Will ChatGPT replace programmers? | Chris Lattner and Lex Fridman

Summary

The speaker discusses the impact of large language models (LLMs) on programming, noting their ability to generate code that raises questions about the uniqueness of human thought and creativity. They highlight LLMs' proficiency in replicating common coding tasks but emphasize the importance of human collaboration and understanding of complex problems in innovation. The speaker is optimistic about LLMs as tools for automation and productivity, suggesting their potential in assisting with coding and even within compilers, while acknowledging the challenges of integrating them into existing systems.

Takeaways

• 🧠 Large language models (LLMs) are increasingly capable of generating code, raising questions about the uniqueness of human thought and creativity in programming.
• 🤖 LLMs can predict and generate code similar to what a programmer is about to write, which can make one wonder about the extent of human ingenuity in coding.
• 📈 The ability of LLMs to generate code is based on their extensive training on existing codebases available on the internet.
• 🔄 LLMs are good at solving standard coding problems because these problems are common and have been solved many times before.
• 🛠 Building innovative solutions involves more than just coding; it requires understanding the problem, working with people, and considering user needs.
• 🤝 LLMs are seen as tools to help automate repetitive tasks, allowing developers to focus on more complex and creative aspects of problem-solving.
• 🔧 LLMs can be a valuable companion in the coding process, helping to increase productivity but not replacing the need for human coders.
• 🌐 The discussion about LLMs generating code for new programming languages like Mojo highlights the potential for LLMs to learn and adapt to new languages.
• 🔑 LLMs do not require programming languages to be designed for them; they can learn and generate code in languages that are not machine-oriented.
• 🛑 While LLMs can help with coding, they may introduce uncertainties and errors, especially when the code needs to be precise and error-free for production.
• 🔮 There is potential for LLMs to be integrated into compilers or used in creative brainstorming, but the challenge remains in expressing clear intent for the machine to follow accurately.

Q & A

How are large language models impacting the nature of programming and thought?

Large language models are generating code so well that they raise questions about the uniqueness of human thought and the source of valuable ideas in programming. They can predict code that a programmer is about to write, challenging the notion of individual ingenuity and innovation in coding.

What is the role of large language models in programming synthesis?

Large language models are assisting in programming synthesis by automating the mechanical aspects of coding, such as generating boilerplate code and standard solutions to common programming problems.

How do large language models handle the task of learning from mistakes in programming?

While the script does not directly address learning from mistakes, it suggests that large language models can generalize from instances found on the internet, which implies they could potentially learn from common mistakes made by programmers.

What is the significance of 'standing on the shoulders of giants' in the context of programming with large language models?

The phrase 'standing on the shoulders of giants' is used to illustrate how large language models can help programmers by providing them with access to a vast amount of existing knowledge and code, thus accelerating the development process.

How do large language models assist in building applied solutions to problems?

Large language models can aid in building applied solutions by automating routine coding tasks, allowing programmers to focus more on understanding the problem, working with people, and defining the product and its use cases.

What is the potential of large language models in the development of new programming languages?

The script suggests that large language models could potentially be trained on the syntax and semantics of new programming languages, helping to generate code in those languages and even possibly assisting in their design.

How do large language models deal with the challenge of compilers' strict requirements?

Large language models can help address the strict requirements of compilers by providing predictive coding assistance, ensuring that code adheres to the necessary syntax and structure before it is submitted for compilation.

What is the future potential of integrating large language models within compilers?

While the integration of large language models into compilers is possible, it may currently be impractical due to the high computational costs associated with running large language models. However, on-device models and other technological advancements could make this integration more feasible in the future.

How can large language models tap into the creative potential of 'hallucinations' in programming?

The 'hallucinations' of large language models refer to their ability to generate novel and creative outputs. While this can be beneficial for brainstorming and creative writing, it may not always be desirable in programming where correctness is paramount.

What are the implications of large language models for the future of coding and software development?

Large language models are likely to continue to enhance productivity in software development by automating routine tasks and assisting with code generation. However, they are seen as a companion to human programmers rather than a replacement, with the potential to handle more complex reasoning and proof-based systems in the future.

How can large language models assist in expressing a programmer's intent in coding?

Large language models can help express a programmer's intent by generating specifications or documentation that reflect the desired outcomes. This could be complemented by other systems that implement the actual code, ensuring that the final product aligns with the programmer's vision.

Outlines

00:00

🤖 Impact of Large Language Models on Coding

The speaker discusses the impressive capabilities of large language models (LLMs) in generating code, which raises questions about the uniqueness of human thought and creativity in programming. They ponder the value of human ingenuity when LLMs can predict code so well, and whether traditional programming wisdom like 'standing on the shoulders of giants' still applies. The speaker also considers the role of LLMs in automating routine coding tasks, allowing humans to focus on more complex and creative aspects of software development.

