Robotics/AI

DUDE, YOU’RE HARSHING MY LUDDISM:

What If AI Chatbots Are Saving Lives? (Adam Omary and Jennifer Huddleston, 5/05/26, Cato at Liberty)

According to the Centers for Disease Control and Prevention, the American suicide rate began climbing around the year 2000—before ChatGPT, smartphones, or social media even existed. It accelerated through the 2010s, then, contrary to popular narrative, plateaued and modestly declined after 2018—even as generative AI moved from research labs into the pockets of nearly every teenager in the country. If chatbots were a meaningful driver of adolescent suicide, the curves should have moved together. They have not, and, importantly, suicide rates among young Americans remain the lowest among any age group.

While any loss of a young life to suicide is a tragedy, whatever is killing young Americans predates the technology that lawmakers now propose to ban them from using.

What the GUARD Act’s sponsors do not seriously consider is the other side of the ledger. There are cases where AI could help Americans of all ages when it comes to mental health. Roughly half of Americans with a diagnosable mental health condition never seek professional help; stigma, cost, and fear of involuntary intervention keep them silent. For some of them—especially adolescents in households where therapy is unaffordable, unavailable, or unsafe to disclose—a chatbot is their most reliable form of emotional support.

THERE’S NO SUCH THING AS QUALITY:

Consumers Prefer AI Music Until They’re Told It’s AI (Jana Friedrichsen, Julia Schwarz and Michel Clement, May 4, 2026, ProMarket)

In our final study from 2024, we conducted a similar experiment to study how listeners compared human-made songs to AI-generated ones. Our study varied whether songs were human-made or AI-generated (song origin) and whether the listener received this information or not for pop and electronic dance songs. In addition to listeners’ stated preferences, we also measured how much they were willing to pay to listen to the song as a second measure of preference. We found that listeners actually perceive AI-generated songs to be superior. However, if the music is disclosed to be AI-generated, their desire to relisten to the song and their willingness to pay decreases. This effect is mainly driven by pop listeners.

THE FUTURE ALWAYS HAPPENS FASTER THAN EXPECTED:

Quantum computers to break our codes faster than expected (Craig Costello, April 13, 2026, Asia Times)

The changes are coming on two fronts. On one, tech giants such as IBM and Google are racing to build ever-larger quantum computers: IBM hopes to achieve a genuine advantage over classical computers in some special cases this year, and an even more powerful “fault-tolerant” system by 2029.

On the other front, theorists are refining quantum algorithms: recent work shows the resources needed to break today’s cryptography may be far lower than earlier estimates.

The net result? The day quantum computers can break widely used cryptography – portentously dubbed “Q Day” – may be approaching faster than expected.

THE FUTURE HAPPENS WHILE YOU’RE DISMISSING IT:

The case of the disappearing secretary (Rowland Manthorpe, Mar 01, 2026, Rowland’s Newsletter)

Not so long ago, the work of secretaries – typing, filing, organising, administrating – was a cornerstone of the economy. By 1984, six years after the map above, there were around 18 million clerical and secretarial workers in the United States, roughly 18 percent of the entire workforce. This was totally normal. In the UK at the same time, between 17 and 18 percent of the workforce was some kind of secretary. In France it was 16 percent. Different economies with different economic policies; all ended up with one in five or six workers employed in clerical work.

Why so many? Because every stage of information processing required a human hand. In a mid-century organisation, a manager did not “write” a memo. He dictated it. A secretary took it down in shorthand, then retyped it. Then made copies. Then collated the copies by hand. Then distributed them. Then filed them. And so on and so on. Nothing moved unless someone physically moved it. There was no other way.

Human computers at NASA’s Jet Propulsion Lab in the 1950s. Credits: NASA/JPL-Caltech
For this reason, the most sophisticated, information-dense organisations were often the ones with the most administrative staff. As NASA prepared to launch the Apollo missions in the mid-1960s, 15% to 18% of its civil service workforce was classified as “clerical and administrative support”. There were the human “computers” made famous by Hidden Figures, but also technical typists, who typed up mathematical equations. As one of those typists, Estella Gillette, later put it: “The engineers depended on us for everything that wasn’t their job. We were their support system.”

