{"id":78163,"date":"2023-05-02T15:13:54","date_gmt":"2023-05-02T12:13:54","guid":{"rendered":"https:\/\/forklog.com\/en\/?p=78163"},"modified":"2025-09-11T03:26:12","modified_gmt":"2025-09-11T00:26:12","slug":"ai-learns-to-turn-human-thoughts-into-text","status":"publish","type":"post","link":"https:\/\/forklog.com\/en\/ai-learns-to-turn-human-thoughts-into-text\/","title":{"rendered":"AI learns to turn human thoughts into text"},"content":{"rendered":"

Researchers at the University of Texas at Austin have developed a non-invasive AI system that translates human brain activity into a stream of text. The study results were published in the journal Nature Neuroscience<\/a>.<\/p>\n

To collect brain activity data, participants were placed in an \u0444\u041c\u0420\u0422<\/a> scanner and were allowed to listen to several hours of podcasts. Next, the researchers created a decoder algorithm that operates on the principle of chatbots such as ChatGPT or Bard.<\/p>\n

In the end, the trained AI system was able to generate a stream of text when a participant listens to recordings or imagines telling a new story.<\/p>\n

The produced text is not an exact transcript. According to the researchers, the algorithm is more likely to capture general thoughts and ideas.<\/p>\n

According to the press release<\/a>, the trained system yields text that closely or exactly matches the intended meaning of the participant’s original words in about half the cases.<\/p>\n

For example, when a participant heard the words \\”I still haven’t obtained a driver’s license yet\\” during the experiment, the thoughts were transformed into \\”she hasn’t even started learning to drive yet\\”.<\/p>\n

\n

\\”For a non-invasive method, this is a real leap forward compared with what was done previously, when typically individual words or short phrases were used,\\” said one of the study’s leaders, Alexander Khut.<\/p>\n<\/blockquote>\n

In his view, the decoding model can sustain lengthy sessions and handle complex ideas.<\/p>\n

Participants were also asked to watch four silent videos while in the scanner. As a result, the AI system was able to accurately recognise \\”specific events\\” from the clips, the researchers said.<\/p>\n

The brain activity decoder cannot be used outside the laboratory because it requires an \u0444\u041c\u0420\u0422<\/a> scanner. But researchers believe the algorithm will be useful in the future when more portable brain-imaging systems become available.<\/p>\n

The developers also believe the technology will benefit patients who have lost the ability to communicate physically after a stroke, paralysis, or other degenerative diseases.<\/p>\n

The leading authors of the study intend to patent the technology.<\/p>\n

In March, Japanese researchers taught AI to recreate images from brain activity data using Stable Diffusion.<\/p>\n","protected":false},"excerpt":{"rendered":"

Researchers at the University of Texas at Austin have developed a non-invasive AI system that converts brain activity into a stream of text.<\/p>\n","protected":false},"author":1,"featured_media":78164,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"select":"1","news_style_id":"1","cryptorium_level":"","_short_excerpt_text":"","creation_source":"","_metatest_mainpost_news_update":false,"footnotes":""},"categories":[3],"tags":[438,1760,167],"class_list":["post-78163","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-and-analysis","tag-artificial-intelligence","tag-generative-ai","tag-research"],"aioseo_notices":[],"amp_enabled":true,"views":"23","promo_type":"1","layout_type":"1","short_excerpt":"","is_update":"","_links":{"self":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts\/78163","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/comments?post=78163"}],"version-history":[{"count":1,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts\/78163\/revisions"}],"predecessor-version":[{"id":78165,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts\/78163\/revisions\/78165"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/media\/78164"}],"wp:attachment":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/media?parent=78163"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/categories?post=78163"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/tags?post=78163"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}