First of all you don't know what they are doing. Secondly, you failed to grasp the major failure in your reasoning again. If the way they do it is inaccurate that in no way accounts for any aberration in the data.

You must be an engineer to have figured that out!

Crap to call this greenwashing.
The problem of microplastics already exists around the world, including in the snow on Antarctica and mother's milk.
When this would lead to an affordable way to catch and remove the existing microplastics this would be great news.

So your theory is that the OS fingerprinting became significantly more unreliable for a short period of time, and then returned to the previous degree of unreliability? I'm going to go out on a limb and say you didn't think that line of reasoning through.

In order to de-shittify Windows you would have to first de-shittify Microsoft, and that is never going to happen by cause Microsoft didn't get shittified, it has always been a shit company.

The Indian outfit I consult with is a Microsoft shop, everything is tied to Office 365.
I use Kubuntu and using the Brave browser I can easily access their documents.
Two days ago I spoke via Teams with an engineer working in the Quality and Regulatory dept. of a French company and he told me he is also using Linux.
At first he tried to reach me from a Windows computer but the mandated anti virus software did not allow him to use the camera in Teams, switching to Linux solved the problem.

You should ask Gemini to read the summary for you next time so that you won't seem stupid.

If there is one thing I love about posts from you, it is that whenever I see a reply from you in my inbox I know I'm about to be entertained by a clown. LOL

I forgot to mention in my most recent post ... The extraordinary claim is the one made by you, to wit that the exponential improvement of AI models that has been going on for years and still continues has plateud and that you are the only one who knows about it. I'm always amazed at how truly stupid you actually are.

Remember' Chat-Gpt is just a word predictor! It doesn't think, and it will never be of any use! Only an idiot would be filled into thinking it does anything other than generate words! Thank you so much for trying to make a post to make me look foolish, and in doing so, unwittingly proving that you now admit that you have been clueless in every exchange we have had regarding AI. ROTFLMAO! Off you go now ...

Or are they more mentally fit due to more social interactions and navigating a car?

Any details on that?

I just watched the video. It doesn't at all say what you claim it says. It doesn't even remotely claim that AI model capabilities have stopped improving exponentially. I'm not sure where you got that idea. Advancements aren't limited to making bigger LLMS. Imagine if you will that you have a brain that has never been trained ... a baby. Over time it first learns to recognize objects and gains object permanence, then it learns the alphabet, then words, then sentence structure. It eventually gets to the point where it has learned basic math skills. It gets exposed to more and more ideas and concepts, and *learns* more and more. It learns physics, biology, neurology, and gains other medical knowledge. The size of the brain doesn't increase, but it gets closer and closer to being an expert on all of these subjects. That, loosely speaking describes the difference between making a neural network bigger and increasing its capabilities. My explanation is a bit simplistic, but that isn't a bad thing. The idea is to get you off of this idea that there is a linear correlation to neural network size and the capabilities it has after learning over time. You can empirically prove this to yourself by having a conversation with OpenAI or Google models from 2024 and then with the latest releases running them locally on *your* hardware. The hardware is the same. It is no more capable. The *models* however, are worlds apart.

Again ... Scaling is adding more hardware resources to a single running instance of a model to gain improvement. It has never been true that you get an exponential improvement that way, and nobody ever claimed it did. The purpose of building data centers isn't scaling. It is to allow more model instances to run concurrently. Why would I start by focusing on the wrong studies?

I'm going to look more deeply at some of the links you provided, however I can also already tell you that you are confusing scaling (throwing more hardware at a given model) with the exponential improvement of model capabilities with each new model that has been occurring exponentially since before 2025, and that capability increase has accelerated in 2026, not plateud. You are also confusing LLMs with AI. LLMs are just one component of a complete AI stack. Given the same exact hardware the capabilities are improving exponentially and continue to do so as I type this.

It appears you haven't even seen the summary. ROTFLMAO