I've turned my recent post about #Firefox, #AI, #ML and #LLM in to a quick article on how to disable this new feature in #Firefox
https://firesphere.dev/articles/removing-aimachine-learningllm-integrations-from-ff

I've turned my recent post about #Firefox, #AI, #ML and #LLM in to a quick article on how to disable this new feature in #Firefox
https://firesphere.dev/articles/removing-aimachine-learningllm-integrations-from-ff

Asking the Fedi.

Is the expected hyperscaling of AI data centers a generativeAI/LLM thing? I cannot see "old school" ML type things (factory and port optimisation, medical image analysis) generating that leap in data center use.

Or am I missing something?

and if it is does that mean, if the bubble bursts, it will take out the big western base load electrical demand growth story as well?

#generativeAI #ml #ai #electricity

All major AI companies have leaned heavily into anthropomorphization. I think there are broader downsides to this approach, but I also find it personally annoying. When I’m using an LLM, I don’t want to think of it as a person with feelings and preferences or as a friend. I want to think of it as a tool I can use to accomplish a goal.

https://chromamine.com/2025/07/make-chatgpt-less-human/

#ML#AI#LLM#ChatGPT

It's time to stop calling LLMs "AI". This practice is tarnishing all the really powerful advances in machine learning that are accelerating drug discovery, making previously intractable optimization problems feasible, helping to elicit scientific models from humanly incomprehensible troves of data, etc.

Lumping all the "AI" under one term is doing a major disservice to humanity. At the moment, when I see a company is heavily investing in "AI" I can't tell if they're going to start having major technological breakthroughs, or start having major infrastructure failures from trusting vibe coding by minimal wage casuals.

It's time to taboo the term "AI" and be more specific.
#AI#ML#LLMs

It's time to stop calling LLMs "AI". This practice is tarnishing all the really powerful advances in machine learning that are accelerating drug discovery, making previously intractable optimization problems feasible, helping to elicit scientific models from humanly incomprehensible troves of data, etc.

Lumping all the "AI" under one term is doing a major disservice to humanity. At the moment, when I see a company is heavily investing in "AI" I can't tell if they're going to start having major technological breakthroughs, or start having major infrastructure failures from trusting vibe coding by minimal wage casuals.

It's time to taboo the term "AI" and be more specific.
#AI#ML#LLMs

I also got experience with the following (5 = a lot, 1 = a little) :

#machinelearning #ml (3) (I have implemented some ML models myself in the past, for learning purposes.)
#guix (3) (Using it for reproducible setups of projects.)
#functionalprogramming #fp (5) (Doing it in my own projects.)
#objectorientedprogramming #oop (4) (last job and past 😜 in my own projects.)
#CI / #CD (3) (Last job)
#make (4) (using it for my own project setups and convenience)
#testing (4) (last job, own projects)

Something I’ve been thinking about a lot in the current battle over the future of (pseudo) AI is the cotton gin.

I live in a country where industrial progress is always considered a positive. It’s such a fundamental concept to the American exceptionalism claim that we are taught never to question it, let alone realize that it’s propaganda.

One such myth, taught early in grade school, is the story of Eli Whitney and the cotton gin. Here was a classic example of a labor-saving device that made millions of lives better. No more overworked people hand cleaning the cotton (slaves, though that was only mentioned much later, if at all). Better clothes and bedding for the world. Capitalism at its best.

But that’s only half the story of this great industrial time saver. Where did those cotton cleaners go? And what was the impact of speeding up the process?

Now that the cleaning bottleneck was gone, the focus was on picking cotton as fast as possible. Those cotton cleaners likely, and millions of other slaves definitely, were sent to the fields to pick cotton. There was an unprecedented explosion in the slave trade. Industrial time management and optimization methods were applied to human beings using elaborate rule-based systems written up in books. How hard to punish to get optimal productivity. How long their lifespans needed to be to get the lost production per dollar. Those techniques, practiced on the backs and lives of slaves, became the basis of how to run the industrial mills in the North. They are the ancestors of the techniques that your manager uses now to improve productivity.

Millions of people were sold into slavery and worked to death because of the cotton gin. The advance it provided did not, in fact save labor overall. Nor did it make life better overall. It made a very small set of people much much richer; especially the investors around the world who funded the banks who funded the slave purchases. It made a larger set of consumers more comfortable at the cost of the lives of those poorer. Over a hundred years later this model is still the basis for our society.

Modern “AI” is a cotton gin. It makes a lot of painstaking things much easier and available to everyone. Writing, reading, drawing, summarizing, reviewing medical cases, hiring, firing, tracking productivity, driving, identifying people in a lineup…they all can now be done automatically. Put aside whether it’s actually capable of doing any of those things well; the investors don’t care if their products are good, they only care if they can make more money off of them. So long as they work enough to sell, the errors, and the human cost of those errors, are irrelevant. And like the cotton gin, AI has other side effects. When those jobs are gone, are the new jobs better? Or are we all working that much harder, with even more negative consequences to our life if we fall off the treadmill? One more fear to keep us “productive”.

The Luddites learned this lesson the hard way, and history demonizes them for it; because history isn’t written by the losers.

They’ve wrapped “AI” with a shiny ribbon to make it fun and appealing to the masses. How could something so fun to play with be dangerous? But like the story we are told about the cotton gin, the true costs are hidden.

#ML#TESCREAL

In a move that surprises absolutely noone, GitHub now requires users to login in order to browse public repositories (including open source projects). After a few (~10) requests, you get blocked (I can confirm). In order to fight AI scrapers, I guess.

So, GitHub decided to blanket-limit access to open source projects as a defense against the very scourge that they(r parent company) unleashed on the world.

I won't be hypocrite: it's a bit embarrassing, but undeniably satisfying to say "told you so". I moved away from GitHub long ago and I moved all my stuff to Codeberg instead. And so happy I did!

Next step: radicle.xyz maybe?

https://github.com/orgs/community/discussions/159123

#github #microsoft #openai #codeberg #ai #ml #llm #enshittification #foss #floss #opensource #radicle

I recently had the opportunity to present at the Melbourne #ML and #AI Meetup on the topic of the #TokenWars - the resource conflict over data being harvested to train AI models like #LLMs - and the alateral damage this conflict is causing to the open web.

With a huge thanks to Jaime Blackwell you can now see the video here:

https://www.youtube.com/watch?v=C86Y3mXnsNI

Huge thanks to Lizzie Silver for all her behind the scenes work and to @jonoxer for making the connections.

Check out the Meetup at:

https://www.meetup.com/machine-learning-ai-meetup/