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Registered on:11/21/2008
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quote:

Would you expect a firm in this stage of buildout to make a profit?


No but I was responding to the poster that says they are making a profit
quote:

Anthropic begs to differ


Even the article you posted refutes what you just said

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before accounting for revenue shared with distribution partners, including Amazon (AMZN), and the cost of training its model


Ah yes if we ignore our largest cost we made money!!

So no they are not making a profit

You are technically correct.

VFIAX and VTSAX if you must use mutual funds, as they have identical structure as voo and vti. The ETFs have slightly lower fees
VOO and VTI

Pretty much all you need
quote:

Your entire financial worth is just an electronic record. That would seem like a high value target for bad actors/rogue AI.


We have financial records and backups across multiple institutions including air gapped backups. The govt also insured your holdings.

If you are gambling in crypto though you are correct, you are screwed. But that's by design, because it was designed for fraud from the beginning
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The point I’m making is it’s not data center noise, it’s construction noise. That will end once center is built.


Space X already disclosed the noise is coming from 60 gas powered turbines at the site.
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Google and to a lesser extent, Apple, are too big to just be tertiary players in the most transformational technology of our lives


Google doesn't need to say anything because unlike the other AI players they are one of the few that has the dominant position.

The path to profitability with LLMs is though inserting ads through the responses. They are one of the few that is actually profiting.

Open AI is moving to ads quickly
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A private entity could do their job more efficiently and probably for 1/4 of the cost…


The first thing a private entity would do is cut unprofitable routes.

The USPS can't do that, and no profit company would deliver to everyone.

That's why it's a government service
Another year of record highs.. Looks like lately it's always October before any decent temps
I've tried chunks as well. However since every model has no concept of pages on PDFs it becomes a guessing game in what is contained in a chunk.

You end up searching the file yourself for the information, rather than getting false information.

The worst part though is even in small files they still hallucinate horribly.

Gemini Enterprise is the worst. Even giving it small documents and telling it to prove what it states with documentation. It confidently states wrong locations with fake information.

You can't point it to page numbers, since it has no concept of pages.

Codex at least say it can't process the file these days with the latest model, as it seems like they have given up on it lately. It will churn through almost a million tokens to give you no answer lol.

At this point we are trying different file formats. But every manufacturer on the planet puts their documentation in PDF. So unless it's something like a markdown file, I have little hope it can read anything remotely sophisticated.

Claude actually did better if you asked if for information instead of providing the source. But also once you try to tell it to point you to the reference to double check it generally has no way to consistently cite it's sources.

Some models definitely gotten worse over time.

LLMs are pattern matching to a fault, they just suck at folding human made documentation
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depends on the type, contents, and legibility of the PDF. If you have text based PDFs, then the process theoretically should be trivial and nearly 100% accurate. It sounds like you may have the other type of PDFs though. Those PDFs appear contain text, but the contents of the PDF are actually images and not text. If that is the case, then the PDF must go through an OCR process that is CPU intensive and somewhat error prone even if the PDFs appear to be pretty legible.


None of them can read text PDFs. Try it man, large PDFs like data sheets. Anything with text tables you might as well assume it is going to make everything up

We have had so many errors from so many models a lot of our guys are losing any confidence in it.

Literally none of them can read any decently formatted pdf that is more than a few pages.

I know for home use 85-90% might sound great.. For our work we are almost to the point to stop using it because it just inserts randomly wrong data constantly.
This is just another Monaco... Unfortunate
I can't share what we use it for, but for the life of me I can't find one decent model that can read large PDFs well at all.

Most just make up shite as it goes, I know generally it's breaking the file down and reading the internal data so page numbers basically are meaningless. But none of the frontier models can read them without making up shite.

Would be a massive time saver, but in reality it's a time sink because we can't trust any of them reading PDFs we have found.

Tried Claude, chatgpt, Gemini, and grok. Chatgpt might be the best... Hard to say. At best it's right about 85-90% of the time of the information it ingests. Which is exceptionally bad. We are looking at converting PDFs to something more ingestable, but that costs a lot of time and money the second the models change
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How do all the companies develop at essentially the same pace? It doesn’t make sense


They don't? Open AI and anthropic LLMs are far ahead of the competition. Google is probably next. Then the open models are behind them.

However the open models are catching up quickly. I am of the form believe that a completely different approach from the current approach to LLMs will need to occur to reach any sort of AGI.

I think the public's definition of AGI will not match what these companies will state is AGI as they push for more investment.

In the second half of 26 right now we are in the efficiency and monetization transition phase.. That is why there is so much fear mongering as anthropic nears IPO. Fear drives investment
OpenAI financials are an absolute disaster everyone knows it. They have no real path to profitability right now as models are switching to efficiency gains.

Open models are catching up fast and do well enough to avoid the massive costs of frontier models which are not improving enough to justify their massive cost increase.

Users aren't paying for AI, and businesses that do pay prefer anthropic.

Anthropic isn't making money yet either. As they approach IPO they will ratchet up the stories of AI dangers as they have shown it drives fear and investments. Even though it's clear Dario just wings it for the last 5 years.
2 TE and still can't run for the first
Need to give KR an easy throw on first...
Right when I complained about pressure they prove me wrong