Row 64420

Row ID: 64420 | Dataset Entry | Axioma AXP Content Repository

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Part 2. Due to the complexity required "X" in a real neural network, is actually composed of every value for every weight and every bias of every node and every single one of its connections to every other node in the layers before and after it, of which there are often quite a lot. An LLM like GPT-4 is built to accurately mimic human text responses. A human can understand and be influenced by emotion, this means GPT-4 needs to assign a section of its brain to accurately predict and replicate emotion, otherwise, when I asked it "if a pirate got hit on the head with a foam sword or had his mother referred to as a walking talking fleshlight which would make him angrier?" it would not know what to say, since there is a low chance of that exact example being present in its training data, however, it answers that question pretty well. This is just one example (alongside being able to somewhat convincingly roleplay as a human) of how neural networks build some level of understanding/capability for things as a side effect of complying with their training pressure. Things such as: emotion, physics, logic, conversational rules, strategic planning, mathematical theory, cultural understanding, individual preference, theorization, and even limited understanding of time and 3D space (which it has practically no actual experience of). are some things we know it has some level of understanding for. I could keep going on and on, but as far as I am aware even OpenAI doesn't know exactly what the GPT series is capable of understanding/reproducing since when you look at its "brain" from the outside all you see is a bunch of values that mean nothing until they are put together with every other value for every other node in the neural network. All we know is what results we get when we give it a certain prompt.

But yeah I guess it just exactly puts word after word, and the math is actually pretty simple right?

FieldValue
text Part 2. Due to the complexity required "X" in a real neural network, is actually composed of every value for every weight and every bias of every node and every single one of its connections to every other node in the layers before and after it, of which there are often quite a lot. An LLM like GPT-4 is built to accurately mimic human text responses. A human can understand and be influenced by emotion, this means GPT-4 needs to assign a section of its brain to accurately predict and re…
label r/chatgpt
dataType comment
communityName r/ChatGPT
datetime 2024-05-23
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Raw Record

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  "text": "Part 2.  \n  \nDue to the complexity required \"X\" in a real neural network, is actually composed of every value for every weight and every bias of every node and every single one of its connections to every other node in the layers before and after it, of which there are often quite a lot.  \n  \nAn LLM like GPT-4 is built to accurately mimic human text responses. A human can understand and be influenced by emotion, this means GPT-4 needs to assign a section of its brain to accurately predict and replicate emotion, otherwise, when I asked it   \n\"if a pirate got hit on the head with a foam sword or had his mother referred to as a walking talking fleshlight which would make him angrier?\"   \nit would not know what to say, since there is a low chance of that exact example being present in its training data, however, it answers that question pretty well. This is just one example (alongside being able to somewhat convincingly roleplay as a human) of how neural networks build some level of understanding/capability for things as a side effect of complying with their training pressure.  \n  \nThings such as: emotion, physics, logic, conversational rules, strategic planning, mathematical theory, cultural understanding, individual preference, theorization, and even limited understanding of time and 3D space (which it has practically no actual experience of). are some things we know it has some level of understanding for.  \n  \nI could keep going on and on, but as far as I am aware even OpenAI doesn't know exactly what the GPT series is capable of understanding/reproducing since when you look at its \"brain\" from the outside all you see is a bunch of values that mean nothing until they are put together with every other value for every other node in the neural network. All we know is what results we get when we give it a certain prompt. \n\nBut yeah I guess it just exactly puts word after word, and the math is actually pretty simple right?",
  "label": "r/chatgpt",
  "dataType": "comment",
  "communityName": "r/ChatGPT",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1ibnFlQU9vazhnUkdvYUJuWnhoV3dnYWo5T0hHblI1d1gwc1dWSGR2TVRwY0JiVC1kSmg0dE52N01YZUdmeW01MWhsLW1SNkZoS2RfZVcyNW1NYkZoWEE9PQ==",
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