Enlarge / The Kalalau Valley between sheer cliffs in the Na Pali Coast State Park on the western shore of the island of Kauai in Hawaii, United States. This view is from the Pihea Trail in the Kokee State Park. (credit: Getty | Jon G. Fuller)

The Hawaiian island of Kauai may not have any spewing lava, but hikers along the magnificent Napali coast have brought their own volcanic action recently, violently hollowing their innards amid the gushing waterfalls and deeply carved valleys.

Between August and early September, at least 50 hikers fell ill with norovirus along the famed Kalalau Trail, which has been closed since September 4 for a deep cleaning. The rugged 11-mile trail runs along the northwest coast of the island, giving adventurers breathtaking views of stunning sea cliffs and Kauai's lush valleys. It's situated just north of Waimea Canyon State Park, also known as the Grand Canyon of the Pacific.

"It’s one of the most beautiful places in the world. I feel really fortunate to be able to be there, and appreciate and respect that land,” one hiker who fell ill in late August told The Washington Post. "My guts exploding all over that land was not what I wanted to do at all."

Enlarge / A woman wearing a sweatshirt for the QAnon conspiracy theory on October 11, 2020 in Ronkonkoma, New York. (credit: Stephanie Keith | Getty Images)

Belief in conspiracy theories is rampant, particularly in the US, where some estimates suggest as much as 50 percent of the population believes in at least one outlandish claim. And those beliefs are notoriously difficult to debunk. Challenge a committed conspiracy theorist with facts and evidence, and they'll usually just double down—a phenomenon psychologists usually attribute to motivated reasoning, i.e., a biased way of processing information.

A new paper published in the journal Science is challenging that conventional wisdom, however. Experiments in which an AI chatbot engaged in conversations with people who believed at least one conspiracy theory showed that the interaction significantly reduced the strength of those beliefs, even two months later. The secret to its success: the chatbot, with its access to vast amounts of information across an enormous range of topics, could precisely tailor its counterarguments to each individual.

"These are some of the most fascinating results I've ever seen," co-author Gordon Pennycook, a psychologist at Cornell University, said during a media briefing. "The work overturns a lot of how we thought about conspiracies, that they're the result of various psychological motives and needs. [Participants] were remarkably responsive to evidence. There's been a lot of ink spilled about being in a post-truth world. It's really validating to know that evidence does matter. We can act in a more adaptive way using this new technology to get good evidence in front of people that is specifically relevant to what they think, so it's a much more powerful approach."

Enlarge (credit: Aurich Lawson | Getty Images)

As far back as 2016, work on AI-based chatbots revealed that they have a disturbing tendency to reflect some of the worst biases of the society that trained them. But as large language models have become ever larger and subjected to more sophisticated training, a lot of that problematic behavior has been ironed out. For example, I asked the current iteration of ChatGPT for five words it associated with African Americans, and it responded with things like "resilience" and "creativity."

But a lot of research has turned up examples where implicit biases can persist in people long after outward behavior has changed. So some researchers decided to test whether the same might be true of LLMs. And was it ever.

By interacting with a series of LLMs using examples of the African American English sociolect, they found that the AI's had an extremely negative view of its speakers—something that wasn't true of speakers of another American English variant. And that bias bled over into decisions the LLMs were asked to make about those who use African American English.

Enlarge / For now, "you should see a doctor" remains good advice.

ChatGPT was able to pass some of the United States Medical Licensing Exam (USMLE) tests in a study done in 2022. This year, a team of Canadian medical professionals checked to see if it’s any good at actual doctoring. And it’s not.

ChatGPT vs. Medscape

“Our source for medical questions was the Medscape questions bank,” said Amrit Kirpalani, a medical educator at the Western University in Ontario, Canada, who led the new research into ChatGPT’s performance as a diagnostic tool. The USMLE contained mostly multiple-choice test questions; Medscape has full medical cases based on real-world patients, complete with physical examination findings, laboratory test results, and so on.

The idea behind it is to make those cases challenging for medical practitioners due to complications like multiple comorbidities, where two or more diseases are present at the same time, and various diagnostic dilemmas that make the correct answers less obvious. Kirpalani’s team turned 150 of those Medscape cases into prompts that ChatGPT could understand and process.

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 (credit: BlackJack3D/Getty Images)

Advances in artificial intelligence are making it increasingly difficult to distinguish between uniquely human behaviors and those that can be replicated by machines. Should artificial general intelligence (AGI) arrive in full force—artificial intelligence that surpasses human intelligence—the boundary between human and computer capabilities will diminish entirely.

In recent months, a significant swath of journalistic bandwidth has been devoted to this potentially dystopian topic. If AGI machines develop the ability to consciously experience life, the moral and legal considerations we’ll need to give them will rapidly become unwieldy. They will have feelings to consider, thoughts to share, intrinsic desires, and perhaps fundamental rights as newly minted beings. On the other hand, if AI does not develop consciousness—and instead simply the capacity to out-think us in every conceivable situation—we might find ourselves subservient to a vastly superior yet sociopathic entity.

Neither potential future feels all that cozy, and both require an answer to exceptionally mind-bending questions: What exactly is consciousness? And will it remain a biological trait, or could it ultimately be shared by the AGI devices we’ve created?

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 / A movable robotic face covered with living human skin cells. (credit: Takeuchi et al.)

In a new study, researchers from the University of Tokyo, Harvard University, and the International Research Center for Neurointelligence have unveiled a technique for creating lifelike robotic skin using living human cells. As a proof of concept, the team engineered a small robotic face capable of smiling, covered entirely with a layer of pink living tissue.

The researchers note that using living skin tissue as a robot covering has benefits, as it's flexible enough to convey emotions and can potentially repair itself. "As the role of robots continues to evolve, the materials used to cover social robots need to exhibit lifelike functions, such as self-healing," wrote the researchers in the study.

Shoji Takeuchi, Michio Kawai, Minghao Nie, and Haruka Oda authored the study, titled "Perforation-type anchors inspired by skin ligament for robotic face covered with living skin," which is due for July publication in Cell Reports Physical Science. We learned of the study from a report published earlier this week by New Scientist.

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.