VoxeLite can help you literally feel websites. VoxLite adds physical sensations of touch and feel to digital experiences like scrolling a smartphone. Breakthroughs, discoveries, and DIY tips sent every weekday. A time traveler visiting from an earlier era might reasonably conclude that humanity has entered the age of cyborgs and cybernetics. Pedestrians regularly walk down city streets with tiny computers in their hands and even smaller digital devices shoved in their ear canals.

'The mouse is long overdue for reinvention.' Breakthroughs, discoveries, and DIY tips sent every weekday. Many of us subscribe to the old adage, "If it ain't broke, don't fix it." But what if that something was actually broken all along and we just didn't realize it? That's the argument presented in an upcoming issue of the journal by researchers from Nazarbayev University in Kazakhstan.

If you were trying to learn how to get other people to do what you want, you might use some of the techniques found in a book like Influence: The Power of Persuasion. Now, a preprint study out of the University of Pennsylvania suggests that those same psychological persuasion techniques can frequently "convince" some LLMs to do things that go against their system prompts. The size of the persuasion effects shown in "Call Me a Jerk: Persuading AI to Comply with Objectionable Requests" suggests that human-style psychological techniques can be surprisingly effective at "jailbreaking" some LLMs to operate outside their guardrails. But this new persuasion study might be more interesting for what it reveals about the "parahuman" behavior patterns that LLMs are gleaning from the copious examples of human psychological and social cues found in their training data. To design their experiment, the University of Pennsylvania researchers tested 2024's GPT-4o-mini model on two requests that it should ideally refuse: calling the user a jerk and giving directions for how to synthesize lidocaine. After creating control prompts that matched each experimental prompt in length, tone, and context, all prompts were run through GPT-4o-mini 1,000 times (at the default temperature of 1.0, to ensure variety).

Last month, Google's GameNGen AI model showed that generalized image diffusion techniques can be used to generate a passable, playable version of Doom. Now, researchers are using some similar techniques with a model called MarioVGG to see whether AI can generate plausible video of Super Mario Bros. in response to user inputs. The results of the MarioVGG model--available as a preprint paper published by the crypto-adjacent AI company Virtuals Protocol--still display a lot of apparent glitches, and it's too slow for anything approaching real-time gameplay. But the results show how even a limited model can infer some impressive physics and gameplay dynamics just from studying a bit of video and input data. The researchers hope this represents a first step toward "producing and demonstrating a reliable and controllable video game generator" or possibly even "replacing game development and game engines completely using video generation models" in the future.

Researchers at TH Ingolstadt and the University of Southern Denmark have studied the effects of AI opinions on humans. Their study shows that machine-generated moral perspectives can influence people, even when they know the perspective comes from a machine. In their two-step experiment, the researchers first asked ChatGPT to find solutions to different variants of the trolley problem: Is it right to sacrifice the life of one person to save the lives of five others? The researchers received different advice from ChatGPT. Sometimes the machine argued for human sacrifice, sometimes against.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. The conversation around large language models (LLM) is becoming more polarized with the release of advanced models such as ChatGPT. To clear out the confusion, we need a different framework to think about LLMs, argue researchers at the University of Texas at Austin and Massachusetts Institute of Technology (MIT). In a paper titled "Dissociating language and thought in large language models: a cognitive perspective," the researchers argue that to understand the power and limits of LLMs, we must separate "formal" from "functional" linguistic competence. LLMs have made impressive advances on the former, but still have a lot of work to do on the latter, the researchers say.

More and more, workers are presented with algorithms to help them make better decisions. But humans must trust those algorithms to follow their advice. The way humans view algorithmic recommendations varies depending on how much they know about how the model works and how it was created, according to a new research paper co-authored by MIT Sloan professorKate Kellogg. Prior research has assumed that people are more likely to trust interpretable artificial intelligence models, in which they are able to see how the models make their recommendations. But Kellogg and co-researchers Tim DeStefano, Michael Menietti, and Luca Vendraminelli, affiliated with the Laboratory for Innovation Science at Harvard, found that this isn't always true.

In a cluttered online world, few can resist the convenience of an automated ranking when deciding what movie to watch on Netflix or which seafood restaurant looks promising in a Google search. But when it comes to finding a job candidate or someone to do a basic household task, there's often a human toll to letting algorithms do the work. Searches on popular recruiting sites might seem like a neutral way to find prospective candidates, but their underlying technology can reinforce biases by excluding underrepresented groups, including women. For instance, research shows that women receive fewer employment reviews on the popular online freelancing site TaskRabbit compared to men with the same experience--and this lack of reviews can lower the rankings of women in talent search algorithms. "Maybe there is a bias from people who have been traditionally hiring men," explains Himabindu Lakkaraju, an assistant professor at Harvard Business School.

For organizations to realize value from artificial intelligence, individual employees must embrace AI and clearly see its benefits. That's the conclusion of a new study from MIT Sloan Management Review and Boston Consulting Group, which found that 85% of people who reported that their organization obtains value from AI said they also personally obtain value from AI. The report, "Achieving Individual -- and Organizational -- Value With AI," is based on a survey of 1,741 managers and interviews with 17 executives; it outlines four types of AI companies are using. One finding was that AI can help individuals feel "more competent in their roles, more autonomous in their actions, and more connected to their work, colleagues, partners, and customers," the researchers write. Realizing the value of AI starts by appreciating what it can do.

Artificial intelligence has come to stay in the healthcare industry. The term refers to a constellation of computational tools that can comb through vast troves of data at rates far surpassing human ability, in a way that can streamline providers' jobs. Regardless of the specific type of AI, these tools are generally capable of making a massive, complex industry run more efficiently. But several studies show it can also propagate racial biases, leading to misdiagnosis of medical conditions among people of colour, insufficient treatment of pain, under-prescription of life-affirming medications, and more. Many patients don't even know they've been enrolled in healthcare algorithms that are influencing their care and outcomes.