The late Rear Admiral Grace Hopper was a gifted mathematician and undisputed pioneer in computer programming, honored posthumously in 2016 with the Presidential Medal of Freedom. She was also very much in demand as a speaker in her later career. Hopper's famous 1982 lecture on "Future Possibilities: Data, Hardware, Software, and People," has long been publicly unavailable because of the obsolete media on which it was recorded. The National Archives and Records Administration (NARA) finally managed to retrieve the footage for the National Security Agency (NSA), which posted the lecture in two parts on YouTube (Part One embedded above, Part Two embedded below).

Hopper earned undergraduate degrees in math and physics from Vassar College and a PhD in math from Yale in 1930. She returned to Vassar as a professor, but when World War II broke out, she sought to enlist in the US Naval Reserve. She was initially denied on the basis of her age (34) and low weight-to-height ratio, and also because her expertise elsewhere made her particularly valuable to the war effort. Hopper got an exemption, and after graduating first in her class, she joined the Bureau of Ships Computation Project at Harvard University, where she served on the Mark I computer programming staff under Howard H. Aiken.

She stayed with the lab until 1949 and was next hired as a senior mathematician by Eckert-Mauchly Computer Corporation to develop the Universal Automatic Computer, or UNIVAC, the first computer. Hopper championed the development of a new programming language based on English words. "It's much easier for most people to write an English statement than it is to use symbols," she reasoned. "So I decided data processors ought to be able to write their programs in English and the computers would translate them into machine code."

Enlarge / In the experiments, people had to judge what constituted a fair monetary offer. (credit: manusapon kasosod)

In many cases, AIs are trained on material that's either made or curated by humans. As a result, it can become a significant challenge to keep the AI from replicating the biases of those humans and the society they belong to. And the stakes are high, given we're using AIs to make medical and financial decisions.

But some researchers at Washington University in St. Louis have found an additional wrinkle in these challenges: The people doing the training may potentially change their behavior when they know it can influence the future choices made by an AI. And, in at least some cases, they carry the changed behaviors into situations that don't involve AI training.

Would you like to play a game?

The work involved getting volunteers to participate in a simple form of game theory. Testers gave two participants a pot of money—$10, in this case. One of the two was then asked to offer some fraction of that money to the other, who could choose to accept or reject the offer. If the offer was rejected, nobody got any money.

Enlarge / Right now, the software doesn't do arms, so don't go taking on any aliens with it. (credit: 20th Century Fox)

Exoskeletons today look like something straight out of sci-fi. But the reality is they are nowhere near as robust as their fictional counterparts. They’re quite wobbly, and it takes long hours of handcrafting software policies, which regulate how they work—a process that has to be repeated for each individual user.

To bring the technology a bit closer to Avatar’s Skel Suits or Warhammer 40k power armor, a team at North Carolina University’s Lab of Biomechatronics and Intelligent Robotics used AI to build the first one-size-fits-all exoskeleton that supports walking, running, and stair-climbing. Critically, its software adapts itself to new users with no need for any user-specific adjustments. “You just wear it and it works,” says Hao Su, an associate professor and co-author of the study.

Tailor-made robots

An exoskeleton is a robot you wear to aid your movements—it makes walking, running, and other activities less taxing, the same way an e-bike adds extra watts on top of those you generate yourself, making pedaling easier. “The problem is, exoskeletons have a hard time understanding human intentions, whether you want to run or walk or climb stairs. It’s solved with locomotion recognition: systems that recognize human locomotion intentions,” says Su.

Enlarge (credit: Aurich Lawson | Getty Images)

It's one of the world's worst-kept secrets that large language models give blatantly false answers to queries and do so with a confidence that's indistinguishable from when they get things right. There are a number of reasons for this. The AI could have been trained on misinformation; the answer could require some extrapolation from facts that the LLM isn't capable of; or some aspect of the LLM's training might have incentivized a falsehood.

But perhaps the simplest explanation is that an LLM doesn't recognize what constitutes a correct answer but is compelled to provide one. So it simply makes something up, a habit that has been termed confabulation.

Figuring out when an LLM is making something up would obviously have tremendous value, given how quickly people have started relying on them for everything from college essays to job applications. Now, researchers from the University of Oxford say they've found a relatively simple way to determine when LLMs appear to be confabulating that works with all popular models and across a broad range of subjects. And, in doing so, they develop evidence that most of the alternative facts LLMs provide are a product of confabulation.