Podcast Summary
Andrei Mashenko's Background in Mathematics and Circle Packing: Andrei Mashenko, an engineer at OpenAI, has a math PhD and a background in circle packing research. He joined OpenAI eight months ago and currently works on the 3rd party plug-ins project. Before OpenAI, he played poker in Zurich for a year.
Andrei Mashenko, a guest on the Syntax Supper Club podcast, is an engineer at OpenAI with a background in mathematics and circle packing research. He joined OpenAI eight months ago and currently works on the 3rd party plug-ins project. Before OpenAI, he obtained a math PhD and played poker in Zurich for a year. During his graduate studies, he focused on circle packing, which explores how circles can be arranged in patterns and whether those arrangements are unique. Despite ChatGPT's inaccurate introduction, the podcast hosts were able to learn about Andrei's background through their own research and conversation with him. They also discovered that they both have connections to the University of Michigan.
ChatGPT uses plugins to expand its capabilities: ChatGPT is a conversational AI model that uses plugins to expand its functionality, allowing it to understand and respond to complex queries through external data and functions.
OpenAI's ChatGPT is a conversational AI model that uses plugins to expand its capabilities, allowing it to understand and respond to complex queries. The model is trained on a large dataset of conversations, which it uses to generate responses in a conversational format. To add plugins, the model is given access to a specific syntax and interface, enabling it to call functions and interact with external data. The plugins are deserialized and parsed by the system, and the model's response is then translated back into a format that can be understood by the user. The model itself is a complex system that uses deep learning techniques to understand and respond to natural language queries. It's important to note that ChatGPT is not a standalone product but rather an API that developers can use to build more advanced conversational AI applications. The model's ability to understand and respond to queries is a result of its extensive training on a large dataset of conversations, which allows it to generate human-like responses. The plugins add an extra layer of functionality, making the model even more versatile and powerful. The developer discussed in the conversation has built an internal tool called Code Chat, which is a simpler version of ChatGPT's UI, used for prototyping new capabilities for the model.
Training large language models with human feedback: Large language models learn from human feedback during training to generate helpful and engaging text.
Large language models like the one discussed operate based on a string completion model. They predict the next word in a sentence based on the context provided and learn from this simple task to implicitly grasp various facts and concepts about the world. This learning happens primarily during the fine-tuning phase. The model uses a neural network with billions of parameters to make these predictions, which requires heavy computing power. The RLHF (Reinforcement Learning with Human Feedback) method is another aspect of the training process. It involves training two models: a reward model and the actual model. The reward model evaluates completed strings or sets of messages and estimates their quality based on human feedback. This feedback is collected from labelers who evaluate the generated outputs and provide ratings. The reward model learns what humans prefer, which the string completion model does not. It is trained to generate conversations that humans find helpful and engaging, enhancing the overall performance of the language model. The feedback for the reward model comes from various sources, including labelers' evaluations. While user interactions, such as thumbs up or thumbs down in a chat window, could also be used, it is unclear whether this is the case for the specific model being discussed. The goal is to create a model that generates text that resonates with and is useful to humans, making the learning process more effective and valuable.
YouTube no longer displays thumbs down counts publicly but uses them for content evaluation: YouTube's algorithm uses thumbs down data for content evaluation in secret, while plugins expand ChatGPT's functionality and automate tasks, Python is popular for machine learning, and React is common for front-end development
YouTube does not disregard thumbs down buttons as some may believe, but they no longer display the number of thumbs downs publicly. Instead, their algorithm likely uses this information behind the scenes for content evaluation. Moving on, plugins expand the functionality of ChatGPT by allowing users to integrate third-party services, enabling the model to access APIs and perform tasks that were previously unavailable. These plugins can save time and effort by automating repetitive tasks. As for the languages used in building these systems, Python is popular due to its compatibility with major machine learning frameworks and ease of use, while React is commonly used on the front end. Despite the excitement about a potential compiled version of Python, it's important to note that achieving full language conformity might not be possible.
