Researchers from the University of California, San Francisco, have developed a brain implant which uses deep-learning artificial intelligence to transform thoughts into complete sentences. The technology could one day be used to help restore speech in patients who are unable to speak due to paralysis.
“The algorithm is a special kind of artificial neural network, inspired by work in machine translation,” Joseph Makin, one of the researchers involved in the project, told Digital Trends. “Their problem, like ours, is to transform a sequence of arbitrary length into a sequence of arbitrary length.”
The neural net, Makin explained, consists of two stages. In the first, the neural data gathered from brain signals, captured using electrodes, is transformed into a list of numbers. This abstract representation of the data is then decoded, word by word, into an English language sentence. The two stages are trained together, not separately, to achieve this task. The words are finally outputted as text — although it would be equally possible to output it as speech using a text-to-speech converter.
For the study, four women with epilepsy, who had previously had electrodes attached to their brains to monitor for seizures, tested out the mind-reaching tech. Each participant was asked to repeat sentences, allowing the A.I. to learn and then demonstrate its ability to decode thoughts into speech. The best performance had an average translation rate error of only 3%.
Currently the A.I. has a vocabulary of around 250 words. By comparison, the average American adult native English speaker has a vocabulary of somewhere between 20,000 and 35,000 words. So if the researchers are going to make this tool as valuable as it could be, they will need to vastly scale up the number of words it can identify and verbalize.
“The algorithms for natural-language processing, including machine translation, have advanced quite a bit since I conceived the idea for this decoder in 2016,” Makin continued. “We’re investigating some of these now. [In order to] achieve high-quality decoding over a broader swath of English, we need to collect more data from a single subject — or somehow get even bigger boosts from our transfer learning.”
A paper describing the work was recently published in the journal Nature Neuroscience.
San Francisco startup Dimensional advertised for people to walk robot dogs through city streets. The New York Times reported that the job could include carrying a roughly 33-pound robot back to the office if it overheated. A lift home is an awkward requirement for a labor-saving machine.
Keeping the hardware running is only one challenge. Anthropic's robotics tests found that language models mostly failed when controlling joints directly, but did better when directing trained controllers. Giving an AI agent a body doesn't teach it to handle ordinary household objects.
Soon, a mattress protector may be more than a bed cover. Australian medtech company Sleeptite and researchers at RMIT University have received a US patent for FlexiSense, a smart bedding technology that tracks body position and posture to help caregivers identify when older residents may be at risk of falling.
The technology is being developed for Sleeptite’s in-bed monitoring system, REMi, and was recently on display at The MedTech Conference in Boston. A peer-reviewed study in Flexible and Printed Electronics says the sensors can also be made at scale without sacrificing performance or durability.
AI companies spend enormous amounts of time convincing us their technology is safe and secure. But what happens when those same companies become targets of increasingly sophisticated AI-powered attacks? Anthropic, the company behind Claude, is now facing that question. According to a Washington Post report, cybersecurity startup Cathedral discovered vulnerabilities that let its researchers access sensitive information in Anthropic's internal systems.
The researchers used AI agents to identify potential entry points in less than 12 hours. Interestingly, they didn't need to crack Claude itself. Instead, they exploited weaknesses in third-party business applications that Anthropic uses for everyday operations. Anthropic's security testing program disclosed the findings, which allows outside researchers to identify and report vulnerabilities.