AI now has the ability to convert “non-invasive” thoughts into texts. According to the latest report, researchers are leading the way towards this achievement.
Artificial intelligence (AI) is increasingly being used to translate brain activity into a continuous stream of text. This has the potential to revolutionize communication for those with severe neurological conditions. Artificial intelligence has great potential in interpreting brain activity, particularly in the context of neuroimaging techniques.
Take advantage of new opportunities with artificial intelligence
In its latest development, an AI-based semantic decoder has demonstrated innovative ways of translating brain activities into a continuous stream of texts. For the first time, this breakthrough will allow “non-invasive” ideas or turn them into scripts. This can be of great help to those who are struggling to communicate after a stroke or motor neurone disease.
Interpreting brain activity requires sophisticated data analysis techniques to extract meaningful information from complex and noisy data. AI algorithms can help automate and simplify this process. This allows researchers to make more accurate and reliable inferences about brain function.
Here, the decoder can accurately reconstruct speech as respondents listen to or imagine a story. Already a huge leap in innovation compared to past Language decoding systems that incorporated surgical implants.
Renowned scientists supported the latest development as it overcame the major hurdle. Leading the research is Dr. Alexander Huth, a neuroscientist at the University of Texas who added:
“For a non-invasive method, this is a real leap forward compared to what has been done before, which was usually single words or short sentences.”
AI overcome setbacks
Functional magnetic resonance imaging (fMRI) measures changes in blood flow to different regions of the brain, which can be used to infer neural activity. However, this process is relatively slow compared to the actual firing of neurons in the brain. The time resolution of fMRI is usually on the order of seconds, which means it cannot capture rapid changes in brain activity. This makes it difficult to analyze brain activity in response to “normal speech” because it gives off a “jumble of information” spread out over a few seconds, according to an article in The Guardian.
The emergence of large language models such as OpenAI’s ChatGPT was an important development in AI. These models are trained on vast amounts of textual data, enabling them to generate human-like responses to a wide variety of inputs. In this case, it allowed the researchers to look at the semantic meaning of the utterances. That is, to understand the activity patterns of neurons corresponding to a series of words.
Following this breakthrough, the group in question aims to advance the utility of using this technology in other, more portable brain imaging systems, such as functional infrared spectroscopy (fNIRS).
But again, security concerns may arise after the emergence of the latest innovations.
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