Well, the technology is no longer just “coming.” It’s here. With the help of AI, scientists from the University of Texas at Austin have developed a technique that can translate people’s brain activity — like the unspoken thoughts swirling through our minds — into actual speech, according to a study published in Nature. In the past, researchers have shown that they can decode unspoken language by implanting electrodes in the brain and then using an algorithm that reads the brain’s activity and translates it into text on a computer screen. But that approach is very invasive, requiring surgery. It appealed only to a subset of patients, like those with paralysis, for whom the benefits were worth the costs. So researchers also developed techniques that didn’t involve surgical implants. They were good enough to decode basic brain states, like fatigue, or very short phrases — but not much more. Now we’ve got a non-invasive brain-computer interface (BCI) that can decode continuous language from the brain, so somebody else can read the general gist of what we’re thinking even if we haven’t uttered a single word. How is that possible? It comes down to the marriage of two technologies: fMRI scans, which measure blood flow to different areas of the brain, and large AI language models, similar to the now-infamous ChatGPT. In the University of Texas study, three participants listened to 16 hours of storytelling podcasts like The Moth while scientists used an fMRI machine to track the change in blood flow in their brains. That data allowed the scientists, using an AI model, to associate a phrase with how each person’s brain looks when it hears that specific phrase. Because the number of possible word sequences is so vast, and many of them would be gibberish, the scientists also used a language model — specifically, GPT-1 — to narrow down possible sequences to well-formed English and predict which words are likeliest to come next in a sequence. The result is a decoder that gets the gist right, even though it doesn’t nail every single word. For example, participants were asked to imagine telling a story while in the fMRI machine. Later, they repeated it aloud so the scientists could see how well the decoded story matched up with the original. When the participant thought, “Look for a message from my wife saying that she had changed her mind and that she was coming back,” the decoder translated: “To see her for some reason I thought she would come to me and say she misses me.” Here’s another example. When the participant thought, “Coming down a hill at me on a skateboard and he was going really fast and he stopped just in time,” the decoder translated: “He couldn’t get to me fast enough he drove straight up into my lane and tried to ram me.” It’s not a word-for-word translation, but much of the general meaning is preserved. This represents a breakthrough that goes well beyond what previous brain-reading tech could do — and one that raises serious ethical questions.