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GPT-4 - How does it work, and how do I build apps with it? - CS50 Tech Talk

CS50・53 minutes read

High interest in AI, GPT chat, and open AI tech talk with 100 RSVPs in an hour, leading to exploration of GPT's evolution, potential applications, and limitations, showcasing diverse uses of language models. AI models like GPT4 show advancements, with companies leveraging AI applications for economic benefits and potential wide integration into everyday devices as a second processor.

Insights

  • GPT, a powerful language model, evolves into Chat GPT, enabling question-answering interactions and problem-solving capabilities, showcasing its versatility beyond text generation.
  • AI, particularly models like GPT, offers vast potential in creating companionship bots, question-answering systems, utility functions, and fostering creativity, highlighting the diverse applications and opportunities for software builders to explore.

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Recent questions

  • What is GPT?

    A large language model predicting text.

  • How can GPT be utilized?

    For companionship, question-answering, and creativity.

  • What are the challenges with GPT models?

    Struggles with reasoning and logic.

  • How can AI models like GPT be improved?

    Through multi-step planning and domain knowledge.

  • What is the future of AI models like GPT?

    Widespread integration into everyday devices.

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Summary

00:00

"Exploring AI Tech Talk on GPT"

  • A Google form was circulated for an AI tech talk, resulting in 100 RSVPs within an hour, showcasing high interest in AI, GPT chat, and open AI.
  • A URL was shared for trying out the chat GPT tool, sign up for a free account, and explore the technology.
  • Open AI offers low-level APIs for integrating AI into software, with additional abstractions and services built on top of these technologies.
  • Friends from McGill University and Steamship are present to discuss simplifying application building, deployment, and sharing using similar technologies.
  • Ted and Sill will delve into the workings of GPT, its research grounding, and examples of real-world app development.
  • GPT is described as a large language model, a generative AI, a neural network, and an artificial intelligence, predicting the next word in a sequence based on probabilities.
  • GPT's ability to generate new text is highlighted, with its training on vast amounts of internet data leading to improved predictions and expressive capabilities.
  • GPT's evolution into a question-answering model, known as Chat GPT, is explained, allowing for interaction in a Q&A format and problem-solving.
  • Instruction tuning and reinforcement alignment with human feedback have enhanced GPT's ability to operate as an agent, interacting with the world effectively.
  • Ted emphasizes the potential of GPT in building companionship, question-answering, utility functions, creativity, and wild experiments, showcasing the diverse applications of language models.

13:24

Creating Companionship Bots with GPT Technology

  • Companionship bots can be created by wrapping GPT or a language model in an endpoint that injects a specific perspective or goal into prompts.
  • An example of this is a mandarin idiom coach that generates poetic Chinese idioms based on user input.
  • The process involves using GPT, injecting personality into prompts, and potentially adding tools for web searches or database interactions.
  • The ease of building such bots lies in manipulating prompts creatively and adding a specific lens to the conversation.
  • Question answering apps involve querying GPT for specific information from documents or articles, requiring the creation of embedding vectors for text fragments.
  • The process includes cutting up documents, turning text fragments into embedding vectors, and storing them in a vector database for retrieval.
  • A simple prompt can be used to create a question-answering system by loading information into an agent with access to GPT.
  • Utility functions can automate tasks like generating unit tests, looking up documentation, or ensuring code compliance, utilizing basic language understanding.
  • These tasks are context-free operations that can be automated with linguistic understanding, presenting a vast space for software builders to explore.
  • Creativity in utilizing tools like GPT opens up possibilities for weekend projects, startup ideas, and exploring new applications in various fields.

27:47

AI's Creative Process and Domain Knowledge

  • AI generates staggering results, raising questions about IP and artistic style.
  • The template for AI builders involves domain knowledge.
  • Creative process: conceive a big idea, generate possibilities, edit down, repeat.
  • AI excels in tasks like generating possibilities due to the pre-agreed deletion process.
  • Use domain knowledge to guide AI in generating content.
  • Example of an app using AI to suggest stories based on user input and domain knowledge.
  • AI searches for similar stories, uses GPT to provide tailored suggestions.
  • Utility apps benefit from human domain knowledge to enhance AI-generated content.
  • Baby AGI concept involves multi-step planning bots for more complex interactions with GPT.
  • Developers can mitigate hallucination issues by providing examples and external databases for specific knowledge.

42:05

Evolving AI Models: Collaboration and Integration

  • A programming model is evolving where multiple software agents, each with distinct objectives and skills, collaborate to solve problems collectively.
  • Spacecraft systems are over-engineered with three computers that must unanimously agree on decisions to ensure safety, highlighting the importance of redundancy in critical systems.
  • Hallucinations in AI models are typically isolated incidents, and having multiple models working together can enhance overall success rates.
  • Language models like GPT can simulate personalities and interactions based on prompts, reflecting how people communicate and interact in various scenarios.
  • GPT models struggle with reasoning and logic, often providing plausible but contradictory answers, showcasing limitations in their ability to think and reason like humans.
  • GPT4 has shown progress in passing tests like the LSAT, indicating advancements in the model's capabilities over time.
  • Companies are leveraging AI applications like GPT to create value in various domains, with experimentation ongoing to explore economic benefits.
  • The future of AI models like GPT may involve widespread integration into everyday devices, akin to a second processor, with potential applications in diverse fields.
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