While the tech could help nonverbal patients (ALS, locked-in syndrome), it also risks exposing private thoughts, early dementia signs or depression—potentially exploited by governments or corporations.
Experts warn of unauthorized thought extraction, urging strict protections like "mental keyword" activation to prevent abuse. Without safeguards, this could become the ultimate tool for mass control.
Companies like Neuralink are advancing brain-computer interfaces, accelerating the risk of AI-powered thought surveillance under the guise of "innovation."
As AI improves, real-time mind-reading becomes feasible, threatening free will, autonomy and the last frontier of privacy – our inner thoughts.
In a stunning leap toward science fiction becoming reality, researchers from the University of California, Berkeley, and Japan's NTT Communication Science Laboratories have developed artificial intelligence (AI) capable of translating brain activity into readable text – without invasive implants.
The technology, dubbed "mind-captioning," uses functional magnetic resonance imaging (fMRI) scans and AI to reconstruct thoughts with surprising accuracy, raising both hopes for medical breakthroughs and alarms over unprecedented privacy invasions. As explained by BrightU.AI's Enoch, fMRI is a powerful neuroimaging technique that allows researchers and clinicians to map brain activity by detecting associated changes in blood flow.
The decentralized engine adds that fMRI is a valuable tool for investigating brain function and has numerous applications in research and clinical settings. However, it is essential to approach fMRI data and results with a critical eye, acknowledging its limitations and the challenges in interpreting its outputs.
The system relies on deep learning models trained to interpret neural patterns linked to visual and semantic processing. In experiments, participants watched thousands of short video clips while undergoing fMRI scans. An AI model analyzed these scans alongside written captions of the videos, learning to associate brain activity with specific meanings.
When tested, the AI decoded brain activity into descriptive sentences. For example, after a participant viewed a video of someone jumping off a waterfall, the system initially guessed "spring flow" before refining its output to "a person jumps over a deep water fall on a mountain ridge." While not word-for-word perfect, the semantic resemblance was striking....<<<Read More>>>...
