New research shows that scientists have created a language decoder that can translate human thoughts into text using an artificial intelligence converter (AI) Looks like ChatGPT. This innovation marks the first time that continuous language has been reconstructed from human brain activity non-invasively, as read by a functional magnetic resonance imaging (fMRI) machine.

The decoder was able to interpret the gist of the stories the subjects watched, heard, or even imagined using fMRI brain samples, essentially allowing it to read people’s minds with unprecedented efficiency. While this technology is still in its early stages, scientists hope that one day it could help people with neurological conditions that affect speech communicate clearly with the outside world.

However, the team behind the decoder also warned that brain-reading platforms could end up with malicious applications, including as a surveillance tool for governments and employers. Although the researchers stressed that their decoder required human cooperation to work, they argued that “brain-computer interfaces must respect mental privacy“, according to a study published Monday in Nature Neuroscience.

“Language decoding is currently done with implanted devices that require neurosurgical intervention, and our study is the first to decode continuous language, meaning more than words or full sentences, from non-invasive brain recordings that we have collected using fMRI.Jerry Tang, a computer science graduate student at the University of Texas at Austin who led the study, said at a press conference Thursday.

“The purpose of language decoding is to record the user’s brain activity and predict the words the user has heard, spoken, or imagined.“, he noted.”Ultimately, we hope this technology can help people who have lost the ability to speak due to injuries such as stroke or diseases such as amyotrophic lateral sclerosis (ALS).“.

Tan and his colleagues were able to build their decoder with the help of three human participants, each of whom spent 16 hours in an fMRI machine listening to stories. The researchers trained an AI model called GPT-1 in a study of Reddit comments and autobiographical stories to link the semantic characteristics of recorded stories to neural activity captured in fMRI data. In this way, it can learn which words and phrases are associated with certain brain patterns.

How does the system work?

After the initial phase of the experiment, participants scanned their brains with fMRI while listening to new stories that were not part of the training data set. The decoder was able to translate the audio stories into text as participants listened to them, although these interpretations often used different semantic constructs than the original recordings. For example, a recording of someone saying the phrase “I still don’t have a driver’s license” was decoded from the listener’s thoughts using an fMRI scanner in “She hasn’t even started learning to drive yet.“

These approximate translations arise from a fundamental difference between the new decoder and existing methods using invasive electrodes implanted in the brain. Electrode-based platforms typically predict text based on motor activity, such as the movements of a person’s mouth when they try to speak, while Tang’s team focused on blood flow through the brain, which is captured by fMRI machines.

“Our system works on a completely different level.“said Alexander Huth, assistant professor of neuroscience and computer science at UT Austin and senior author of the new study, at a press conference.”Instead of looking at this low-level motorized thing, our system actually works at the level of ideas, semantics, and meaning. That’s what he’s up to.“

“That is why I believe that we are not getting the exact words that someone heard or said, but the essence“, continuation.”It is the same idea, but expressed in different words.“

The innovative approach allowed the team to push the boundaries of mind-reading technology by testing whether a decoder could translate participants’ thoughts when they were watching silent films or simply imagining stories in their heads. In both cases, the decoder was able to decipher what the participants saw, in the case of films, and what they thought while telling short stories in their imagination.

The decoder produced more accurate results during tests with audio recordings than with imaginary speech, but was still able to extract some basic details of unspoken thoughts from brain activity. For example, when the subject imagined the sentence “I was walking along a dirt road through a wheat field, across a stream and past some wooden buildings,” the decoder produced a text that said: “He had to cross the bridge to the other side and a large building on the horizon “.

The study participants completed all of these tests inside an fMRI machine, which is bulky, immobile laboratory equipment. For this reason, the decoder is not yet ready for the practical treatment of patients with speech disorders, although Tang and his colleagues hope that future versions of the device can be adapted to more convenient platforms such as near-infrared spectroscopy (fNIRS) sensors. which can be used on the patient’s head.

While the researchers mentioned the technology’s promise as a new means of communication, they also warned that scramblers raise ethical privacy concerns.

“Our privacy analysis shows that subject cooperation is currently required for both learning and applying the decoder.‘, says the study by Tan’s team.However, future developments may allow decoders to circumvent these requirements. In addition, even if the decoder’s predictions are inaccurate without the participation of the subject, they can be deliberately misinterpreted for malicious purposes.“

“For these and other unforeseen reasons, it is critical to raise awareness of the risks associated with brain-encrypting technologies and to implement policies that protect everyone’s privacy.“, the researchers concluded.

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Source: Motherboard

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