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

The speaker introduces the topic of AI, specifically focusing on GPT (Generative Pre-trained Transformer). They discuss the significant interest in AI technologies, including open AI and GPT chatbots, and share an anecdote about a recent event where they had 100 RSVPs within an hour, showcasing the demand for learning about these technologies. The speaker also mentions the availability of tools for those interested in exploring AI further, such as signing up for a free account to experiment with Chat GPT and using low-level APIs for software integration. The segment ends with an introduction to guests from McGill University who will discuss the ease of deploying applications using these technologies.

The speaker delves into the mechanics of how GPT functions, describing it as a large language model that predicts the next word in a sequence based on a vocabulary of 50,000 words. They explain that GPT has been trained on the entire internet, allowing it to understand which words are likely to follow one another. The speaker also touches on the concept of probability in relation to word prediction and the model's ability to generate new text by appending predicted words. The segment highlights the evolution of GPT models and their increasing capabilities, such as understanding and responding to questions through instruction tuning and reinforcement learning.

The speaker discusses the versatility of GPT and how it can be integrated into various applications. They mention the creation of chatbots and the use of GPT as a writing assistant, content generator, and agent for tasks like interacting with the world. The speaker also talks about the concept of 'instruction tuning' and how it has led to the development of applications like Chat GPT, which gained a significant number of users shortly after its release. They also highlight the potential of GPT to interface with the world in a meaningful way, leading to the development of tools that build on the question-and-answer format used by Chat GPT.

The speaker explores the potential of using GPT to build various applications. They discuss the concept of companionship bots, which are designed to provide support and assistance with specific tasks or goals. The speaker shares a demo of a Mandarin idiom coach, a bot that generates Chinese idioms based on user input, as an example of how GPT can be used to create educational tools. They also touch on the importance of iterating and engineering prompts to ensure consistent performance. The segment concludes with a discussion on the accessibility of building AI applications, emphasizing that it is within reach for individuals with varying levels of expertise.

The speaker discusses the application of GPT in enhancing search and question-answering capabilities. They explain the process of embedding documents into a vector database, which allows for efficient searching and retrieval of information based on similarity to a query. The speaker provides a practical example of building a question-answering system using the CS50 syllabus, demonstrating how GPT can be prompted to provide specific answers based on source documents. They also mention the potential of creating multiple such systems for different contexts and the importance of prompt engineering in refining the accuracy of the model's responses.

The speaker talks about using GPT for utility functions and creativity. They describe how GPT can automate tasks that require basic language understanding, such as generating unit tests or performing brand checks. The speaker emphasizes the importance of domain knowledge in leveraging GPT for creative tasks, such as generating story ideas or advertising headlines. They provide an example of a writing Atlas project that uses GPT to suggest short stories based on user preferences, highlighting the role of domain knowledge in refining the model's output. The segment concludes with a call to action for builders to explore the potential of GPT in their projects.

The speaker addresses the issue of hallucinations in GPT's responses and discusses ways to mitigate this problem. They mention the use of examples, fine-tuning, and post-processing as methods to improve the model's accuracy. The speaker also talks about the potential of using multiple models in unison to reduce the occurrence of hallucinations, drawing an analogy with the redundancy systems in spacecraft. They emphasize the importance of treating GPT as a tool that can be fine-tuned and directed towards specific tasks, rather than a standalone solution.

The speaker reflects on the limitations of GPT, particularly its inability to reason logically in the same way humans do. They discuss the sensitivity of GPT to prompts and the potential for it to contradict itself. The speaker also talks about the future of AI, suggesting that models like GPT will become integrated into all aspects of computing, much like microprocessors. They encourage the audience to think of GPT as a foundational tool that will evolve and improve over time, becoming an essential part of software development and problem-solving.

The speaker discusses the privacy implications of using AI and GPT models. They outline the different models of software deployment, including SaaS (Software as a Service), private VPC (Virtual Private Cloud) versions, and running one's own machines. The speaker acknowledges that prompts used in GPT models may be used for training purposes, raising concerns about intellectual property and privacy. They suggest that while current models may be sufficient for some tasks, the industry is moving towards more sophisticated models that offer better intelligence, and users will need to consider the balance between privacy and the benefits of using advanced AI tools.