Will ChatGPT replace programmers? | Chris Lattner and Lex Fridman
Summary
TLDRThe 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
🤖 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.
🛠 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.
Mindmap
Keywords
💡Large Language Models (LLMs)
💡Program Synthesis
💡Innovation
💡Standing on the Shoulders of Giants
💡Mistakes and Learning
💡Product Development
💡Delegation
💡Mojo
💡Intermediate Representation
💡Compiler
💡Creative Potential
💡Algebraic Reasoning Systems
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
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