DARPA suggests turning legacy C code automatically into Rust
www.theregister.com
Who wants to make a TRACTOR pull request?

To accelerate the transition to memory safe programming languages, the US Defense Advanced Research Projects Agency (DARPA) is driving the development of TRACTOR, a programmatic code conversion vehicle.

The term stands for TRanslating All C TO Rust. It’s a DARPA project that aims to develop machine-learning tools that can automate the conversion of legacy C code into Rust.

The reason to do so is memory safety. Memory safety bugs, such buffer overflows, account for the majority of major vulnerabilities in large codebases. And DARPA’s hope is that AI models can help with the programming language translation, in order to make software more secure.

“You can go to any of the LLM websites, start chatting with one of the AI chatbots, and all you need to say is ‘here’s some C code, please translate it to safe idiomatic Rust code,’ cut, paste, and something comes out, and it’s often very good, but not always,” said Dan Wallach, DARPA program manager for TRACTOR, in a statement.

  • mke@lemmy.world
    0·
    5 months ago

    You’re already making the assumption that “statistics around text” isn’t knowledge. That’s a very big assumption that you need to show.

    And you’re making the assumption that it could be. Why am I the only one who needs to show anything?

    I’m saying that LLMs fail at many basic tasks that any person which could commonly be said to have an understanding of them wouldn’t. You brought up the Turing test as though it was an actual, widely accepted scientific measure of understanding.

    Turing did not explicitly state that the Turing test could be used as a measure of “intelligence”, or any other human quality.

    Nevertheless, the Turing test has been proposed as a measure of a machine’s “ability to think” or its “intelligence”. This proposal has received criticism from both philosophers and computer scientists. […] Every element of this assumption has been questioned: the reliability of the interrogator’s judgement, the value of comparing the machine with a human, and the value of comparing only behaviour. Because of these and other considerations, some AI researchers have questioned the relevance of the test to their field.

    Source - Wikipedia.

    Sure but only if you are certain of the answer. As soon as you have a little uncertainty that breaks down.

    What do you mean, “certain of the answer?” It’s math. I apply knowledge, my understanding gained through study, to reason about and solve a problem. Ask me to solve it again, the rules don’t change; I’ll get the same answer. Again, what do you mean?

    Ask an LLM what Obama’s first name is a thousand times and it will give you the same answer.

    Apples to oranges. “What’s Obama’s first name” doesn’t require the same kind of skills as solving a math problem.

    Also, it took me 7 attempts to get ChatGPT to be confidently wrong about Obama’s name:

    It couldn’t even give me the same answer 7 times.

    Does my daughter not have any knowledge because she can’t do 12*2 reliably 1000 times in a row? Obviously not.

    That’s not my argument. If your daughter hasn’t learned multiplication yet, there’s no way she could guess the answer. Once has grown and learned it, though, I bet she’ll be able to answer that reliably. And I fully believe she’ll understand more about our world than any LLM. I hope you do so as well.

    it’ll just make things up

    Yes that is a big problem, but not related to this discussion.

    It’s absolutely related, because as I stated, LLMs have no concept of knowing. Even if there are humans that’ll lie, make things up, spread misinformation—sometimes even on purpose—at least there are also humans who won’t. People who’ll try to find the truth. People that will say, “Actually, I’m not sure. Why don’t we look into it together?”

    LLMs don’t do that, and they fundamentally can’t. Any insurmountable objection to answering questions is a guardrail put in place by their developers, and researchers are already looking into how to subvert those.

    I worked with a guy who was very like an LLM. Always an answer but complete bullshit half the time.

    Sorry to hear that. From experience, I know they can cause a lot of damage, even unintentionally.

    That’s not the same as understanding what those tokens actually mean

    Prove it. I assert that it is the same.

    Very confident assertion, there. Can I ask where’s your proof?

    I see that you also neglected to answer a critical part of my comment, so I’ll just copy and paste it here.

    At this point, I don’t think it’s unreasonable to say that insisting LLMs understand anything is a discussion more related to the meaning of words than to current AI capabilities. In fact, since understanding is more closely associated with knowledge that you can reason with and about, the continuous use of this word in these discussions can actually be harmful by misleading people who don’t know better.

    Any opinion on this?