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Most of us have at one point or another had a dream in which we could neither speak nor move; waking up from such a nightmare\u2014and remembering what it’s like to be able to use your voice freely\u2014feels like liberation. Now Meta says he’s getting closer to helping people who actually live with the crippling condition communicate with artificial intelligence that decodes brain waves.<\/span><\/p>\n On Monday, the company presented <\/span>Brain2Qwerty v2<\/span><\/a>his latest attempt to translate noisy brain activity into coherent text: think of it as a rudimentary form of algorithmically mediated mind reading. Although the research is still in its early stages, it offers a glimpse into a perhaps not-so-distant future in which patients suffering from anarthria, locked-in syndrome, amyotrophic lateral sclerosis (ALS) and other crippling neurodegenerative disorders will be able to communicate through thought without the need for neuroprosthetics, which usually require extremely invasive, complex and expensive surgery to brain.<\/span><\/p>\n “<\/span>We believe that this research has the potential to make a real difference to the millions of people who suffer from brain injuries that prevent them from communicating,” Meta wrote in his <\/span>announcement<\/span><\/a>. The base code for Brain2Qwerty v2, as well as its predecessor, v1, has been made available online. “<\/span>We hope that this work, carried out in the open, will advance neuroscience to detect, diagnose and treat neurological disorders faster than in silos,” the company wrote, repeating the claim movement<\/a> in the AI \u200b\u200bindustry to give scientists access to open source AI in the name of accelerating the pace of discovery.<\/span><\/p>\n The new training model, conducted at the Basque Center for Cognition, Brain and Language in San Sebasti\u00e1n, Spain, involved nine healthy volunteers aged 25 to 56 who were asked to type more than 2,500 sentences over ten sessions. During these sessions, their brain activity was monitored using magnetoencephalography (MEG), which measures the tiny electrical fields created by neuronal activity in the brain. All of these typed sentences and brain scans then served as the raw training data fed into Brain2Qwerty.<\/span><\/p>\n In its most successful experiment, Brain2Qwerty v2 achieved word accuracy \u2014 meaning that more than half of the sentences that were decoded based on brain activity contained no more than one word error \u2014 of 78%. In contrast, Brain2Qwerty v1 (which was released last year) achieved a score of 48% in the most successful case. <\/span><\/p>\nHow Meta Trained Brain2Qwerty v2<\/h2>\n