Dynamic nature of Python allows for monkey-patching functions and classes at runtime: Python's dynamic nature enables functions to be modified during runtime, offering advantages similar to JavaScript and TypeScript, while the unexpected popularity of ChatGPT required the team to adapt quickly from a niche company to a consumer product, and the speaker's math background has been beneficial for his career as a developer
Python, as a dynamically typed language, offers the ability to modify functions and class definitions at runtime, which can be compared to the dynamic nature of JavaScript and TypeScript. This dynamic nature allows for functions to be monkey-patched even while they're running. While TypeScript gets stripped out at compile time, there are runtimes like Deno that do it at runtime. The discussion also touched upon the unexpected popularity explosion of OpenAI's ChatGPT, which was released around 2 months before the conversation. The team had to quickly adapt to the sudden attention and shift from being a niche company to a consumer product. The speaker's background in math has been beneficial for his career as a developer, providing him with a rigorous thinking process and valuable vocabulary, which is also relevant to language models.
Working with abstractions in math and programming: Math and programming both use abstractions to manage complexity. In programming, we use functions, classes, and higher level abstractions. In math, we abstract away complex concepts into symbols and equations. Math majors are in high demand in tech due to the importance of algorithms and problem-solving, but finding a professorship is competitive.
Both math and programming involve working with abstractions to manage complexity. The speaker explained that in programming, we use functions, classes, and higher level abstractions to avoid dealing with base-level abstractions all the time. Similarly, in math, we abstract away complex concepts into symbols and equations. The speaker also shared that they were introduced to programming in high school and then accidentally declared a math major in college because they enjoyed a particular math class. Math majors are now in high demand in the tech industry due to the increasing importance of algorithms and technical problem-solving. The speaker noted that their friends from math programs have done well in tech, consulting, finance, and academia, but finding a professorship is highly competitive. The speaker also mentioned that they learned to program in high school through a CS program started by a former software engineer and that they enjoyed math because of its abstract nature and technical puzzles.
AI tools augment human jobs, not replace them: AI handles data analysis and trend identification, freeing up time for higher-level thinking and problem-solving
AI tools like Copilot are not intended to replace human jobs but rather augment them. While AI can handle data analysis and trend identification efficiently, humans are needed to ask the right questions, focus their attention, and make decisions based on the data. The use of AI in tasks such as coding or data analysis can free up time for more complex and conceptual work. However, it's important for individuals to adapt and learn how to effectively leverage AI tools to complement their skills. The shift in work will be towards higher-level thinking and problem-solving, rather than routine tasks. While there may be concerns about job displacement, the overall impact of AI on the workforce is expected to be a net positive, enabling us to accomplish more with the same or even fewer resources.
The Future of Work: Automation and New Opportunities: As technology advances, some jobs may be automated, but new opportunities may also emerge. Focusing on crafts and industries requiring human touch could be potential areas for employment.
As AI and automation continue to advance, there will be categories of labor that will be automated, leading to a shift in the types of jobs people will do. Some possibilities include focusing on crafts that don't scale, such as building custom homes or writing poetry, or industries that have already been affected by technology but still require human touch, like farming with advanced machinery. The pace of this change is uncertain and it's important to note that not all industries will be immune. While there are valid concerns about job loss, it's also important to consider the potential for new opportunities and industries to emerge. The speaker also mentioned the use of advanced technology in farming, such as laser beams to remove weeds and self-driving robots, which are already having a significant impact on the industry. Overall, the future is uncertain, but it's clear that technology will continue to change the nature of work.
The Future of Interacting with Tools: Plugin Interfaces: We'll continue using programming languages, but better abstractions and code generation tools will complement them.
We are on the brink of a significant shift in how we interact with tools, particularly in the realm of AI, with the rise of plugin interfaces. This interface allows for more intuitive, conversational interactions, such as ordering a pizza or managing tasks, and it's a direction the speaker sees things going due to its user-friendly nature. However, the question of whether we will continue writing Python and JavaScript to build these types of things or move towards higher-level abstractions is still up for debate. While efforts are being made to improve abstractions and create no-code or low-code solutions, professional enterprises still prefer having control over the low-level details. The Language Model (LLM) can help auto-generate code, but it's still pattern-matchy enough for the engine to be effective. In summary, programming languages will continue to be used, but they will be complemented by better abstractions and more advanced code generation tools.
