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.

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.

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?

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

> 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...

http://sarahtolmie.ca/horsesReviews.html

    cat >Dockerfile <<'EOF'

"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]."

- "The JVM has been modified to be aware that it is running in a Docker container and will extract container specific configuration information instead of querying the operating system. The information being extracted is the number of CPUs and total memory that have been allocated to the container." https://www.oracle.com/java/technologies/javase/8u191-relnot...

- Here's a more detailed explanation and even a shared library that can be used to patch container unaware versions of Java. I wonder if the same could be done for Go?

"LD_PRELOAD=/path/to/libproccount.so java "

https://stackoverflow.com/a/64271429

https://gist.github.com/apangin/78d7e6f7402b1a5da0fa3abd9381...

-

There are more recent changes to Java container awareness as well:

https://developers.redhat.com/articles/2022/04/19/java-17-wh...

An analogy would be if you make a browser, to use standard HTML and HTTP, not try to invent your own thing.