dweinus

To improve life, liberty, and the pursuit of happiness for its citizens.

It also avoids a lot of waste/pollution/cost. That might sound trivial, but over the course of 17 years an average person might own 5 laptops.

Another fascist takes his mask off

I knew a bunch of them. Most of them moved on: retired or started over in new careers. It hit them hard. Maybe not so hard because trading was a lucrative career, but most of us don't have that kind of dough to fall back on.

Oh good! I've got all these car batteries to dispose of, maybe it can help me figure out where to put them.

I shudder to think what the USA's would be, but strong chance it involves Florida Man.

TIL that in 2024 people are still trying to claim that trickle-down economics works. Given all the wealth at the top these days, I'll expect my trickle-down check in the mail any day now.

> "Let's consider a very simple distributed system. Our system is composed of two servers, [Math Processing Error] and [Math Processing Error]."

Yup. Pretty sure I've built that system before. Can relate.

It is a special building. Like most of his work: charming, unique, and completely impractical. Keeping it maintained out where it is was always going to be hard, but it looks like they've been had. It's a shame.

A site run by a fascist transphobe? Hard pass.

> Now the Web at large is full of slop generated by large language models, written by no one to communicate nothing.

Fair and accurate. In the best cases the person running the model didn't write this stuff and word salad doesn't communicate whatever they meant to say. In many cases though, content is simply pumped out for SEO with no intention of being valuable to anyone.

I'm not anti-union at all, but I do think there is a very common experience that sours people against them early in their career: that of a mandatory union that docks your already meager pay without any obvious benefits. It of course overlooks the things you enjoy as an employee that the union won and fights to retain (breaks, safety standards, etc). I think anyone who wants unions to succeed needs to think about solving that problem, because unions only succeed on collective action.

They take down the myth, but in this whole long article I didn't see any attempts to get quotes/perspective from actual indigenous people. Why?

Citation very much needed. For each person living rurally or suburbanly, the per-capita footprint is bigger in terms of land, roads, building materials, facilities, shipping, HVAC, and transportation. The supply problem you mention gets worse in rural environments, not better, because you need to distribute goods across large areas. Here's some data too: https://theconversation.com/suburban-living-the-worst-for-ca...

100% I think the author is really misunderstanding the issue here. "Hallucination" is a fundamental aspect of the design of Large Language Models. Narrowing the distribution of the training data will reduce the LLM's ability to generalize, but it won't stop hallucinations.

"This single parameter model provides a large improvement over the prior state of the art in fitting an elephant"

Lol

So many articles on this paper, none link to it: https://bmjpublichealth.bmj.com/content/2/1/e001000

In case you want other mental health tips from the study: "Individuals who abstained from alcohol showed lower cognitive scores than those who consumed alcohol"

...or RAG!

If I understand the paper right...

At indexing time:

- run LLM over every data point multiple times ("gleanings") for entity extraction and constructing a graph index

- run an LLM over the graph multiple times to create clusters ("communities")

At query time:

- Run the LLM across all clusters, creating an answer from each and score them

- Run the LLM across all but the lowest scoring answers to produce a "global answer"

...aren't the compute requirements here untenable for any decent sized dataset?

...don't forget about Planetary Annihilation! Not free or open source, but still a lot of fun! https://planetaryannihilation.com/

If 80% of employees stay each year, then the expected tenure would be about 3 years.

Genuinely curious: do any of the features/use cases they market here sound appealing to people? I am struggling to imagine wanting to use any of them. I can, however, easily imagine the sound my cooling fan will make while the NPU is running 24/7.

100% accurate. Impressive

This.

"Since 2014, it has raised its menu prices by 100%"

... during the same time period, US inflation has been 32%.

Carbon capture from the air is at best a tiny fraction of the solutions we need. Right now our time is better spent on emissions reduction.

https://www.nature.com/articles/d41586-023-00953-x

Cool, but carbon capture from the air is at best a tiny fraction of the solutions we need. Right now our time is better spent on emissions reduction.

https://www.nature.com/articles/d41586-023-00953-x

Thinking out loud... I don't think these problems can be solved. If you are going to do it anyway, I would suggest:

- Using a RAG architecture on top of a database of factual information. Wikipedia is probably your best bet. It is not 100% factual or correct either, but maybe as good as it gets. Scaling RAG to wikipedia size is not trivial, but I think it can be done.

- Prompting the LLM to cite its sources so people can fact-check the fact-checker

- Prompting the LLM to say it is unsure when something does not have a clear answer. I don't expect this to be reliable, but maybe somewhat better

Andrew, you and your friends should be proud. It's really encouraging to see people, especially in your generation, thinking seriously about the problem of misinformation. There are fundamental challenges you all will face in this idea:

- most current LLMs are trained on large amounts of web data that itself contains facts, opinions, and misinformation. These things are treated equally, so I would expect the LLM to get common facts right, but also to represent opinions or misinformation as facts when they are pervasive.

- LLMs "hallucinate" and tend not to know when to say "I don't know" or to not try to fact-check something that is not factual in nature.

...in short, I would expect LLMs to be an unreliable fact checker, which has the potential to do as much harm as good.

This paper is bad and Gizmodo should feel bad.

Besides making ridiculous and unsupported causal claims (as other commenters have noted), the results are really poor (R score of 0.2). They show that the model achieves human-level performance, which is also really poor. Then they run it on natural images of politicians and the results are significantly worse. What the paper actually shows is that facial appearance can not consistently predict political alignment, but the authors want to try really hard to do it anyway.

Yay, the planet is saved! /sarcasm