AI models like Language Models can understand and generate language, automate tasks, and even write code.: AI models, such as Language Models, can understand and generate human-like language, automate tasks, and even write code, making them valuable tools for various industries.
AI models, such as Language Model (LLMs), are excellent at language and pattern matching, making them ideal for smoothing the transition between natural and formal language. These models can even write code for you and execute formal language instructions in response to natural language commands. However, the randomness in the output is a feature, not a bug, and the models can't always provide the same output for the same input. AGI (Artificial General Intelligence) is the ultimate goal, and LLMs will likely be a part of it, but there's debate over whether a pure language model can scale all the way up to AGI. AGI is defined as an autonomous system that performs economically relevant tasks as well or better than humans. A fascinating AI tool currently making waves is Microsoft's Copilot, which changes the way we code by understanding context and reading open files. The number of tokens it can process is increasing, and the new chat feature seems to pass more data to it. Overall, AI's ability to understand and generate language, as well as its potential to automate tasks, makes it an exciting and transformative technology.
Exploring the capabilities of AI models like ChatGPT: AI models like ChatGPT can generate library documentation, understand and answer customer queries, and simplify complex processes, making everyday tasks more efficient and convenient.
AI models like ChatGPT are becoming increasingly capable of handling complex tasks and providing accurate responses, making them valuable tools for various applications, especially in areas where human interaction is time-consuming or less effective. The speaker shared an experience of generating a library with documentation using ChatGPT and was impressed by its ability to understand and execute the task. He also mentioned the potential of AI in improving customer service experiences, such as understanding and answering customer queries effectively, and even simplifying complex processes like filing taxes. However, it's important to remember that AI should not replace human expertise entirely, and there are still limitations and potential errors that need to be addressed. Overall, the speaker expressed excitement about the potential of AI to make everyday tasks more efficient and convenient.
AI enhances legal capabilities, but human relationships matter: AI improves understanding of complex laws, but human connection and emotional intelligence remain vital in the legal profession. Find what works best for you in coding projects, focus on the enjoyable aspects, and continue learning to succeed.
Technology, particularly AI, is increasingly becoming an essential tool in various industries, including law. While it may not completely replace human lawyers, it can significantly enhance their capabilities by providing a better understanding of complex laws and regulations. However, human relationships and emotional intelligence remain crucial aspects of the legal profession. Regarding technology tools, developers often experiment with different text editors, terminals, and keyboard shortcuts to optimize their workflow. Some prefer customizing their setup extensively, while others opt for the default settings. The key is to find what works best for each individual. When starting a new coding project, focusing on what is interesting and enjoyable can lead to greater learning and growth. While it's essential to learn the fundamentals, being curious and having fun can lead to a more rewarding development career. As both Wes and the guest emphasized, the most successful developers are those who remain curious and continue to learn new things.
Deepen your understanding of programming tools: To effectively develop, learn the basics but also dig deeper to fully grasp programming tools concepts. Eliminate unfamiliar elements to focus on building a solid foundation of knowledge.
Deep understanding of the tools and technologies used in programming is essential for effective and efficient development. The speaker emphasizes the importance of not only learning the basics but also digging deeper to fully grasp the concepts. He shares his personal experience of reading books and learning vanilla JS before moving on to more complex tools like TypeScript and React. He advises against ignoring unfamiliar concepts in code and instead encourages developers to take the time to investigate and understand each element. This approach builds a stronger mental model of the program and compounds the understanding of individual concepts. The speaker's main tip for learning deeply is to eliminate anything in the codebase that is not fully understood. By doing so, one can avoid feeling overwhelmed by unfamiliar concepts and instead focus on building a solid foundation of knowledge. This will lead to more context, better understanding, and fewer roadblocks in the development process.
