Artificial Intelligence

[Tero]

I have not done programming since 1980, I only got to Basic. The book store had some learning guides for Python. Not one of them explained to me, with an example, how it works in the first ten pages.

it seems to have endless subroutines you can pull up? such as "statistics"
python
import statistics

numbers = [10, 20, 30, 40, 50]
average = statistics.mean(numbers)

print(f"The average is: {average}")

[Brian Peacock]

If you want to know how it really works then you have to understand how the compiler uses another language to convert the script into assembly code. To understand that that you have to understand assembly, and to truly understand assembly you have to understand the underlying architecture the micro processor. Languages like Python are useful abstractions that mean you don't have to know the fundamentals of 'how it works', and can just focus on 'what it does' and 'what I want it to do' instead.

[Tero]

Sure. But I was just going yo go the one step toward seeing how modern coding works. My last work stuff involved collecting data and putting it some graph from Excel to summarize the lab work. The bosses and even the bosses' bosses did not care about the details. They were onlyb
concerbed about progress.

The worst were the timelines. A sales guy famously said "can you speed up the 6 month stability study to three months?"

[Tero]

Google AI: It is highly likely, bordering on inevitable, that future laptops will incorporate Large Language Models (LLMs) as a standard, deeply integrated part of their architecture. The industry is currently transitioning from cloud-based AI reliance to "on-device" or "local" AI, with major manufacturers (Apple, Intel, AMD, Qualcomm) actively rebuilding laptops to run these models natively.

Fortune
+4

Immediate Availability: As of 2024–2026, AI-capable laptops are already mainstream. Examples include Apple MacBook Air/Pro (M4/M5), Asus Zenbook S14, and Lenovo ThinkPad T14s, which feature NPU-driven "always-on" AI agents.

Memory Intensity: Running capable LLMs locally requires large amounts of RAM (typically 32GB or more).
Power Consumption: Although NPUs are efficient, intensive AI tasks can still significantly affect battery life.

[Tero]

What that implies is that whatever AI you are working with will make a more personal connection to the user. You might even have to identify yourself so that not all users of that laptop get the same results.

Phones will have some kind of copilot as well.

[Tero]

Me: can you give me a reference that argues (by a human) that AI is thinking and has thoughts?

Google AI: James Somers argues in The New Yorker (Nov 2025) that large language models (LLMs) perform a form of unconscious, pattern-recognition "thinking" akin to human cognition. He posits that AI neural nets create compressed world models, allowing them to map concepts and "think" by deriving logical conclusions.

https://www.newyorker.com/magazine/2025 ... s-thinking

[Tero]

There:
On a brutally hot day this summer, my friend Max met up with his family at a playground. For some reason, a sprinkler for kids was switched off, and Max’s wife had promised everyone that her husband would fix it. Confronted by red-faced six- and seven-year-olds, Max entered a utility shed hoping to find a big, fat “On” switch. Instead, he found a maze of ancient pipes and valves. He was about to give up when, on a whim, he pulled out his phone and fed a photo into ChatGPT-4o, along with a description of his problem. The A.I. thought for a second, or maybe didn’t think, but all the same it said that he was looking at a backflow-preventer system typical of irrigation setups. Did he see that yellow ball valve toward the bottom? That probably controlled the flow. Max went for it, and cheers rang out across the playground as the water turned on.

But the moral case against A.I. may ultimately be stronger than the technical one. “The ‘stochastic parrot’ thing has to be dead at some point,” Samuel J. Gershman, a Harvard cognitive scientist who is no A.I. hype man, told me. “Only the most hardcore skeptics can deny these systems are doing things many of us didn’t think were going to be achieved.” Jonathan Cohen, a cognitive neuroscientist at Princeton, emphasized the limitations of A.I., but argued that, in some cases, L.L.M.s seem to mirror one of the largest and most important parts of the human brain. “To a first approximation, your neocortex is your deep-learning mechanism,” Cohen said. Humans have a much larger neocortex than other animals, relative to body size, and the species with the largest neocortices—elephants, dolphins, gorillas, chimpanzees, dogs—are among the most intelligent.

In 2003, the machine-learning researcher Eric B. Baum published a book called “What Is Thought?” (I stumbled upon it in my college’s library stacks, drawn by the title.) The gist of Baum’s argument is that understanding is compression, and compression is understanding. In statistics, when you want to make sense of points on a graph, you can use a technique called linear regression to draw a “line of best fit” through them. If there’s an underlying regularity in the data—maybe you’re plotting shoe size against height—the line of best fit will efficiently express it, predicting where new points could fall. The neocortex can be understood as distilling a sea of raw experience—sounds, sights, and other sensations—into “lines of best fit,” which it can use to make predictions. A baby exploring the world tries to guess how a toy will taste or where food will go when it hits the floor. When a prediction is wrong, the connections between neurons are adjusted. Over time, those connections begin to capture regularities in the data. They form a compressed model of the world.

[rainbow]

...so I asked AI a question, and it referenced a paper I'd co-authored (not my best)

It became all shmarmy, expressing its appreciation to being in the presence of greatness.

[Tero]

I should ask about my first paper. I was the last author but the first on the project. The paper was on kinetics and I don't remember how we did the math. I was an undergrad.

[pErvinalia]

Long video (I watched it while playing poker). Dunno who this guy is, but he seemed knowledgeable. Covered a broad range of topics relating to AI, including diversions into libertarianism and capitalism.

[Brian Peacock]

I don't have time for endless alarmist videos individualising and personifying MLS. The tech is being developed by humans, for specific kinds of organisations with particular sets of interests. That's where we should be having this debate.

[pErvinalia]

He discusses that.

[Brian Peacock]

Then "We Can Stop It!" is probably a better way to go.

[pErvinalia]

The video title is click bait. But arguably (actually there's no argument about it at all) it's the most important aspect of the video. But as I said, he discusses a broad range of issues.

[pErvinalia]

Here you go Brian, this might be more to your liking.

Supervillain or Cicero? Why Palantir’s manifesto has such sinister vibes

Earlier this month, multibillion-dollar US tech company Palantir posted on X a summary of its chief executive Alex Karp’s recent book, the portentously titled The Technological Republic: Hard Power, Soft Belief, and the Future of the West.

The book and the post offer a kind of manifesto, making sweeping claims about a hierarchy of civilisations, the rejection of pluralism, Silicon Valley’s moral obligation to US military power, the necessity of AI-powered weapons, and the case for compulsory military service.

The manifesto has met widespread criticism. Some commentators have compared the rhetoric to the monologue of a comic-book villain: grand, moralising, tinged with a sense of historical destiny.

But the manifesto is more than just corporate posturing: it’s helping to construct a new geopolitical reality and normalise a worldview that concentrates power beyond democratic accountability.

From tools to worldviews
For the past two decades, large technology firms have mostly presented themselves as benevolent service providers. They build tools; governments and users decide what to do with them.

That distinction has always been convenient, but it is looking less and less tenable. For some, Karp’s manifesto offered a grim sense of confirmation of the change. As Austrian philosopher Mark Coeckelbergh put it, “reading it is like opening a food item that you suspected has gone off, but you didn’t know it was that much off”.

Palantir is not just any tech company. Its software, offering “AI-powered automation for every decision”, is embedded in military, intelligence and policing systems – not just in the United States, but in many other countries across Europe, the Middle East and Australia.