Large Language Models and The End of Programming - CS50 Tech Talk with Dr. Matt Welsh

Insights from the video:

The Future of Programming and AI

🌐 Dr. Matt Welsh envisions a future where computers will write code for us, potentially eliminating the need for manual coding.

🤖 The field of computer science is doomed because the core idea of translating algorithms into programs that can be understood by humans is being challenged by AI.

🤔 50 years of research into programming languages has done effectively nothing to solve the problem of humans being terrible at writing, maintaining, and understanding programs.

💻 Co-pilot, an AI tool for writing code, is a game changer for real-world developers and has become an essential tool in their workflow.

💡 The language model, Co-pilot, is incredibly good at interpreting code and understanding the programmer's intent, making it a powerful tool for developers.

🌌 The revolution in AI seemed to occur overnight, going from "kind of crappy" to "amazing" in a short span of time, which is mind-blowing for those who haven't been following the literature for years.

💻 The future of programming may involve skipping the programming step entirely, with AI generating code without the need for human programmers.

💡 "I think what we need to do is get the humans out of the loop on that and change the relationship between humans and the way software is built so that we can unlock that potential."

🔄 The symbiosis between humans and AI models may be the key to effective problem-solving, with each entity leveraging their strengths and iterating together, as seen in tools like copilot.

Implications for Society and Education

🤔 "What does it mean for our society if we have AI that is smarter than us and people are taking that very seriously today?"

🤔 It's time for the field of computer science to evolve and consider the implications of AI language models like chat GPT and co-pilot in education and programming.

🚀 "People write programs in C in 2023 that should be a federal crime if you think about how many software mistakes bugs crashes have endangered and actually killed people."

💡 Academic computer science education should focus on teaching students to reason about AI models, understand their mechanics, limitations, and how to evaluate them, rather than viewing them as magical black boxes.

Advancements in AI Language Models

🤔 Contrary to popular belief, large language models can go beyond simple autocorrect and actually perform interesting and creative tasks by manipulating a model of the world in their minds.

🧠 The emergence of a new computational architecture, the "natural language computer," where programs are written in natural language and language models can call out to external systems and generate new knowledge.

Summary

TLDR: Large language models have the potential to revolutionize programming by generating code, but collaboration between humans and AI is necessary to address their limitations and biases.

1. 00:00 🤖 Computer science faces a bleak future as AI challenges the assumption that humans must understand code, and despite 50 years of research, automated tools to help humans write better software have reached their limit, leading to the emergence of large language models that can write code but are too complex to be written in any programming language.

   1.1 Dr. Matt Welsh discusses the future of programming, envisioning a world where computers write code for us.

   1.2 Computer science is facing a bleak future as the field's core assumption that programs must be understood by humans is challenged by the emergence of AI.

   1.3 50 years of research into programming languages has not solved the problem of humans being bad at writing, maintaining, and understanding programs, and the idea of building automated tools to help humans write better software has reached its limit.

   1.4 Programming languages like Fortran, Basic, and APL have made programming easier over the years, but understanding the intent of the programmer can still be challenging without prior knowledge.

   1.5 Programming languages have evolved from complex ones like Rust to more user-friendly ones that are easier to understand, maintain, and reason about.

   1.6 Large language models are being used to write code by providing prompts and instructions, but the complexity of these algorithms makes it impossible to write them in any existing or future programming language.

2. 08:12 🤖 Large language models like Co-pilot are revolutionizing programming by understanding natural language, generating code, and increasing productivity, but also raising concerns about job displacement.

   2.1 There is no mathematical proof or symbolism, and it is unclear if the information can be understood or reasoned about.

   2.2 We are entering an era where machines can understand natural language and produce computational results, replacing traditional programming with instructing language models.

   2.3 Co-pilot, a code-writing tool, has become essential for programmers, providing a significant boost in productivity and efficiency, and is now considered a game-changer in the industry.

   2.4 Using large language models like co-pilot keeps developers in the zone of writing code, increasing productivity and leading some companies to mandate its use.

   2.5 Large language models like Co-pilot and Chat GPT are incredibly good at interpreting and generating code, understanding data structures, and providing relevant information, making programming tasks easier and more efficient.

   2.6 AI technology is advancing rapidly, raising concerns about the future of the programming industry and the potential for job displacement.

3. 17:48 🤖 Large language models like GPT-3 have the potential to revolutionize the programming industry by reducing the need for highly educated programmers and allowing for rapid code generation with minimal mistakes, potentially replacing human engineers in certain tasks.

   3.1 On average, software engineers in Silicon Valley or Seattle cost their employers around $1,200 a day, but often check in zero lines of code due to various factors like waiting on others or attending meetings.

   3.2 The cost of using GPT-3 for the output of one human software developer is 12 cents, based on an average of 100 lines of code per day.

   3.3 Large language models have the potential to drastically change the programming industry by reducing the need for highly educated programmers and allowing robots to do most of the work.

   3.4 Large language models can quickly generate code with minimal mistakes, allowing for rapid iteration and potentially reducing the need for human engineers in certain tasks.

   3.5 The future software team may include a human product manager who translates business and user requirements into a usable form.

