Neuroscientists at the University of Texas in Austin have made a significant breakthrough by using artificial intelligence (AI) powered ChatGPT to translate brain activity into words. This discovery has the potential to greatly benefit patients suffering from conditions such as “locked-in” syndrome and stroke, which leave them unable to communicate effectively.

The researchers leveraged OpenAI’s advanced chatbot technology, which has demonstrated its applications in various sectors, including healthcare. The integration of AI into our daily lives is steadily advancing, and this development showcases its potential in the field of neuroscience.

Alexander Huth, an assistant professor of neuroscience and computer science at the University of Texas, emphasized that the term “mind reading” is inaccurate and misleading, as it implies capabilities that are beyond our current reach.


To conduct their study, Professor Huth spent 20 hours inside an fMRI (functional magnetic resonance imaging) machine while listening to audio clips. The machine captured detailed snapshots of his brain activity, which were then analyzed by the AI system. Through this analysis, the technology was able to predict the words Professor Huth was hearing solely by monitoring his brain activity.

The researchers utilized OpenAI’s chatGPT-1 model, which has been trained on a vast database of books and websites. They found that the AI system accurately predicted participants’ auditory and visual experiences based on their mental activity.

While still in its early stages, this technology holds promise, particularly in assisting individuals who have lost the ability to communicate. Professor Huth explained that the true potential application lies in aiding patients with conditions such as “locked-in” syndrome and stroke, whose brains are functional but lack the ability to speak.

Importantly, this breakthrough demonstrates the achievement of high accuracy levels without the need for invasive brain surgery. The researchers believe this marks the first step toward helping individuals regain their ability to communicate without resorting to neurosurgery.


However, the technology’s results have also raised concerns regarding its potential use in controversial contexts. The researchers highlight the importance of obtaining consent from subjects and conducting brain scans within an fMRI machine. Additionally, the AI technology requires extensive training on an individual’s brain for accurate predictions to be made.

Jerry Tang, the lead author of the research paper, emphasizes the need for safeguarding the privacy of brain data. He asserts that everyone’s brain data should be kept private, as our thoughts represent one of the last frontiers of personal privacy. Tang acknowledges the potential misuse of brain decoding technology and emphasizes the importance of legislators taking mental privacy seriously.

Professor Huth clarifies that the technology can discern the general ideas and narratives individuals have in mind, effectively capturing internal storytelling. However, Tang warns against complacency, highlighting that technology is continually evolving, which could impact the accuracy of decoding methods and the extent to which an individual’s cooperation is required.

In summary, the use of AI to translate brain activity into words has emerged as a groundbreaking discovery by neuroscientists. Although promising, further development and considerations regarding privacy and ethical use are necessary before widespread implementation can occur.