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Comment Re:I'm surprised it's still 50%+ (Score 1) 99

Inertia. Took me several years once streaming had taken off and we pretty much exclusively used our Roku and never used our Dish Network box to persuade the rest of my family that the $60-70/mo we were paying for Dish was a waste of money.

I also suspect a fair number have it for the same reason as their landline, as a reliable back up in case of emergencies. I had to demonstrate our antenna was fine for getting local news stations multiple times to deal with this argument.

Comment Re:AI/LLMs and language translation (Score 1) 66

I wonder how TIOBE would measure this sort of work. As activity in the source language (C)? Editing language (C#)? Or both?

It wouldn't. TIOBE is bullshit, I don't know why anyone uses it. Look at what it is: https://www.tiobe.com/tiobe-in...

It's just searching various engines for "$LANG programming" and applying magic fudge factors. It searches multiple languages versions of Google as well as for some reason Amazon and Ebay. And it relies on the "$NUM results found" provided by those sites.

So at best it's a vague indicator of the language's presence. It doesn't say much of anything about whether it's in use. If a popular documentation site goes down it will note a decrease, and it's trivial to cheat by encouraging the insertion of keywords in websites.

Comment Re:media (Score 2) 42

"Secret trick destroys AI" is bullshit. What is not bullshit is that for less common tokens, the conditional distributions of their occurrence in language depend on a relatively small number of examples. This is not an LLM property, it's a property of the language data itself. Also known as the hapax problem. Any language generator, including LLMs, is constrained by this fact. It has nothing to do with the architecture.

In practical terms, this means if you have a learning machine that tries to predict a less common token from some context (either directly like an LLM, or as an explicit intermediate step), then the local output will be strongly affected if it sees a single new context in the training data, such as when someone is poisoning a topic.

There are no solutions for this in the current ML learning paradigm. The system designers can make the system less sensitive to the tokens in the training data, but this comes at a price of being less relevant, due to deliberately discounting newly encountered facts against a implicit or explicit prior. Your example falls in this category.

It is fundamentally impossible to recognise the truth and value of a newly encountered datum without using a semantic model of reality. Statistical language models do not do this.

Comment Re:It's not Lupus (Score 4, Interesting) 49

That was probably the right thing to do. It's called differential diagnosis, and it doesn't mean that the doctors didn't suspect Lupus from the start. They were being careful to rule out alternatives in order of priority.

If your Word document that you're writing has a grammatical error, it could be caused by many things, from a typo to bad autocompletion to hackers messing with your desktop or bad choice of language dictionary packs etc. You don't start the first treatment by rebooting the computer and replacing the whole MS Office suite. You first try a bunch of less invasive things.

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