4. 23:29 🤖 The future of programming is AI-generated code that humans review and improve, as messy and duplicative code doesn't matter as long as it works, leading to a societal shift in our expectations of AI's capabilities.

   4.1 In the future, AI code generators will provide code that humans will still need to review and make improvements on.

   4.2 The speaker argues that as long as code generated by robots does what it's supposed to do, it doesn't matter if it's messy, structured, reusable, or duplicative, as these concerns are only relevant for humans dealing with complex code bases.

   4.3 The rapid advancement of AI, particularly in the field of language models like GPT, has surprised many and is leading to a societal shift in our expectations of what AI can achieve, reminiscent of the gradual progress seen in computer graphics.

   4.4 Computers were once seen as fundamentally dumb, but now there is a shift in society's dialogue towards the potential destruction of society by AI that is smarter than humans.

   4.5 The future of programming is skipping the programming step entirely, where humans will no longer need to write code.

   4.6 Large language models can compute results without traditional programming steps, presenting both challenges and opportunities in effectively teaching and engineering these models, as they have the potential to manipulate complex problems and models of the world.

5. 31:46 🤖 Programming may be replaced by teaching language models new skills, leading to the emergence of a new computational architecture called the natural language computer, as demonstrated by Fixie's software that combines natural language and programming language to create custom chatbots.

   5.1 The model can accurately solve a simple problem of laying out three stacks of cards by following step-by-step instructions, but it can also be easily tripped up and provide incorrect results.

   5.2 Programming may eventually be replaced by teaching language models new skills and how to interact with software, leading to the emergence of a new computational architecture called the natural language computer.

   5.3 We are in the early stages of developing a natural language computer and our startup, Fixie, is focused on building software that can generate revenue and attract customers.

   5.4 Fixie aims to make it easy for developer teams to create custom chatbots that can understand and respond to data, by providing a programming abstraction that combines natural language and programming language.

   5.5 The framework ai.Jsx allows for the natural composition of operations, such as rewriting messages to be kid-safe, using language models.

6. 38:00 🤖 Real-time voice interaction with large language models is promising, but data passing between systems needs improvement; coding may become obsolete as automation tools advance.

   6.1 You can combine operators to fetch data from a source, use it to answer questions, and integrate it into a website as a chat box with a natural and interactive voice interface.

   6.2 At a restaurant called Dr. Donut, a customer orders two pumpkin spice iced donuts and two caramel macchiatos, and casually asks about the speaker's thoughts on large language models.

   6.3 Real-time voice interaction with large language models is becoming more promising, but there is a need to streamline the process of passing data between different systems to improve performance.

   6.4 The field of computer science may eventually evolve to resemble the field of engineering, with the concept of humans writing code becoming obsolete as automation tools advance.

   6.5 Science and math are still important, but not everyone needs to understand them.

7. 44:14 🤖 Using AI in software development can make computing more accessible, but there are concerns about testing and ensuring code correctness, and it's important to consider safety and regulation while leveraging AI.

   7.1 The use of AI in software development can greatly expand access to computing for people without formal training in computer science, allowing them to easily automate tasks and utilize the power of computing.

   7.2 Language models are a powerful tool for computation, but their inner workings are not fully understood, and their potential to replace traditional programming is both exciting and relieving.

   7.3 We can potentially let robots do programming tasks while humans focus on other things, but there are still questions about how to test and ensure the correctness of code generated by AI models.

   7.4 People should start writing programs in languages other than C due to the potential dangers and risks associated with software mistakes, bugs, and crashes, and while leveraging AI, it is important to consider safety and regulation.

   7.5 Throwing more transistors at the problem will improve language models, but the challenge lies in obtaining enough transistors and in finding a way to reason about the capabilities of these models in a formal way, which may require a shift towards thinking about them in terms of social sciences rather than just machines.

   7.6 Can one language model explain another language model and provide understanding of its functions?

8. 55:20 🤖 Large language models have the potential to revolutionize software engineering, but their limitations and biases must be addressed through collaboration between humans and AI.

   8.1 The speaker discusses the potential limitations of scaling language models with more data and computation, particularly in regards to diminishing returns and the availability of data and transistors, but also mentions the possibility of addressing the transistor problem in the future.

   8.2 Language models like chat GPT have only accessed a small fraction of the vast amount of knowledge available in both digital and analog forms, primarily biased towards English-speaking internet users, and there is a significant untapped potential for these models to leverage non-public data to gain a deeper understanding and perform more tasks.

   8.3 The future of software engineering may involve language models that make engineers 10,000 times more productive, but they may still lack the human qualities and knowledge that make them unique.

   8.4 Humans need to be removed from the process of writing code in order to unlock the full potential of computation and overcome the limitations of programming languages and tools.

   8.5 The future of large language models lies in the symbiotic collaboration between humans and AI, where the AI model performs tasks it excels at while humans excel at expressing their needs and teaching the model effectively.

   8.6 The future of programming may involve AI programs as an abstraction, and while foundational knowledge is important, academic computer science education should also focus on understanding the mechanics and limitations of AI models to prevent students from viewing them as magical black boxes.