[ExI] An article from an AI-skeptic.

[Darin Sunley]

I just watched a bunch of python and neural net videos on youtube this
afternoon.

This one, in particular, is an amazing medium-level overview of what
ChatGPT is doing. It's all animated diagrams, easy to follow even if you
aren't a programmer.

The key thing you'll get from this, even if you're non-technical, is the
sheer size and complexity of what the GPT-3 transformer is doing. It's
literally mindblowing.

https://www.youtube.com/watch?v=wjZofJX0v4M

On Sat, Apr 13, 2024 at 6:10 PM BillK via extropy-chat <
extropy-chat at lists.extropy.org> wrote:

> On Sat, 13 Apr 2024 at 22:57, BillK <pharos at gmail.com> wrote:
>
>>
>> It also mystifies me. It seems that generating correct sentences is only
>> a relatively small final part of the process.
>> I asked Claude to explain how he does the trick. I don't think I really
>> even understand the explanation!  :)
>>
>
>
> So I asked Claude to explain the steps in his thinking process and give
> some examples
>
> Claude:
> As an AI language model, I use a combination of natural language
> processing (NLP) techniques and deep learning algorithms to understand and
> respond to questions. The process can be broken down into several steps:
>
> 1. Input processing: When you ask a question, I first analyze the input
> text, breaking it down into individual words, phrases, and sentences. This
> helps me understand the structure and meaning of your query.
>
> 2. Tokenization and embedding: The input text is then converted into
> numerical representations called tokens and embeddings. These
> representations capture the semantic and syntactic relationships between
> words and phrases, allowing me to process the input more effectively.
>
> 3. Context understanding: Using the embeddings and my pre-trained
> knowledge, I try to understand the context of your question. This involves
> identifying key entities, concepts, and the relationships between them, as
> well as determining the intent behind your query.
>
> 4. Knowledge retrieval: Based on the context and intent of your question,
> I access relevant information from my pre-trained knowledge base. This
> knowledge base is built using vast amounts of text data from various
> sources, such as books, articles, and websites.
>
> 5. Response generation: Using the retrieved knowledge and the context of
> your question, I generate a response. This involves selecting the most
> relevant information, structuring it coherently, and expressing it in
> natural language.
>
> 6. Output processing: Finally, the generated response is processed to
> ensure it is grammatically correct, contextually appropriate, and adheres
> to any predefined rules or constraints (such as avoiding harmful content).
>
> Throughout this process, I rely on the patterns and relationships learned
> during my training phase, which involved processing enormous amounts of
> text data. This allows me to understand and respond to a wide range of
> questions, even if they are phrased in different ways or cover topics I
> haven't encountered before.
>
> It's important to note that while I can process and respond to natural
> language effectively, my understanding is based on statistical patterns and
> associations rather than true comprehension or reasoning like humans.
> ---------
>
> Now give some examples -
>
> Claude:
>
> Certainly! Let's use the question "What is the capital of France?" as an
> example to illustrate each step.
>
> 1. Input processing:
>    - Input text: "What is the capital of France?"
>    - Processed text: ["What", "is", "the", "capital", "of", "France", "?"]
>
> 2. Tokenization and embedding:
>    - Tokens: ["What", "is", "the", "capital", "of", "France", "?"]
>    - Embeddings: Each token is converted into a high-dimensional vector
> representation. For example, "France" might be represented as [0.2, 0.4,
> -0.1, ..., 0.8].
>
> 3. Context understanding:
>    - Entities: "France" (recognized as a country)
>    - Intent: The question is asking for the capital city of France.
>
> 4. Knowledge retrieval:
>    - Relevant facts:
>      - France is a country in Europe.
>      - The capital of France is Paris.
>      - Paris is the largest city in France and a major cultural and
> economic center.
>
> 5. Response generation:
>    - Generated response: "The capital of France is Paris. Paris is the
> largest city in France and serves as the country's cultural and economic
> center."
>
> 6. Output processing:
>    - Final response: "The capital of France is Paris. Paris is the largest
> city in France and serves as the country's cultural and economic center."
>
> Here's another example with a more complex question: "What are the main
> causes of climate change, and what can individuals do to help mitigate its
> effects?"
>
> 1. Input processing:
>    - Input text: "What are the main causes of climate change, and what can
> individuals do to help mitigate its effects?"
>    - Processed text: ["What", "are", "the", "main", "causes", "of",
> "climate", "change", ",", "and", "what", "can", "individuals", "do", "to",
> "help", "mitigate", "its", "effects", "?"]
>
> 2. Tokenization and embedding:
>    - Tokens: ["What", "are", "the", "main", "causes", "of", "climate",
> "change", ",", "and", "what", "can", "individuals", "do", "to", "help",
> "mitigate", "its", "effects", "?"]
>    - Embeddings: Each token is converted into a high-dimensional vector
> representation.
>
> 3. Context understanding:
>    - Entities: "climate change", "individuals"
>    - Intent: The question is asking for the main causes of climate change
> and actions individuals can take to help reduce its impact.
>
> 4. Knowledge retrieval:
>    - Relevant facts:
>      - Climate change is primarily caused by human activities that release
> greenhouse gases into the atmosphere, such as burning fossil fuels and
> deforestation.
>      - Individuals can help mitigate the effects of climate change by
> reducing their carbon footprint, supporting renewable energy, and
> advocating for climate-friendly policies.
>
> 5. Response generation:
>    - Generated response: "The main causes of climate change are human
> activities that release greenhouse gases into the atmosphere, such as
> burning fossil fuels for energy, transportation, and industrial processes,
> as well as deforestation and land-use changes. To help mitigate the effects
> of climate change, individuals can take actions such as reducing their
> energy consumption, using renewable energy sources, driving less or using
> electric vehicles, supporting climate-friendly policies, and making
> environmentally conscious consumer choices."
>
> 6. Output processing:
>    - Final response: (same as the generated response, as it is already
> well-structured and concise)
>
> These examples demonstrate how I break down and process questions to
> generate relevant and informative responses based on my pre-trained
> knowledge.
> ---------------------------
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