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> There's no thinking, no reasoning, no calculation, no logic, no deduction, no intelligence, no anything. It's only token, token, token. False. The neural net

by xcv123 3y ago

> There's no thinking, no reasoning, no calculation, no logic, no deduction, no intelligence, no anything. It's only token, token, token. False. The neural network inside the transformer LLM contains a hierarchical semantic model, and has inferred some rules of reasoning from the training set. It can apply those rules to new input. There are semantic layers above the "token token tokens". Explore them here: https://openaipublic.blob.core.windows.net/neuron-explainer/neuron-viewer/index.html https://openaipublic.blob.core.windows.net/neuron-explainer/...

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YeGoblynQueenne 3y ago
What you're commenting here is simply repearting, without critical intent, the baseless claims connectionists have made about their systems for many decades. Similarlly those claims have been criticised but connectionsists simply ignore the criticisms and continue with the same old nonsense, as if nothing happened. For example, that ridiculous conceit that their systems have "neurons", or that the weights of functions in a neural net somehow represent semantic categories recognised by humans. These are all complete fantasies. If you are not aware of the long history of debunking such fabrications, I suggest you start here: Connectionism and Cognitive Architecture: A Critical Analysis https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/proseminars/Proseminar13/ConnectionistArchitecture.pdf https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/pro...
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- xcv123 3y ago
  We are talking about artificial neurons here. Not biological neurons. These are mathematical structures. https://en.wikipedia.org/wiki/Artificial_neuron https://en.wikipedia.org/wiki/Artificial_neuron These models infer semantic categories that correlate to categories within the human mind, to the extent that they can solve natural language understanding tasks. No one is saying they are biological neurons, or that they model semantics exactly as the human mind would. It is mechanical pattern recognition that approximates our understanding. You can browse those artificial neurons online and view their associations.
  - YeGoblynQueenne 3y ago
    You're just saying words without ever explaining why. What am I supposed to do about that? There's nothing to argue with if you're just repeating nonsensical claims without even trying to support them. For example: >> It is mechanical pattern recognition that approximates our understanding. That's just a claim and you're not even saying why you make it, what makes you think so, etc.
    - xcv123 3y ago
      > That's just a claim and you're not even saying why you make it, what makes you think so, etc. Mechanical - it is an algorithm, not a living being. Pattern recognition - a branch of machine learning that focuses on the detection and identification of regularities and patterns in data. It involves classifying or categorizing input data into identifiable classes based on extracted features. The patterns recognized could be in various forms, such as visual patterns, speech patterns, or patterns in text data. Approximates our understanding - meaning the model is not exactly the same as human understanding When I say 'mechanical pattern recognition that approximates our understanding,' what I mean is that large language models (LLMs) like GPT-4 learn patterns from the vast amounts of text data they're trained on. These patterns correspond to various aspects of language and meaning. For example, the models learn that the word 'cat' often appears in contexts related to animals, pets, and felines, and they learn that it's often associated with words like 'meow' or 'fur'. In this sense, the model 'understands' the concept of a cat to the extent that it can accurately predict and generate text about cats based on the patterns it has learned. This isn't the same as human understanding, of course. Humans understand cats as living creatures with certain behaviors and physical characteristics, and we have personal experiences and emotions associated with cats. A language model doesn't have any of this - its 'understanding' is purely statistical and based on text patterns. The evidence for these claims comes from the performance of these models on various tasks. They can generate coherent, contextually appropriate text, and they can answer questions, translate languages, and perform other language-related tasks with a high degree of accuracy. All of this suggests that they have learned meaningful patterns from their training data.
      - 3y ago

by xcv123 3y ago

deleted 3y ago

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YeGoblynQueenne 3y ago

What you're commenting here is simply repearting, without critical intent, the baseless claims connectionists have made about their systems for many decades. Similarlly those claims have been criticised but connectionsists simply ignore the criticisms and continue with the same old nonsense, as if nothing happened. For example, that ridiculous conceit that their systems have "neurons", or that the weights of functions in a neural net somehow represent semantic categories recognised by humans. These are all complete fantasies. If you are not aware of the long history of debunking such fabrications, I suggest you start here: Connectionism and Cognitive Architecture: A Critical Analysis https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/proseminars/Proseminar13/ConnectionistArchitecture.pdf https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/pro...

deleted 3y ago
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xcv123 3y ago
We are talking about artificial neurons here. Not biological neurons. These are mathematical structures. https://en.wikipedia.org/wiki/Artificial_neuron https://en.wikipedia.org/wiki/Artificial_neuron These models infer semantic categories that correlate to categories within the human mind, to the extent that they can solve natural language understanding tasks. No one is saying they are biological neurons, or that they model semantics exactly as the human mind would. It is mechanical pattern recognition that approximates our understanding. You can browse those artificial neurons online and view their associations.
- YeGoblynQueenne 3y ago
  You're just saying words without ever explaining why. What am I supposed to do about that? There's nothing to argue with if you're just repeating nonsensical claims without even trying to support them. For example: >> It is mechanical pattern recognition that approximates our understanding. That's just a claim and you're not even saying why you make it, what makes you think so, etc.
  - xcv123 3y ago
    > That's just a claim and you're not even saying why you make it, what makes you think so, etc. Mechanical - it is an algorithm, not a living being. Pattern recognition - a branch of machine learning that focuses on the detection and identification of regularities and patterns in data. It involves classifying or categorizing input data into identifiable classes based on extracted features. The patterns recognized could be in various forms, such as visual patterns, speech patterns, or patterns in text data. Approximates our understanding - meaning the model is not exactly the same as human understanding When I say 'mechanical pattern recognition that approximates our understanding,' what I mean is that large language models (LLMs) like GPT-4 learn patterns from the vast amounts of text data they're trained on. These patterns correspond to various aspects of language and meaning. For example, the models learn that the word 'cat' often appears in contexts related to animals, pets, and felines, and they learn that it's often associated with words like 'meow' or 'fur'. In this sense, the model 'understands' the concept of a cat to the extent that it can accurately predict and generate text about cats based on the patterns it has learned. This isn't the same as human understanding, of course. Humans understand cats as living creatures with certain behaviors and physical characteristics, and we have personal experiences and emotions associated with cats. A language model doesn't have any of this - its 'understanding' is purely statistical and based on text patterns. The evidence for these claims comes from the performance of these models on various tasks. They can generate coherent, contextually appropriate text, and they can answer questions, translate languages, and perform other language-related tasks with a high degree of accuracy. All of this suggests that they have learned meaningful patterns from their training data.
    - 3y ago