4 - 6 minute read
Researchers at the University of Texas at Austin have made a significant breakthrough in the field of artificial intelligence (AI) that could have far-reaching implications for the future of technology. The team has developed an AI system that can decode a person’s thoughts and translate them into words in real-time, a feat that has been attempted before but with limited success. The researchers achieved this breakthrough by training a neural network to decode fMRI signals from multiple areas of the human brain simultaneously, allowing them to non-invasively translate human thoughts into words.
Current methods for decoding thought into words are either invasive or limited, as they can only identify stimuli from among a small set of words or phrases. However, the researchers at Austin were able to circumvent these limitations by using a non-invasive method and training the AI system on a specific user’s thought patterns. The team had several test subjects listen to hours of podcasts while a fMRI machine recorded their brain activity. The resulting data was then used to train the system on the specific user’s thought patterns.
During the testing phase, the AI system was fed the subjects’ fMRI data and decoded the signals into plain language in real-time. The system was able to get things right approximately 50% of the time, although the results were not exact. The researchers designed the AI to convey the general ideas being thought about, not the exact words being thought.
The team notes that the system only functions if it’s trained on a specific user’s brainwaves, making it useless for scanning individuals who haven’t provided fMRI data. Even if such data was generated without a user’s permission, the researchers conclude that both the decoding of the data and the machine’s ability to monitor thoughts in real-time require active participation on the part of the person being scanned.
However, the researchers also note that future developments might enable decoders to bypass these requirements. Moreover, even if decoder predictions are inaccurate without subject cooperation, they could be intentionally misinterpreted for malicious purposes. This could lead to a ‘proof of thought’ paradigm wherein, perhaps, we could mint non-fungible tokens (NFTs) of our thoughts or record immutable ledgers of our feelings and ideas for posterity, legal purposes, or just bragging rights.
“[O]ur privacy analysis suggests that subject cooperation is currently required both to train and use the decoder. However, future developments might enable decoders to bypass these requirements. Moreover, even if decoder predictions are inaccurate without subject cooperation, they could be intentionally misinterpreted for malicious purposes.”
The impact of thought-to-blockchain NFT minting could have implications for copywriting and patent applications where the blockchain serves as proof of exactly when a thought or idea was recorded. It could also allow celebrity thinkers such as Nobel laureates or contemporary philosophers to codify their ideas in an immutable record—one that could be commoditized and served as collectible digital assets.
The researchers’ breakthrough comes at a time when the field of AI is rapidly evolving, with new applications and use cases emerging every day. The potential for AI to transform our lives is immense, and this latest development is just one example of the technology’s possibilities. However, as with any new technology, there are also risks and challenges that must be addressed.
The Bottom Line
The breakthrough achieved by researchers at the University of Texas at Austin has significant implications for the future of AI and its potential impact on trading strategies. While the technology is still in its early stages, traders should monitor its development closely and consider the potential risks and opportunities that may arise. As with any new technology, there are risks and challenges that must be addressed, but the potential benefits of AI are immense.