Artificial intelligence — While Artificial intelligence has recently gained traction in technology, it is also gaining traction in science.
Artificial intelligence is being researched by scientists from a variety of disciplines.
For example, a peer-reviewed study published in the Monday issue of Nature Neuroscience magazine described how it may be applied to brain activity.
Scientists developed a noninvasive Artificial intelligence system that can convert people’s brain activity into a stream of text, according to the research.
Artificial intelligence & neuroscience
Artificial intelligence can improve neuroscience by improving the efficiency and precision of large-scale neuroscience dataset evaluation.
It has the promise of producing more accurate models of neural systems and processes.
Artificial intelligence can also assist in the development of innovative neurological diagnostic and therapeutic methods.
The system is known as a semantic decoder.
It may be beneficial to persons who have lost their physical ability to communicate as a result of a stroke, paralysis, or other degenerative illnesses.
The technique was developed by academics at the University of Texas in Austin using a transformer model.
The transformer concept is comparable to that of OpenAI’s ChatGPT and Google’s Bard.
The most recent study’s participants learned how to utilize an fMRI machine’s decoder by listening to hours of podcasts.
It’s also a larger piece of equipment used to monitor brain activity.
A surgical implant is not required for the semantic decoder.
Artificial intelligence may aid neuroscience in establishing techniques for thoughts to become text by using machine learning algorithms to investigate brain activity patterns connected to language processing.
By analyzing patterns of brain activity and then utilizing this information to create corresponding text output, Artificial intelligence systems may distinguish specific words or phrases that a person is thinking about.
This technology has the potential to revolutionize communication for individuals who are unable to speak or type, such as those suffering from severe paralysis or communication issues.
More research is needed, however, to improve these systems’ precision and dependability, as well as take on the ethical and privacy concerns associated with accessing and interpreting people’s thoughts.
The Artificial intelligence system creates a stream of text when people listen to or anticipate hearing a new tale.
Although the text created was not an exact transcription, the researchers wanted it to express key principles or ideas.
According to a recent news release, the trained system produces language that around half of the time closely fits the intended context of the participant’s original thinking.
When a research participant hears the words “I don’t have my driver’s license yet,” their immediate impression is “She hasn’t even begun to learn to drive yet.”
The absence of implants
Alexander Huth, one of the study’s major researchers, stated:
“For a noninvasive method, this is a real leap forward compared to what’s been done before, which is typically single words or short sentences.”
“We’re getting the model to decode continuous language for extended periods of time with complicated ideas.”
The semantic decoder, unlike earlier decoding systems under development, does not require surgical implants, making it noninvasive.
Furthermore, participants aren’t obligated to use only terms from a specified list.
Concerns regarding the technology’s potential misuse were also addressed by the researchers.
The researchers discovered that decoding only worked when volunteers offered to educate the decoder.
Individuals who did not use the decoder produced results that were incomprehensible.
People who used the decoder but showed resistance produced ineffective results.
“We take very seriously the concerns that it could be used for bad purposes and have worked to avoid that,” said researcher Jerry Tang.
“We want to make sure people only use these types of technologies when they want to and that it helps them.”
An fMRI machine can only be utilized in the laboratory due to the time required.
The findings might be extended to other, more portable brain-imaging methods, such as functional near-infrared spectroscopy (fNIRS), according to the researchers.
“fNIRS measures where there’s more or less blood flow in the brain at different points in time, which, it turns out, is exactly the same kind of signal that fMRI is measuring,” said Huth.
“So, our exact kind of approach should translate to fNIRS.”