That way, when the product burns down someone's home, the seller can just punch their keyboard again and continue doing business under the new name.

The headline and summary make it sound as if this functionality was built into Windows itself.

It's not. It's malware.

The only people affected are people who installed Windows and THEN installed the malware. Probably as part of a phishing campaign, or by downloading something that pretended to be legitimate software.

Sorry about the delay here, but you might find this interesting, it talks about the pros and cons of mrna reaching different types of cells.

https://www.science.org/conten...

My feed filled itself with political rage bait groups and pages after I took the bait from one of them. It's pretty annoying because no matter how many times I pick "hide all from..." there is an endless supply of rage bait.

The stuff that brought me to Facebook is basically just needles in the haystack of crap at this point.

We're not looking at the black hole itself, but at the material around it, much of which is far enough from the "extreme gravity" that astrophysicists are not unreasonable in thinking that they have a pretty good handle on what's going on.

The reason we're discovering increasingly older objects in space is mostly just that we're getting increasingly sensitive instruments, and we're getting better at using them.

We're paying $1.2 billion to get LESS energy?

When Republicans said to run it like a business I don't think they understood how Trump runs his businesses....

You're not even wrong. Nothing injected stays "localized."

Muscle tissue is chosen for most injections because it has MORE blood supply than fat, so that more blood will reach it faster.

All I see now is stuff we agree on, and straw man arguments.

I am familiar with fine tuning, but it seems you are not familiar with its limitations. There's a reason that even the frontier labs distinguish between pretraining and fine tuning.

There's not a great analogy for conventional software and fine tuning, but if one has the pretraining corpus then there are no limits (other than GPU time), so that's a good analogy for open source.

You seem to think that since fine tuning allows SOME customization then there's no need to be able to retrain from scratch.

The least bad analogy I can think of is a compiled binary with an SDK for extensibility. That lets you customize some aspects of the behavior. But it is not a substitute for having the full source code and being able to rebuild from it.

Yes and no. You're not wrong, but there's a limit to how far you can go with that approach. A web search for "catastrophic forgetting" will explain more.

It's a hard problem.

That's a bit like saying that compiled binaries are just as good as source code because you can modify the program's behavior by altering the machine code. Just reverse engineer it and patch the binary. Who needs source code anyway?

Also, finding the right weights to update is hard. And by hard I mean that an army of PhD students and postdocs is working on that problem.

As for continued training of an existing model... a web search for "catastrophic forgetting" will be instructive. You can mitigate that risk using the original training data, but then we're back to needing access to that original training data.

The idea of "open weights" AI models makes about as much sense as "open binary" software.

It means you can run it locally. It doesn't mean you can modify the source and recompile and enjoy your customized version.

The following analogies aren't perfect but they are useful:

Using backpropagation to make an LLM from training data is like using a compiler to turn source code into an LLM. Using evaluations to monitor the training process is like using tests to monitor the continuous integration pipeline.

An open source LLM would include the mountain of data that it was trained on, plus the software that trained it. Including evaluations.

There are of course a number of complicating factors, but to a first approximation a truly open model must include the data that was used for pre training and fine tuning. As well as the software that ran the training. And the evaluations.

Part of the challenge is that training systems are often highly customized datacenters full of GPUs, so making portable build environments is hard. But nobody promised that this would be easy.

A5k1ng f0r 4 fr13nd.

It was a few years ago, but it was fascinating.

https://www.preposterousuniver...

They focused entirely on the cost to the AI consumers - which is not unreasonable since that's who Gartner's customers are.

But it's still a noteworthy omission for anyone with the slightest interest in the trajectory of the LLM industry as a whole. If OpenAI / Anthropic / XAI are providing their services at a loss, and their customers are consuming those services at a loss, then the GPU and RAM vendors are the only winners.