the conversation is first tokenized. Each token is like a short ID for a very (multi-thousand-element) vector called an embedding. An LLM is built around putting all these vectors together into a matrix, and then multiplying that matrix by another bunch of matrices, anll of which results in another embedding vector that represents the next token in the conversation. This vector is then added to the end of the input matrix (aka the “context window”), and the process starts again. This continues until the token coming out of the LLM is a special “end of message” token. A human designed the network of matrix multiplications that make up the model, but the matrices all started out filled with zeroes. The training process is what puts nonzero values in these matrices. The values are such that if you look at how each column of the input matrix (which, recall, is an encoding of each token of the conversation history) gets multiplied as it works its way through the network, you can think of the coefficients as the model’s understanding of a “concept”. Notably, because modern LLMs are built around the concept of a “transformer” which looks at every word of the context simultaneously, the “concept” can involve coefficients that act on multiple (usually nearby) tokens. So the model may have internalized the concept of “empathetic” as “a cluster of nearby tokens whose values, when multiplied by M1[2492, 59272] * M2[592827, 7394] * M3[93732, 429474] * ..., are all similar to each other.” As might be apparent, this means that the values for each token embedding are also highly sensitive to the coefficients buried deep within the LLM’s matrices. These values are the result of the training process too. The whole thing gets trained at once on an enormous body of text, some of which explicitly explains concepts like “empathy,” which will enable the model to associate empathetic clusters of words with other clusters of words that define empathy. But if you removed all the definitions and explanations of empathy from the training set, the model would still probably form a (weaker) concept of “empathy” that it would have a harder time explaining. It might not even know the word “empathetic,” but it would have some internal structure that causes token clusters like “I’m sorry for your loss” and “I know what you’re going through” to have similar values across some of their elements. Using LLMs for medical research is all about encoding drug data rather than words and trying to extract the “concepts” that the training process has formed. The hope is that the model has internalized a novel insight from seeing way more data than a human could consume, much less reason about.

by ck2 5mo ago

bauldursdev 5mo ago

This is much much more complex than a traditional program, which can be followed line by line. Trying to understand every bit of the literal logic is like trying to understand a person by thinking about the neurons fired in their brain to make them say or do something. Unfortunately you have to learn to let go, and say, "I'll never be able to keep this all in my head", and learn to think about it in terms of of the outputs/inputs and how you can create a model capable of efficiently modeling your problem and how parameters can be nudged to get an output which is kinda shaped how you want. Maybe some really genius savant could keep it all in their head but I doubt it, like I said it'd be like trying to understand a person by reasoning about their neural pathways.

otabdeveloper4 5mo ago

> I would like to read a book on how the heck machine-learning "comprehends" and follows It matches with Reddit posts that have statistically similar words and starts generating the next statistically likely token.

ck2 5mo ago
I think "AI" code has evolved to be more sophisticated than that beginning level but that still requires it to recognize the concept of "empathy" and "candor" in words of others even if it is just pattern matching on a massively parallel scale, it still seems beyond simple logic if you told "AI" to comb a reddit sub and find only posts that are empathetic, how on earth is that evaluated?
- esseph 5mo ago
  > If you told "AI" to comb a reddit sub and find only posts that are empathetic, how on earth is that evaluated? It was done before LLMs existed via sentiment analysis. Here's some info on how it is done with LLMs. https://blog.jetbrains.com/pycharm/2024/12/how-to-do-sentiment-analysis-with-large-language-models/ https://blog.jetbrains.com/pycharm/2024/12/how-to-do-sentime...

dcrazy 5mo ago

Each turn of the conversation, the entire history of the conversation is first tokenized. Each token is like a short ID for a very (multi-thousand-element) vector called an embedding. An LLM is built around putting all these vectors together into a matrix, and then multiplying that matrix by another bunch of matrices, anll of which results in another embedding vector that represents the next token in the conversation. This vector is then added to the end of the input matrix (aka the “context window”), and the process starts again. This continues until the token coming out of the LLM is a special “end of message” token. A human designed the network of matrix multiplications that make up the model, but the matrices all started out filled with zeroes. The training process is what puts nonzero values in these matrices. The values are such that if you look at how each column of the input matrix (which, recall, is an encoding of each token of the conversation history) gets multiplied as it works its way through the network, you can think of the coefficients as the model’s understanding of a “concept”. Notably, because modern LLMs are built around the concept of a “transformer” which looks at every word of the context simultaneously, the “concept” can involve coefficients that act on multiple (usually nearby) tokens. So the model may have internalized the concept of “empathetic” as “a cluster of nearby tokens whose values, when multiplied by M1[2492, 59272] * M2[592827, 7394] * M3[93732, 429474] * ..., are all similar to each other.” As might be apparent, this means that the values for each token embedding are also highly sensitive to the coefficients buried deep within the LLM’s matrices. These values are the result of the training process too. The whole thing gets trained at once on an enormous body of text, some of which explicitly explains concepts like “empathy,” which will enable the model to associate empathetic clusters of words with other clusters of words that define empathy. But if you removed all the definitions and explanations of empathy from the training set, the model would still probably form a (weaker) concept of “empathy” that it would have a harder time explaining. It might not even know the word “empathetic,” but it would have some internal structure that causes token clusters like “I’m sorry for your loss” and “I know what you’re going through” to have similar values across some of their elements. Using LLMs for medical research is all about encoding drug data rather than words and trying to extract the “concepts” that the training process has formed. The hope is that the model has internalized a novel insight from seeing way more data than a human could consume, much less reason about.