This line is often taken as an inspiring motivational quote, but it was a literal description of the situation at the time, because of what today we might call an interface problem. The invention of shorthand and the typewriter in the early twentieth century had made it possible to create accurate records, but senior staff – even engineers at NASA – didn’t interact directly with the administrative machinery of the office. Secretaries and clerks were the unavoidable interface between the manager and the ability to get things done. You spoke to a secretary; they “interfaced” with the shorthand pad and the typewriter. You handed over a paper; they “interfaced” with the filing cabinet. Every kind of activity was organised this way. The secretary was the interface for the diary, a physical object kept only on their desk. (This could be a source of real influence.) They were the human “firewall” or routing system for phone calls. If the manager wanted a coffee, well that was the secretary too. It all went through her.

Then came the personal computer.

aBOVE aVERAGE IS OVER:

Coding After Coders: The End of Computer Programming as We Know It (Clive Thompson, March 12, 2026, NY Times Magazine)

For decades, coding was considered such wizardry that if you were halfway competent you could expect to enjoy lifetime employment. If you were exceptional at it (and lucky), you got rich. Silicon Valley panjandrums spent the 2010s lecturing American workers in dying industries that they needed to “learn to code.”

Now coding itself is being automated. To outsiders, what programmers are facing can seem richly deserved, and even funny: American white-collar workers have long fretted that Silicon Valley might one day use A.I. to automate their jobs, but look who got hit first! Indeed, coding is perhaps the first form of very expensive industrialized human labor that A.I. can actually replace. A.I.-generated videos look janky, artificial photos surreal; law briefs can be riddled with career-ending howlers. But A.I.-generated code? If it passes its tests and works, it’s worth as much as what humans get paid $200,000 or more a year to compose.

It’s impossible to overstate deflationary pressures.

THE REAL WORLD:

This Absurdly Complex Star Destroyer Model May Be the Most Detailed Ever Created (Tom Hawking, March 5, 2026, Gizmodo)

The thing with real-life models—whether they’re for use in films or for sale as merchandise—is that there’s a fundamental limit on how complex they can get. I mean, you can’t ship a 172,340-piece Lego Star Destroyer set. (Although, if Lego ever did ship a 172,340-piece set, it would absolutely be a Star Destroyer, and a sector of the fandom would absolutely shell out for it.)

But anyway, the point is that the real world is constrained by considerations like supply, demand, manufacturing capacity, and, y’know, common sense. By contrast, a 3D model is really only constrained by the question of whether trying to render it might result in your computer catching fire. So long as your computer is powerful enough to handle them, models and projects can be arbitrarily large.

THAT WAS EASY:

AI and 3D printing help researchers create heat‑ and pressure‑resistant materials for aerospace and defense applications ( Houlong Zhuang & Vitor Rielli 4, 2026, The Conversation)

Our alternative approach uses reinforcement learning, a form of artificial intelligence best known for training computers to master games such as Go or chess.

Designing a new alloy is a bit like mixing ingredients for a recipe, but at the atomic level. Instead of planning moves on a board, the AI system explores thousands of possible alloy recipes – for example, different combinations of chemical elements. Even tiny changes in the ingredients can completely change how the final material behaves.

The AI evaluates each candidate virtually against multiple criteria, including strength at temperatures above 1,800 degrees Fahrenheit (1,000 degrees Celsius) and resistance to damage caused by reacting with oxygen at high heat, as well as weight, cost and, crucially, whether it can be reliably 3D-printed.

THAT WAS EASY:

MIT’s New 3D Printer Can Print a Working Motor, Complete With Moving Parts: “This is a great feat, but it is just the beginning.” (Victor Tangermann, Mar 1, 2026, Futurism)

The tech behind 3D printing has come an extremely long way. The additive manufacturing technique, which generally involves depositing one layer at a time, has gone from relatively crude rapid prototyping in industrial settings to high-end fabrication of detailed parts in a growing list of fields, from medical implants to the construction of entire neighborhoods and rocket engines.

Now, MIT researchers have devised new tech that can 3D print entire complex machines with moving parts in a matter of hours. As Gizmodo half-jokingly points out, it brings us one small step closer to being able to “steal a car” by downloading it from the internet, as suggested in the slogan of the much-derided anti-piracy ad from the early 2000s.

EARLY DAYS:

AI vs 100,000 humans: Which wins the creativity contest? (Dr. Tim Sandle, March 1, 2026, Digital journal)

A large study, comparing more than 100,000 people with today’s most advanced AI systems, has delivered a surprising result: Generative AI can now beat the average human on certain creativity tests.

Models like GPT-4 showed strong performance on tasks designed to measure original thinking and idea generation, sometimes outperforming typical human responses.

ABOVE AVERAGE IS OVER:

Is AI Bringing the End of Theory? (Sabine Hossenfelder, Feb 24, 2026)