sansseriff

It sure is! But ironically, because of the intention behind the obfuscation. Not the fact that AI was used in a research paper.

I have no issues with AI use in science. If claude can explain my research better than me, then have at it. But I do NOT want to read a passage thinking it was written by a human when it wasn't. Science has no idea yet how such disclosures should work yet. What should be done by humans as a matter of principle, and what can't be or should not be done by humans.

Glad to see there's a REV 3 in progress that would support ethernet. That's the one thing that would make me go out of my way to build one of these.

When I read "The honest truth: ...", the AI-generated alarm bells go off in my head. Whether the article is human written or not.

I don’t see why this would be the case with proper tool calling and context management. If you tell a model with blank context ‘you are an extremely rigorous reviewer searching for fake citations in a possibly compromised text’ then it will find errors.

It’s this weird situation where getting agents to act against other agents is more effective than trying to convince a working agent that it’s made a mistake. Perhaps because these things model the cognitive dissonance and stubbornness of humans?

I admit it has dystopian elements. It’s worth deciding what specifically is scary though. The potential fallibility or mistakes of the models? Check back in a few months. The fact they’re run by giant corps which will steal and train on your data? Then run local models. Their potential to incorporate bias or persuade via misalignment with the reader’s goals? Trickier to resolve, but various labs and nonprofits are working on it.

In some ways I’m scared too. But that’s the way things are going because younger people far prefer the interface of chat and question answering to flipping through a textbook.

Even if AI makes more mistakes or is more misaligned with the reader’s intentions than a random human reviewer (which is debatable in certain fields since the latest models game out), the behavior of young people requires us to improve the reputability of these systems. (Make sure they use citations, make sure they don’t hallucinate, etc). I think the technology is so much more user friendly that fixing the engineering bugs will be easier than forcing new generations to use the older systems.

Seriously. More people need to wake up to this. Older generations can keep arguing over display formats if they want. Meanwhile younger undergrad and grad students are getting more and more accustomed to LLMs forming the front end for any knowledge they consume. Why would research papers be any different.

I remember 15 years ago when I was in highschool I really wanted to learn how to program 8 bit microcontrollers without Arduino. And everybody looked at me like I was crazy. There was barely any learning material out there about how to do this.

Now, I imagine the bias pushing everyone to learn on arduino is even more intense? Who out there is programming these chips in pure C using open source compilers and bootloaders?

Edit: Of course there's other platforms like Esp32; teensy; seed. But I've only programmed Esp32s using the arduino dev environment. Are there other good ways of doing it?

There's a great value proposition for a company like Private Internet Access or NordVPN to create an AI browser extension or full-on browser. Anonymize requests and provide various LLM models. Rely on your reputation as a privacy focused corp to pull people away from these OpenAI/Perplexity offerings.

I haven't used LLM chrome plugins because I couldn't trust that they weren't collecting more information about my browsing than I'd like. The same concern exists for this, though now I'm just confident it's a giant software company with access to my data rather than some shady plugin developer. I'm faced with asking myself if that's actually better...

I remember listening to a podcast where Grant Sanderson basically said the opposite. He tried generating manim code with LLMs and found the results unimpressive. Probably just goes to show that competence in manim looks very different to us layman than it does to Grant haha

There's a weird insecurity I've noticed cropping up. I want to design the codebase 'my way'. I want to decide on the fundamental data structures. But there's this worry that my preferred architecture is not massively better than whatever the machine comes up with. So by insisting on 'my way' I'm robbing everyone productivity.

I know most true programmers will vouch for me and my need to understand. But clients and project managers and bosses? Are they really gonna keep accepting a refrain like this from their engineers?

"either it gets done in a day and I understand none of it, or it gets done in a month and I fully understand it and like it"

I wonder if there's a way to do diffusion within some sort of schema-defined or type constrained space.

A lot of people these days are asking for structured output from LLMs so that a schema is followed. Even if you train on schema-following with a transformer, you're still just 'hoping' in the end that the generated json matches the schema.

I'm not a diffusion excerpt, but maybe there's a way to diffuse one value in the 'space' of numbers, and another value in the 'space' of all strings, as required by a schema:

{ "type": "object", "properties": { "amount": { "type": "number" }, "description": { "type": "string" } }, "required": ["amount", "description"] }

I'm not sure how far this could lead. Could you diffuse more complex schemas that generalize to a arbitrary syntax tree? E.g. diffuse some code in a programming language that is guaranteed to be type-safe?