richdougherty

Joined 15 years ago

Karma

1,798

Submissions

404

Member since

Aug 13

https://rd.nz

View on Hacker News

Recent submissions

Comment · 1 month ago

You probably need a programming language more powerful than Rust that can encode and check the complicated invariants needed by the JIT.

There are classes of type system—"dependently-typed"—where you can encode arbitrary predicates into the type checking. I've remember seeing examples where they could prove arbitrary facts at the assembly level, including self-modifying code. Lean is such a language, currently very popular, but there are others.

Correctness of such programs depends on some basic assumptions about the CPU/memory environment, which should work absent hardware bugs like speculative execution or side channels. Although these could probably be encoded as well, and accounted for.

Without such a general proof language, you could probably make a special language to support JIT development, although its type system would be very complicated and the language itself could be a source of bugs.

Given such languages or proofs (maybe tractable now with AI-assisted theorem proving), you can actually make insanely performant code, since you don't really need to rely on other runtime protection so much (although maybe a good idea still).

Comment · 8 months ago

If you'll excuse the self-post, here's a blog post on goto with delimited continuations.

https://rd.nz/2009/03/goto-in-scala.html

It uses an experimental compiler plugin for the Scala compiler. It's typesafe at compile time. At runtime unfortunately it relies on exceptions for control flow.

Comment · 1 year ago

Seems likely because the article says "Meta’s deal with Google is mainly around artificial intelligence infrastructure, said one of the people".

Comment · 1 year ago

"The cost-effective nature of AI makes it highly plausible we're moving towards an agent vs agent future."

Sounds right. I assume we will all have AI agents triaging our emails trying to protect us.

Maybe we will need AI to help us discern what is really true when we search for or consume information as well. The amount and quality of plausible but fake information is only going to increase.

"However, the possibilities of jailbreaks and prompt injections pose a significant challenge to using language models to prevent phishing."

Gives a hint at the arms race between attack and defense.

Comment · 2 years ago

Kudos to the dev for coming up with the eye position fixing solution.

Building further on this idea, I wonder if instead of changing the image to look at the camera, we could change the "camera" to be where we're looking.

In other words we could simulate a virtual camera somewhere in the screen, perhaps over the eyes of the person talking.

We could simulate a virtual camera by using the image of the real camera (or cameras), constructing a 3D image of ourselves and re-rendering it from the virtual camera location.

I think this would be really cool. It would be like there was a camera in the centre of our screen. We could stop worrying about looking at the camera and look at the person talking.

Of course this is all very tricky, but does feel possible right now. I think the Apple Vision Pro might do something similar already?

Comment · 2 years ago

So it's also vulnerable to a Helicopter Injection Attack?

Comment · 2 years ago

If you have control of the tokenizer you could make sure it doesn't produce these tokens on user input. I.e. instead of the special "<eos>" token, produce something like "<", "eos", ">" - whatever the 'natural' encoding of that string is.

See for example, the llama3 tokenizer has options to control special token tokenization:

Tokenization method with args to control special token handling: https://github.com/meta-llama/llama3/blob/bf8d18cd087a4a0b3f...

And you can see how it is used combined with special tokens and user input here: https://github.com/meta-llama/llama3/blob/bf8d18cd087a4a0b3f...

If you don't have control of the tokenizer, I guess it needs to be sanitized in the input like you say.

Comment · 2 years ago

Source with more information: https://www.tuxedocomputers.com/en/TUXEDO-on-ARM-is-coming.t...

Comment · 2 years ago

A few details here: "Recall leverages your personal semantic index, built and stored entirely on your device. Your snapshots are yours; they stay locally on your PC. You can delete individual snapshots, adjust and delete ranges of time in Settings, or pause at any point right from the icon in the System Tray on your Taskbar. You can also filter apps and websites from ever being saved. You are always in control with privacy you can trust."

https://blogs.microsoft.com/blog/2024/05/20/introducing-copi...

Comment · 2 years ago

I can't track down the citation (either Google or DeepMind I think), but I remember reading research from a year or two ago how adding extra languages (French, German) improved English language performance. There may have also been an investigation about multi modality too, which found that adding vision or audio helped with text as well.

Comment · 2 years ago

> Chain-of-thought responses from language models improve performance across most benchmarks. However, it remains unclear to what extent these performance gains can be attributed to human-like task decomposition or simply the greater computation that additional tokens allow. We show that transformers can use meaningless filler tokens (e.g., '......') in place of a chain of thought to solve two hard algorithmic tasks they could not solve when responding without intermediate tokens. However, we find empirically that learning to use filler tokens is difficult and requires specific, dense supervision to converge. We also provide a theoretical characterization of the class of problems where filler tokens are useful in terms of the quantifier depth of a first-order formula. For problems satisfying this characterization, chain-of-thought tokens need not provide information about the intermediate computational steps involved in multi-token computations. In summary, our results show that additional tokens can provide computational benefits independent of token choice. The fact that intermediate tokens can act as filler tokens raises concerns about large language models engaging in unauditable, hidden computations that are increasingly detached from the observed chain-of-thought tokens.

> In this work, we demonstrate that transformers trained on the next-token prediction objective can achieve improved performance on certain tasks when given filler tokens, achieving perfect accuracy whereas the no-filler, immediate-answer setting achieves only low accuracy.

I wonder if we could get benefits from adding special computation/register tokens to text LLMs?

More discussion:

- https://news.ycombinator.com/item?id=40182695

- https://www.reddit.com/r/LocalLLaMA/comments/1cf2w5a/transfo...

Comment · 2 years ago

Anyone who enjoyed this, might also like "All the Horses of Iceland". It's a fictional work, but written by a medieval scholar. It's set in the 9th century where a Norse trader travels through an alternative central Asia.

http://sarahtolmie.ca/horsesReviews.html

Comment · 2 years ago

http://ymeskhout.substack.com/p/eleven-magic-words-unlocked

Comment · 2 years ago

Agree, nesting files with

    cat >Dockerfile <<'EOF'

and having a basic bash script seems way nicer than putting all the shell logic on the #! line.

Comment · 2 years ago

https://twitter.com/ylecun/status/1728126868342145481

"Please ignore the deluge of complete nonsense about Q. One of the main challenges to improve LLM reliability is to replace Auto-Regressive token prediction with planning.

Pretty much every top lab (FAIR, DeepMind, OpenAI etc) is working on that and some have already published ideas and results.

It is likely that Q is OpenAI attempts at planning. They pretty much hired Noam Brown (of Libratus/poker and Cicero/Diplomacy fame) to work on that.

[Note: I've been advocating for deep learning architecture capable of planning since 2016]."