05:04

🛠 The Future of LLMs in Programming and Compilers

This paragraph delves into the potential integration of LLMs within compilers and the challenges of doing so, given the current computational expense of LLMs. The speaker acknowledges the creative potential of LLMs in tasks like brainstorming and writing, but also recognizes the need for accuracy in production code. They suggest that future work could involve building more reliable systems, possibly with separate nets for specification and implementation. The discussion hints at the possibility of LLMs providing specifications while other systems handle the actual coding, emphasizing the distinction between inspiration and implementation.

Keywords

💡Large Language Models (LLMs)

Large Language Models (LLMs) refer to advanced artificial intelligence systems designed to process and generate human-like text based on vast amounts of data. In the context of the video, LLMs are highlighted for their ability to generate code, which raises questions about the uniqueness of human thought and creativity in programming. The script discusses the impact of LLMs on programming synthesis and their potential to automate and assist in coding tasks, thus changing the nature of software development.

💡Program Synthesis

Program Synthesis is the process of automatically generating code based on a set of specifications or examples. The video script mentions that LLMs are adept at program synthesis, particularly in generating code for common programming tasks. This ability challenges traditional notions of programming and raises the question of the role of human ingenuity in code creation.

💡Innovation

Innovation in the script refers to the creation of new ideas, methods, or products. The discussion revolves around the contribution of human ingenuity to innovation in coding and whether the use of LLMs diminishes the uniqueness of human input. It is implied that while LLMs can generate code, the innovative application of this code in novel contexts may still require human insight.

💡Standing on the Shoulders of Giants

This idiom, used in the script, means building upon the work of others to achieve greater results. It is applied to the use of LLMs in programming, suggesting that these tools allow developers to leverage existing knowledge and code more efficiently, potentially leading to faster advancements in technology.

💡Mistakes and Learning

The script touches on the idea that learning from mistakes is an important part of the programming process. LLMs can help by providing examples of common errors and how to avoid them, thus accelerating the learning process for developers and contributing to the overall improvement of code quality.

💡Product Development

Product Development in the video is about creating a product that meets user needs and solves a problem. It is mentioned in the context of understanding the problem and building an applied solution. The script suggests that while LLMs can assist in coding, the human role in product development involves understanding user needs and translating them into viable products.

💡Delegation

Delegation in this context refers to the act of assigning tasks to others or, in the case of LLMs, to an AI system. The script discusses the value of delegating routine coding tasks to LLMs to free up developers to focus on more complex and creative aspects of software development, thereby increasing productivity.

💡Mojo

Mojo, as used in the script, seems to refer to a specific project or programming language being discussed. It is mentioned in the context of LLMs generating code for new programming languages, suggesting that AI could be trained to understand and produce code for novel languages, aiding in their adoption and development.

💡Intermediate Representation

An intermediate representation in programming is a data structure used to represent code in a form that is easier for a compiler or interpreter to process. The script notes that programming languages serve as an intermediate representation between humans and machines, and LLMs are expected to learn and generate code in these representations effectively.

💡Compiler

A compiler is a program that translates code written in one programming language into another language, typically machine code. The script suggests that LLMs could potentially be integrated into compilers to assist with tasks such as syntax checking and code optimization, although this would be computationally expensive.

💡Creative Potential

The term creative potential in the script refers to the ability of LLMs to generate novel and imaginative content. It is noted that while LLMs can produce 'hallucinations' or unexpected outputs that can be beneficial in creative tasks, this same characteristic may not be desirable when generating production-ready code that requires precision and correctness.

💡Algebraic Reasoning Systems

Algebraic Reasoning Systems are computational tools designed to perform logical reasoning and problem-solving tasks, often used in mathematics and computer science. The script hints at the potential development of such systems to enhance the reliability of LLMs in generating correct and logical code, suggesting a future where AI can provide more accurate and reliable programming assistance.

Highlights

Large language models are now capable of generating code effectively, raising questions about the uniqueness of human thought and the nature of programming.

Language models can predict code that a programmer is about to write, which challenges the perception of human ingenuity in coding and design.

The concept of 'standing on the shoulders of giants' is redefined with the assistance of AI, which helps in avoiding common mistakes and learning from them.

LLMs are adept at solving common coding problems due to the abundance of such instances on the internet.

The speaker sees LLMs as a valuable tool for automating mechanical aspects of programming, allowing for greater productivity and scalability.

Building applied solutions involves understanding the problem, working with people, and considering the product and its use cases.

Customers may not always know what they need, highlighting the importance of innovation beyond just fulfilling stated desires.

LLMs are not seen as competitors but rather as companions that can help in the coding process, enhancing human capabilities.

The potential for LLMs to generate code for new programming languages, such as Mojo, is discussed as a way to learn and understand language usage.

LLMs can learn any programming language, regardless of whether it was designed for machines, due to their adaptability.

The speaker suggests that LLMs could help solve issues with compilers by providing more flexible and adaptable code generation.

Predictive coding and AI copilot features are seen as positive developments that will increase productivity in coding.

The integration of LLMs within compilers is considered a possibility, although it may be expensive and require further development.

LLMs are praised for their creative potential, especially when used in brainstorming and creative writing where their 'hallucinations' can be beneficial.

The need for reliable and scalable systems is highlighted, suggesting that LLMs could be part of the solution for more robust coding practices.

