AI tagged posts

William Overman began his Ph.D. program at Stanford Graduate School of Business at an auspicious moment: just two months before ChatGPT launched publicly in November 2022, exploding the widely held understanding of what machines are capable of.

Even as Overman began enlisting AI for his research, he grew wary of where the technology was headed. “This isn’t only about the apocalyptic potential of what could happen; I’m also thinking a lot about the future of human flourishing,” Overman says. He fears that misaligned AI could overstep its bounds—not necessarily maliciously—and inflict subtle yet real harms on people.

“To prevent that, we need to get this right,” Overman says.

“We must set up the proper interactions and training and incentive...

There may soon be a new kind of artificial intelligence in town, one that uses a novel approach to thinking that could save massive amounts of computing power and money.

The AI that most people use every day, like ChatGPT, Claude or Google Gemini, relies on large language models that can work through difficult problems step by step. While this may ultimately give us the answers we are searching for, the process can increase response times and use a lot of computing power.

During the day, our brain acquires new memories; at night, during sleep, it consolidates the important ones and eliminates the useless ones. A similar principle has been applied to Hopfield networks, one of the classic models of artificial intelligence inspired by the workings of the brain. In 2025, Federico Ricci-Tersenghi and colleagues developed Daydreaming, an algorithm that combines the learning of new memories with the elimination of spurious ones, drastically improving the network’s capacity.

One limitation remained, however. These networks lose effectiveness when they work with real-world data, which are rarely perfectly balanced—for example, very bright or very dark images, in which white or black pixels overwhelmingly dominate...

Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with each other. While biological neurons exchange information in the form of electrical impulses, SNNs rely on brief signals known as spikes.

SNNs have proved promising for reducing power consumption, as developers can ensure they do not process information continuously, but rather ...