Lifeboat Foundation

To get an inside look at the heart, cardiologists often use electrocardiograms (ECGs) to trace its electrical activity and magnetic resonance images (MRIs) to map its structure. Because the two types of data reveal different details about the heart, physicians typically study them separately to diagnose heart conditions.

Now, in a paper published in Nature Communications, scientists in the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard have developed a machine learning approach that can learn patterns from ECGs and MRIs simultaneously, and based on those patterns, predict characteristics of a patient’s heart. Such a tool, with further development, could one day help doctors better detect and diagnose heart conditions from routine tests such as ECGs.

The researchers also showed that they could analyze ECG recordings, which are easy and cheap to acquire, and generate MRI movies of the same heart, which are much more expensive to capture. And their method could even be used to find new genetic markers of heart disease that existing approaches that look at individual data modalities might miss.

Explore the latest trends in deep learning and artificial intelligence with five emerging technologies including federated learning, XAI and more!

Using AI to read people’s thoughts? 😀

In a groundbreaking study, scientists employ a ChatGPT-like AI model to passively decode human thoughts with unprecedented accuracy, unlocking new potential in brain imaging and raising privacy concerns.

An artificial intelligence chatbot was able to outperform human doctors in responding to patient questions posted online, according to evaluators in a new study.

Research published in the Journal of the American Medical Association (JAMA) Internal Medicine found that a chatbot’s responses to patient questions, pulled from a social media platform, were rated “significantly higher for both quality and empathy.”

A report has found that AI chatbots have created dozens of news websites around the world that focus on “clickbait articles” to generate revenue.

The report published by the news-rating group NewsGuard identified 49 websites across seven different languages that appeared to be generated – or mostly generated – by AI language models. NewsGuard researchers found that these websites tended to use dull language and repeated phrases, which are trademarks of AI chatbots.

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

The study, published in the journal Nature Neuroscience, was led by Jerry Tang, a doctoral student in computer science, and Alex Huth, an assistant professor of neuroscience and computer science at UT Austin. The work relies in part on a transformer model, similar to the ones that power Open AI’s ChatGPT and Google’s Bard.

Unlike other language decoding systems in development, 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.

Scientists have developed a noninvasive AI system focused on translating a person’s brain activity into a stream of text, according to a peer-reviewed study published Monday in the journal Nature Neuroscience.

The system, called 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.

Speaking with ‘The New York Times,’ Geoffrey Hinton says a part of him regrets his life’s work on artificial intelligence.

A team of scientists at the Instituto Italiano di Tecnologia in Italy has created the world’s first completely edible and rechargeable battery. The innovative battery could be used to power edible electronics for health diagnostics, food quality monitoring, and edible soft robotics.

Edible Rechargeable Battery

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