The challenge of expressing human intent to machines is discussed, with the idea that LLMs could provide specifications while other systems implement the code.

The importance of documentation and inspiration in coding is emphasized, distinguishing between AI-generated ideas and their actual implementation.

Transcripts

I have to ask you about the

one of the interesting developments with

large language models

is that they're able to generate code uh

recently really well

I guess to a degree that maybe a

I don't know if you understand but I

have I struggle to understand because it

it forces me to ask questions about the

nature of programming of the nature of

thought

because the uh language models are able

to predict the kind of code I was about

to write so well yep that it makes me

wonder like how unique my brain is and

where the valuable ideas actually come

from like how much do I contribute in

terms of uh Ingenuity Innovation to code

I write or design and that kind of stuff

um when you stand on the shoulders of

giants are you really doing anything and

what L alums are helping you do is they

help you stand on the shoulders of

generality program there's mistakes

they're interesting that you learn from

but I just it would love to get your

opinion first high level yeah of what

you think about this impact of large

language models when they do program

synthesis when they generate code yeah

also

um

I don't know where it all goes yeah

um I'm an optimist and I'm a human

Optimist right I think that

um things I've seen are that a lot of

the llms are really good at crushing

leak code projects and they can reverse

the link list like crazy

um well it turns out there's a lot of

instances of that on the internet and

it's a pretty stock thing and so if you

want to see

standard questions answered LMS can

memorize all the answers and that can be

amazing and also they do generalize out

from that and so there's good work on

that but um but I think that if in my

experience building things building

something like you talk about Mojo where

you talk about these things where you

talk about building an applied solution

to a problem it's also about working

with people

it's about understanding the problem

what is the product that you want to

build what are the use case what are the

customers you can't just go survey all

the customers because they'll tell you

that they want a faster horse maybe they

need a car right and so a lot of it

comes into

um you know I don't feel like we have to

compete with L alums I think they'll

help automate a ton of the mechanical

stuff out of the way and just like you

know I think we all try to scale through

delegation and things like this

delegating wrote things to an llm I

think is an extremely valuable and the

approach that will help us all scale and

be more productive but I think it's a

it's a fascinating companion but I'd say

I don't think that means that we're

going to be done with coding

but there's power in it as a companion

and yeah so from there I could I would

love to zoom in onto Mojo a little bit

do you think uh do you think about that

do you think about llms generating Mojo

code

uh and helping sort of like when you

design new programming language it

almost seems like man it would be nice

to sort of

um

almost as a way to learn how I'm

supposed to use this thing

for them to be trained on some of the

most good so I do lead an AI company so

maybe there will be a Mojo llm at some

point uh but if your question is like

how do we make a language to be suitable

for llms yeah I think that the

um

I think the cool thing about LMS is you

don't have to right and so if you look

at what is English or any of these other

terrible languages that we as humans

deal with on a continuous basis they're

never designed for machines and yet

they're the intermediate representation

they're The Exchange format that we

humans use to get stuff done right and

so these programming languages they're

an intermediate representation between

the human and the computer or the human

and the compiler roughly right and so I

think the LMS will have no problem

learning whatever keyword we pick maybe

the Phi Emoji is gonna oh maybe that's

gonna break it it doesn't tokenize no

the reverse of that it will actually

enable it because one of the issues I

could see with being a super set of

python is there would be confusion about

the gray area

so it'll be mixing stuff uh

but well I'm a human Optimist I'm also

an llm optimist I think that we'll solve

that problem but

but you look at that and you say okay

well

reducing the rote thing right turns out

compilers are very particular and they

really want things they really want the

indentation to be right they really want

the colon to be there on your else or

else it'll complain right I mean

compilers can do better at this but um

lens can totally help solve that problem

and so I'm very happy about the new uh

predictive coding and copilot type

features and things like this because I

think it'll all just make us more

productive it's still messy and fuzzy

and uncertain unpredictable so but is

there a future you see given how big of

a leap gpt4 was where you start to see

something like llms inside a compiler

uh I mean you could do that yeah

absolutely I mean I think that would be

interesting it's not wise well well I

mean it would be very expensive so

compilers run fast and they're very

efficient and LMS are currently very

expensive there's on device llms and

there's other things going on and so

maybe there's an answer there

um I think that one of the things that I

haven't seen enough of is that

so llms to me are amazing when you tap

into the creative potential of the

hallucinations right and so if you're

building doing creative brainstorming or

creative writing or things like that the

hallucinations work in your favor

um

if your writing code that has to be

correct because you're going to ship it

in production then maybe that's not

actually a feature

and so I think that there there has been

research and there has been work on

building algebraic reasoning systems and

kind of like figuring out more things

that feel like proofs and so I think

that there could be interesting work in

terms of building more reliable scale

systems and that could be interesting

but if you chase that rabbit hole down

the question then becomes how do you

express your intent of the machine

and so maybe you want LM to provide the

spec but you have a different kind of

net that then actually implements the

code right so it's a used documentation

and and inspiration versus the actual

implementation yeah potentially