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 open AI and GPT (Generative Pre-trained Transformer). They discuss the significant interest in AI technologies and highlight the ease of access to these tools, such as ChatGPT, for the general public. The speaker also mentions the presence of experts from McGill University and the company Steamship, who will share insights on how these technologies can be utilized to build, deploy, and share applications.

The speaker delves into the technical aspects of GPT, describing it as a large language model capable of predicting the next word in a sequence. They explain that GPT has a vast vocabulary and is trained on a massive amount of internet data, allowing it to predict word probabilities. The speaker also touches on the generative capabilities of GPT, demonstrating how it can create new text by repeatedly predicting the next word. They discuss the evolution of GPT models and the concept of instruction tuning, which has led to the development of more interactive AI applications.

The speaker explores how GPT can be integrated into various applications, emphasizing its role as a foundational model for AI applications. They discuss the potential of GPT to serve as companionship, provide question-answering services, and act as utility functions. The speaker also highlights the experimental nature of AI, with developers building on GPT's capabilities to create innovative solutions. They mention the importance of domain knowledge in leveraging GPT for specific tasks and suggest that the future of AI will involve instruction tuning and the development of agents with ambiguous goals.

The speaker provides a practical example of building a companionship bot using GPT. They describe how a Mandarin idiom coach was created during a hackathon, which uses GPT to provide idiomatic expressions in response to user inputs. The speaker explains the process of wrapping GPT with additional tools and perspectives to create personalized AI companions that can interact with users in a more engaging manner. They emphasize the importance of iterating and engineering prompts to achieve consistent performance.

The speaker discusses the application of GPT in question answering, explaining the process of using GPT to respond to specific queries based on a set of documents. They outline the steps involved in engineering such applications, including document preparation, embedding vectors, and prompt engineering. The speaker also presents a simpler approach to question answering that relies solely on prompts without the need for complex systems. They demonstrate a working example using the CS50 syllabus and discuss the potential for creating numerous such applications for various contexts.

The speaker talks about the use of GPT in utility functions and creative tasks, such as generating unit tests or assisting in content creation. They emphasize the importance of domain knowledge in guiding GPT to produce relevant outputs. The speaker also discusses the potential of AI in the creative process, suggesting that it can be used to generate ideas and content, which can then be refined by humans. They highlight the accessibility of these technologies for software builders and encourage experimentation and creativity in leveraging GPT for new applications.

The speaker discusses the concept of 'baby AGI' (Artificial General Intelligence) and the potential for GPT to engage in multi-step planning and self-direction. They describe the process of allowing GPT to interact with itself in a loop, which can lead to emergent behaviors and capabilities. The speaker also talks about the development of agents with specific goals and tools, which can be used to perform tasks and interact with users in more complex ways. They suggest that the future of AI will involve a combination of instruction tuning and the collective intelligence of multiple software agents.

The speaker addresses the issue of AI hallucinations, where the model generates incorrect or misleading information. They discuss the challenges of managing these issues, as the AI's understanding is based on patterns from its training data rather than an inherent understanding of truth. The speaker suggests practical approaches to mitigate hallucinations, such as providing examples, using external databases, and employing multiple models to cross-verify outputs. They also touch on the potential for AI to incorporate human-like checking systems to improve accuracy.

The speaker reflects on the limitations of AI, particularly in logical reasoning tasks like those found in LSAT exams. They discuss the sensitivity of AI models to prompting and the potential for fine-tuning to achieve better results. The speaker also considers the future integration of AI into everyday tools and processes, suggesting that AI will become a ubiquitous component of computing. They emphasize the importance of domain expertise and the human role in guiding AI to produce valuable outputs.

The speaker discusses the privacy implications of using AI, particularly in relation to the prompts provided by users. They outline the different models of AI deployment, including SaaS, private VPC, and self-hosted versions, and discuss the balance between convenience and privacy. The speaker acknowledges the current limitations of open-source AI models compared to publicly hosted ones but suggests that as AI technology evolves, privately accessible models will become more capable. They also address the issue of prompts potentially being used for further training, highlighting the importance of considering privacy and intellectual property when interacting with AI platforms.