Noland Arbaugh's life changed in a fraction of a second in June 2016. He was a 22-year-old student, working at a kids' summer camp in upstate New York, when he went swimming in a lake. He can't tell me exactly what happened, but thinks one of his friends must have accidentally struck him very hard in the side of his head as they ran into the water and plunged beneath the surface. When he woke up face down in the water, unable to move or breathe, Noland immediately knew he was paralysed. He felt no fear at all, he says. "You never know what you're going to do in those high-stress situations. I found out that day that it's hard to shake me. I am very, very calm under pressure." Elon Musk would ultimately turn this quality to his advantage when, after nearly eight years of being quadriplegic, Noland agreed to allow the world's richest man to implant an electronic chip into his brain. In January 2024, Noland became the first human recipient of a brain-computer interface (BCI) developed by Musk's company, Neuralink. If it worked, it would allow him to control a computer using only the power of his mind. Only four months after he first heard about Neuralink, Noland was on an operating table, with a purpose-built robot poised to insert the N1 chip into his motor cortex. The stakes could not have been higher for him: he was risking infection, haemorrhage and brain damage. "My brain is the last part of myself that I really feel I have control over," he tells me from his wheelchair at his kitchen table in Yuma, Arizona.

As intelligent technologies continue to mature and proliferate, companies need to learn how best to navigate a fast-changing competitive landscape. That challenge was the topic of the recent HARMAN Technology Forum, a Las Vegas event that gathered leading thinkers from the technology, automotive, retail, marketing, and cybersecurity communities to talk candidly about the future of intelligent technology--both its promises and its pain points. Data is the fuel propelling intelligent technologies forward. Technologies such as AI and machine learning require data--in massive, mind-bending amounts--to reach their fullest potential. But the sheer volume of that data threatens to overwhelm us unless we can figure out how to decipher what it all means.