Can AI read Minds?

AI or artificial intelligence commonly recognized by a free tool ChatGPT to easy people’s efforts by automating task. The AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics. General intelligence (the ability to complete any task performable by a human) is among the field’s long-term goals. AI researchers have adapted and integrated a wide range of problem-solving techniques, including formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. AI has been quite beneficial in fields like psychology, linguistics, philosophy, neuroscience and many other fields.

The question arises here can ai read human minds?

Before going further let’s understand what is meant by reading minds. Here reading minds is to predict the behavior from the data here data is pattern of thoughts. Basically, it analyses pattern from data to predict the behavior. It converts linguistic input into meaningful pattern using machine learning and decoder. So, the thoughts are linguistic input which are converted into text using machine learning and decoder.

While doing this it is necessary to know that it does not have access to human thoughts like our brain. But analyses the pattern of thought.

Sematic decoder:

A new artificial intelligence system called a semantic decoder can translate a person’s brain activity — while listening to a story or silently imagining telling a story — into a continuous stream of text. The system developed by researchers at The University of Texas at Austin. This system does not require subjects to have surgical implants, making the process noninvasive. Participants also do not need to use only words from a prescribed list. Brain activity is measured using an fMRI scanner after extensive training of the decoder, in which the individual listens to hours of podcasts in the scanner. Later, provided that the participant is open to having their thoughts decoded, their listening to a new story or imagining telling a story allows the machine to generate corresponding text from brain activity alone.

The result is not a word-for-word transcript. Instead, researchers designed it to capture the gist of what is being said or thought, albeit imperfectly. About half the time, when the decoder has been trained to monitor a participant’s brain activity, the machine produces text that closely (and sometimes precisely) matches the intended meanings of the original words.

For example, a participant listening to a speaker say, “I don’t have my driver’s license yet” had their thoughts translated as, “She has not even started to learn to drive yet.”

Data collected from the survey this image shows decoder predictions from brain recordings collected while a user listened to four stories. Example segments were manually selected and annotated to demonstrate typical decoder behaviors. The decoder exactly reproduces some words and phrases and captures the gist of many more. (Image source: google)

Accuracy of the system

The accuracy of a semantic decoder can vary based on several factors like the quality and quantity of training data, the complexity of the language being analyzed, and the specific task it’s performing. State-of-the-art semantic decoders using advanced machine learning models often achieve high accuracy, especially in well-defined tasks like sentiment analysis or language translation. However, nuances, ambiguity in language, or understanding context in certain situations can still pose challenges, leading to potential errors or lower accuracy. Constant advancements in AI technologies aim to improve accuracy levels by refining models and enhancing training data quality.

In many cases, these models achieve accuracies upwards of 90% or more in controlled environments or specific tasks where they’ve been extensively trained. However, in more complex or nuanced tasks, accuracies may vary and might not always reach such high percentages. It’s essential to consider that accuracy can fluctuate and isn’t a one-size-fits-all measure across different applications or contexts.

Application of the decoder

The system currently is not practical for use outside of the laboratory because of its reliance on the time need on an fMRI machine. But the researchers think this work could transfer to other, more portable brain-imaging systems, such as functional near-infrared spectroscopy (fNIRS).

“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 is translated to fNIRS.

A semantic decoder, could ultimately benefit patients who have lost their ability to physically communicate after suffering from a stroke, paralysis or other degenerative diseases.

Help people who are mentally conscious yet unable to physically speak, such as those debilitated by strokes, to communicate intelligibly again.