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GPT-4 Is a R easoning Engine

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Summary

This article discusses the importance of knowledge and reasoning in GPT models, which are AI reasoning engines. It explains how GPT-4, the most advanced model on the market, is limited by its lack of knowledge, and how its performance can be improved by providing it with the right information. The article also explains the importance of vector databases in storing and making available information to AI applications. Finally, it suggests that private knowledge databases will be a valuable asset to AI, making it easier and more efficient to find the right pieces of information at the right time.

Q&As

What is GPT-4?
GPT-4 is a reasoning engine.

How do AI critics view GPT-4?
AI critics view GPT-4 as nothing more than a stochastic parrot and dismiss its results offhand.

What are vector databases and why are they important?
Vector databases allow for the easy indexing and storage of large amounts of information, and they can quickly query for similar pieces of information to give to AI models when needed. They are important for all sorts of machine learning algorithms.

What is the importance of storing private personal knowledge databases?
Private personal knowledge databases are valuable because they are expensive and time consuming to find relevant information. Having a personal set of notes, articles, books, and highlights can customize an AI experience so it is more useful right off the bat.

How does GPT-4's performance improve when it has access to the right information?
GPT-4's performance improves dramatically when it has access to the right information. Instead of using its reasoning capabilities to come up with a theoretically plausible answer, it does web research to create a knowledge base for itself and distills a more accurate answer.

AI Comments

👍 This article is a great exploration of the importance of knowledge databases in AI and how they intertwine with reasoning.

👎 The article is too long and overly complicated for its own good, making it difficult to comprehend its main points.

AI Discussion

Me: It's about how GPT-4 is a reasoning engine, and how knowledge and reasoning are both important components of thinking. It also talks about how advances in AI will come from advances in its ability to access the right knowledge at the right time, and how vector databases are important for AI applications.

Friend: That's really interesting. It seems like AI is becoming more powerful and more advanced every day.

Me: Absolutely! It's amazing to think about the implications of this article. Vector databases are becoming increasingly important, and it's essential to have access to the right information at the right time to make AI applications more useful. Personal knowledge databases will also be really valuable, as people can customize their AI experience to be more useful to them. It's also interesting to think about the implications for investing in AI.

Action items

Research vector databases and how they are used in AI applications.
Create a personal knowledgebase of notes, articles, books, and highlights.
Experiment with AI tools that use vector databases and personal knowledgebases to enhance their responses.

Technical terms

GPT-4: GPT-4 is a type of artificial intelligence (AI) model developed by OpenAI. It is a language model that uses natural language processing (NLP) to generate human-like text.
Reasoning Engine: A reasoning engine is a type of artificial intelligence (AI) system that uses logical reasoning to solve problems. It is able to take a set of facts and rules and use them to draw conclusions and make decisions.
Vector Database: A vector database is a type of database that stores data in the form of vectors. Vectors are mathematical objects that can represent data points in a multi-dimensional space. Vector databases are used in AI applications to store and quickly query large amounts of information.
Large Language Model (LLM): A large language model (LLM) is a type of artificial intelligence (AI) system that uses natural language processing (NLP) to generate human-like text. LLMs are trained on large datasets of text and are able to generate text that is similar to the text in the dataset.
Next Token Prediction: Next token prediction is a type of natural language processing (NLP) task in which a model is trained to predict the next word or token in a sequence of text. This task is used in AI applications such as language models and chatbots.