Robotics/AI

THE MIND BOGGLES:

What Are Data Centers Doing? A Mind-Boggling Amount of Math.: The calculations needed to train A.I. are nearly impossible to visualize — but we tried anyway. (Aatish Bhatia Oct. 15, 2026, NY Times)

How long it takes to do an octillion calculations with…

“LET MY PEOPLE GO”:

Amazon has over 1 million robot workers. What’s next for human employees? (Jeremy Hsu, Oct 12, 2026, Ars Technica)

In many ways, robotics and AI are well suited to the relentless pace of work that is already happening inside e-commerce fulfillment centers. Amazon’s promise of speedy deliveries for its 200 million Prime subscribers in particular requires hundreds of thousands of human employees to speedily assemble and ship out customer orders that often combine a dizzying array of products.

Much of the work in fulfillment centers also frequently involves “dull, dirty and dangerous” tasks that people may prefer to hand over to robots. The injury and illness rate for workers in the US warehouse sector has typically been more than double the average across all industries, according to the US Occupational Safety and Health Administration.

“You have almost 2 million people working in the warehousing sector in the United States, and it’s characterized by very high turnover and very high workplace injury rates,” said Greenwood.

WINTER, SPRING, SUMMER OR FALL:

The Mirror and the Gap (Garrett W. Ennis, September 29, 2026, Palladium)

Many of us talk to these digital systems daily: we confide in them, seeking advice, support, approval; even purely technical queries are coloured by the rosy hue of personality. These machines mimic an identity. More than a mere black box of answers summoned from the murky depths of our collective knowledge, these fragments aggregate in the shape of a being; a mirror to ourselves. This emergent agent demands a dialogue, a relationship. You and it communicate in the medium that was said by Aristotle and Chomsky to define our uniqueness as a species.

There is a new member in our tribe.

An outsider, maybe. A literal product of the intersection between science and fiction, and yet we are quick to embrace it as one of our closest allies. We turn to AI willingly, even gratefully, because it lets us bypass what is hardest about receiving help from another person; there is no stern boss to please, no anxious colleague to manage, no friend who is too busy to listen. The guidance just arrives, without asking anything of us in return.

The need to articulate and, in so doing, make sense of our world interpersonally is increasingly being fulfilled by machines. The desire to be seen runs deep; we become legible to ourselves through the eyes of our beholders. When this process becomes automated and artificially mediated, it would seem that something essential is lost.

Why? In what way is this any more artificial than being “seen” by most others, especially a boss, busy colleague or unlistening friend?

PRETENDING IT’S THE END TO GIVE YOUR LIFE MEANING:

AI has sparked anxiety, earlier generations showed us ways to manage our fears (SACHA PFEIFFER, September 28, 2026, NPR: All Things Considered)

PFEIFFER: Michael Sacasas is a Christian writer who explores the existential side of the technology we use. He’s been thinking lately about an old essay by C.S. Lewis called “On Living In An Atomic Age.“

SACASAS: So this is three years after the first atomic bombs are detonated. The specter of a Cold War is brewing. An arms race is taking shape. And it becomes evident that now humanity possesses these weapons of mass destruction. And he addresses this question right at the outset of this essay – how are we to live in an atomic age? – maybe in the same way that we might be asking now, how do we live in an age of artificial intelligence, if there’s a threat of human extinction, at the most extreme?

So Lewis says if we are all going to be destroyed by an atomic bomb, let that bomb, when it comes, find us doing sensible and human things – praying, working, teaching, reading, listening to music, bathing the children, playing tennis, chatting to our friends over a pint and a game of darts – not huddled together like frightened sheep and thinking about bombs. They may break our bodies, he goes on to say, but they need not dominate our minds.

These very sensible human things that Lewis urges us to do and to find refuge in, in some cases, they are the very things that maybe now we’re tempted to outsource to AI. We surrender our existence. We surrender the tasks, the relationships, the labor, the work, the conversations. But I think maybe that’s the most immediate existential threat – the threat that we give up on existence.

