Program Synthesis and Large Language Models
The article argues that large language models like ChatGPT won't replace traditional programming, as generating correct code is complex and requires programming skills, despite LLMs assisting developers.
Read original article
The article discusses the limitations of large language models (LLMs) like ChatGPT in the context of program synthesis and the future of programming. While some experts predict that advancements in AI will render traditional programming obsolete, the author argues against this notion. The claim that most software will be AI-generated overlooks the complexity of software development, particularly for non-trivial applications such as operating systems and game engines. The challenges of generating correct program code from specifications are well-documented in computer science, with program synthesis being a computationally hard problem. The author emphasizes that natural language, including English, is semantically ambiguous and cannot simplify the synthesis process. Although LLMs can assist in generating code and may serve as useful tools for programmers, they do not replace the need for programming skills or the study of computer science. The article concludes that while LLMs can facilitate dialogue between developers and users, they do not eliminate the fundamental challenges of program correctness and synthesis.
- The notion that programming will become obsolete due to AI advancements is challenged.
- Generating correct program code from specifications is a complex and computationally hard problem.
- Natural language programming is limited by semantic ambiguity and does not simplify program synthesis.
- LLMs can assist programmers but do not replace the need for programming skills.
- The importance of program correctness remains a central issue in computer science.
Related
Introduction to Program Synthesis
Program synthesis automates software creation by generating programs from requirements. It leverages Large Language Models (LLMs) like co-pilot and AlphaCode, alongside search-based techniques, for tasks like data manipulation and legacy application modernization.
Up to 90% of my code is now generated by AI
A senior full-stack developer discusses the transformative impact of generative AI on programming, emphasizing the importance of creativity, continuous learning, and responsible integration of AI tools in coding practices.
The art of programming and why I won't use LLM
The author argues that the effectiveness of large language models in programming is overstated, emphasizing coding as a creative expression and expressing concern over the diminishing joy in programming due to automation.
Engineering over AI
The article emphasizes the importance of engineering in code generation with large language models, highlighting skepticism due to hype, the need for structural understanding of codebases, and a solid technical foundation.
Transcript for Yann LeCun: AGI and the Future of AI – Lex Fridman Podcast
Yann LeCun discusses the limitations of large language models, emphasizing their lack of real-world understanding and sensory data processing, while advocating for open-source AI development and expressing optimism about beneficial AGI.
10 commentsIf you’re hoping they will soon implement entirely novel complex systems based on a loose specification I think you’ll be disappointed.
Prof. Dawn Song IMO has been articulating a more productive view. LLMs are generating half the new code on github anyways, so lean in: use this as an opportunity to make it easy for new code to use formal methods where before it would have been to hard. Progress will happen either way, and at least this way we have a shot at bringing the verifiability in to more user code.
The difference is we don’t expect chatbots to be 100% right but we do expect program synthesis to be 100% right. For chatbots, 99% is amazing in terms of utility. That other 1% is really hard to get.
Given the limitations of English being able to robustly specify a program, thus requiring constraints for program synthesis to be formal descriptions of specifications, the author is committing a category error in comparing two incommensurable solutions to two distinct problems.
Ultimately not a convincing argument. We could use this same argument to argue that a human cannot write a program to spec. That may be strictly true, but not interesting.
Seems to me the car is the AI and the horse is the human.
Related
Introduction to Program Synthesis
Program synthesis automates software creation by generating programs from requirements. It leverages Large Language Models (LLMs) like co-pilot and AlphaCode, alongside search-based techniques, for tasks like data manipulation and legacy application modernization.
Up to 90% of my code is now generated by AI
A senior full-stack developer discusses the transformative impact of generative AI on programming, emphasizing the importance of creativity, continuous learning, and responsible integration of AI tools in coding practices.
The art of programming and why I won't use LLM
The author argues that the effectiveness of large language models in programming is overstated, emphasizing coding as a creative expression and expressing concern over the diminishing joy in programming due to automation.
Engineering over AI
The article emphasizes the importance of engineering in code generation with large language models, highlighting skepticism due to hype, the need for structural understanding of codebases, and a solid technical foundation.
Transcript for Yann LeCun: AGI and the Future of AI – Lex Fridman Podcast
Yann LeCun discusses the limitations of large language models, emphasizing their lack of real-world understanding and sensory data processing, while advocating for open-source AI development and expressing optimism about beneficial AGI.