Bingo

According to the BLS report the margin of error for the 130,000 jobs in Jan is +/- 127,000

Why would they do that? Taxiâ(TM)s operate on demand. If thereâ(TM)s so little demand in an area for such an extended period of time, the more likely scenario is for Waymo (or whatever robo taxi company) to either send the vehicle to a different location with more current demand, or back to the main hub and power down.

The more land we preserve along the waterways, the better. NJ is an OLD state, which was founded by people going up the waterways and working their way inward. Over time, we have learned that we need to give the rivers, marshes, and estuaries space to do their thang. If it means paying a little more in taxes, so be it. Money is not more important than the well-being of the planet.

You sound like the people in my /r/southjersey subreddit lol. My hometown is one of the most at risk towns of sinking into the water in the state. It's already an island (in southwest nj no less) and most of the land is preserved already. The buying up of the land and marshes really helps protect the land, animals, and people. Boohoo, taxes. Without paying them, you'd be living in a cesspool like Mississippi.

> But it is a purely esthetic difference.

Yeah, I think thatâ(TM)s the point, it looks nicer.

Oooh. I like this idea.

According to the article, the (Google, Meta, Amazon) have changed the life of a deprecating asset (Google and meta up to 6 years, from 4 and 5.5 respectively) AWS has bounced from 5 to 6 and then back to 5)

The neo-clouds are doing something different and appear to be taking out loans to purchase the gpuâ(TM)s and then using those assets (the gpuâ(TM)s) to back the loan. Kind of like how you or I could take a loan out on a car or mortgage on a house. Neo-cloud loans are for 3 years it appears and they are saying the assets are still good for a total of 6.

So basically Burry is saying he disagrees with both sets of companies over how long those assets will be of value.

But the article points out you have things like A100â(TM)s, released in 2020, which are still heavily utilized in inference workloads (just not training workloads) and were already going 5 years out, so maybe that 5-6 year window is justified)

It also appears to miss the point that the assets are just an input (like seed, fertilizer and water for crops) either the time rented for the gpuâ(TM)s and related infrastructure by cloud customers, or the creation of a trained model, or SaaS offering using the inference platform is the economic output.

Have you ever tried to instruct it to ask you for clarifying questions before providing an answer?

That report was about consumer grade GPUs.

Sure some 5900â(TM)s and 5800â(TM)s might be used in local inference tasks, but ai data centers are stocked with a a completely different set of GPU configurations that nothing in the article alludes to being included (H100â(TM)s or H200â(TM)s) for example.

Itâ(TM)s not the share holderâ(TM)s being sued (the owners) itâ(TM)s the officers. The law is clear in regards to what is required by them, and that if they fail to do that, and are found to have failed to do they, they can be held responsible.

They accept this responsibility and risk for the paychecks that they get.

If they follow the rules, and lose money they are fine.

I donâ(TM)t know all the details on the charges. But limited legal liability does not apply here. I do know that. If they are guilty or not in the eyes of existing laws, thatâ(TM)s for the court to determine.

How do I know, Iâ(TM)ve worked in the banking sector on and off for 20 years, and regularly recieved training about the things that if I fail to do, or in some cases do, that I might be legally liable with fines and up to jail.

As to the Boeing example, if any of those developers were Professional Engineers, they absolutely can be legally liable. Limited liability is a good thing, but does not apply in all cases is not a protect me from negligence, when my role at the company is specifically to not be lawfully negligent.

SVB was a C-Corp not an LLC (and Iâ(TM)m not aware of any U.S. bank that is or can be an llc).

The people being sued had a fiduciary duty to not do what happened, and were, or should have been, quite aware that they can be held Legally Liable if I court finds that they did not uphold the duty.

I think you need to do some more research.

Crap in, crap out. If you are using all of the Internet, you will get crap.

If you constrain the input of the rag model, and the questions asked, it will do a generally, to amazingly, great job.

Upload the original wizard of oz from Gutenberg to a rag system.

Then ask about Dorthyâ(TM)s ruby shoes.

Vs ask that question to a general llm that is not constrained by the actual story

Look up retrieval augmented generation.

Basically you add specific context to your system and it saves it off. That is then used to constrain what is generated.

Obviously if you provide crap context, youâ(TM)ll still get crap out put.

Notebooklm from Google is a good example of this that you can use, without needing to roll your own solution

Or you could always just pay for the service.

From all my training, I’m pretty sure that’s already covered by law. It’s called material information.

If you have that you can’t trade legally.