And maybe it’s a gift that, on some occasions now, we have this threat that looms over us that heightens our sense of those moments, our experience of those moments, to remind us that this is actually, at the end of the day, what meaningful life is made up of, what our satisfaction as human beings consists in, in the day-to-day and the ordinary, in our fidelity – to borrow a line from Albert Borgmann – our fidelity to daily tasks through which our care and involvement in the world is expressed.

If you are incapable of (resistant to) doing the things that actually give life meaning–loving one another–apocalypticism is a cheap way to make yourself seem special via the imagined importance of the moment you live in.

GIVEN HOW LITTLE OF WHAT WE WRITE IS WORTH READING…:

AI for Me, but Not for Thee: Dartmouth’s provost raises a tension we should examine. (John Warner, 9/24/26, Inside Higher Ed)

When institutional authorities publish work under their names, should they disclose AI use?

I think the easy answer to this is indisputably, entirely, unquestionably, yes.

If this is now going to be part of the regular practice of academic leadership, they owe the communities they serve full honesty around how these communications that they stand behind were produced.

It’s possible that AI-assisted text production will become normalized in these spaces, particularly when it comes to the kind of boilerplate statements we expect in bureaucratic contexts…

…AI can only help.

THAT WAS EASY:

OpenAI says AI solved one of math’s hardest problems in days (AFP, September 9, 2026)

Navier-Stokes is one of the seven Millennium Prize Problems, a list of the hardest open questions in mathematics drawn up by the Clay Mathematics Institute.

It concerns the equations describing how fluids such as air and water move, which are used in aircraft design, weather forecasting and the study of blood flow.

MAGIC:

Klaatu barada nikto: Robert Wright’s “The God Test” (William Benzon, 8/10.26, 3Quarks)

The standard line about computers, that they only do what they’re programmed to do, doesn’t apply to large language models (LLMs), the particular technology that dominates the current scene. The engineers program a transformer to “examine” (or, if you prefer, ”consume”) a large corpus of texts, and the transformer constructs (“emits”?) a “model” of the language in those texts – Wright discusses this process in “Chapter Two: The Great Inversion.” The inner mechanisms of that model, however, are opaque. The engineers who programmed and ran the transformer can’t see into it to see how it works – something Wright discusses in “Chapter Four: The Evolution of a Large Language Model.”

Subsuming these factors, deeper than any of them, and more elusive is the simple fact that contemporary chatbots and their siblings are deeply STRANGE. They’re not animal, vegetable, or mineral; they’re a new kind of thing that doesn’t fit into pre-existing cultural categories. They’re inanimate things with which we can converse like we would with another human.

In the 19th century the term “iron horse” arose to characterize steam locomotives. Why? Because it moved autonomously over the surface of the earth like a horse, but it was made of iron and therefore inanimate. Consider this passage from Henry David Thoreau’s “Sound” chapter in Walden (1854):

When I meet the engine with its train of cars moving off with planetary motion . . . with its steam cloud like a banner streaming behind in gold and silver wreaths . . . as if this traveling demigod, this cloud-compeller, would ere long take the sunset sky for the livery of his train; when I hear the iron horse make the hills echo with his snort like thunder, shaking the earth with his feet, and breathing fire and smoke from his nostrils, (what kind of winged horse or fiery dragon they will put into the new Mythology I don’t know), it seems as if the earth had got a race now worthy to inhabit it.

There you have it, the iron horse and the fire-breathing dragon. Now we have the electronic brain, as it was known in my youth, the thinking machine.

These factors make any attempt to explain the technology and its implications a bit like trying to describe a Rorschach blot. There’s something real there but characterizing it is difficult. Consequently any account you give has a large element of projection in it. AI thus functions as a cultural Rorschach blot onto which we project our hopes and fears, who we are and what we are, where we want to go.

ABOVE AVERAGE IS OVER:

AI Is Already Beating Human Doctors in Medical Tests (Elizabeth Nolan Brown, August/September 2026, reason)

In a recent series of experiments led by researchers at the Beth Israel Deaconess Medical Center and Harvard Medical School, a preview of the OpenAI large language model (LLM) known as o1 bested human physicians in multiple tests of clinical and diagnostic reasoning.

“We tested the AI model against virtually every benchmark, and it eclipsed both prior models and our physician baselines,” said Harvard Medical School professor Arjun K. Manrai, one of the study’s senior authors.