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Category: robotics/AI – Page 1,466

OpenAI wants to shut a student’s repository over GPT4 access

Xtekky, a European computer science student, finds out what happens if you write a program that runs queries through these freely accessible sites and returns you the answer.

In other news, a big company with a non-profit and for-profit subsidiary sues an independent creator trying to make things easier for everyday folks. Even though it technically doesn’t violate its terms and conditions? Perhaps we’ll leave that for the experts to decide.

OpenAI, the creators of ChatGPT, a scourge on teachers and succor to everyone else, opted against free access when it released its newer GPT4 model.

New AI decoder can translate brainwaves into text

This is an important step on the way to develop brain–computer interfaces that can decode continuous language through non-invasive recordings of thoughts.

Results were published in a recent study in the peer-reviewed journal Nature Neuroscience, led by Jerry Tang, a doctoral student in computer science, and Alex Huth, an assistant professor of neuroscience and computer science at UT Austin.

Tang and Huth’s semantic decoder isn’t implanted in the brain directly; instead, it uses fMRI machine scans to measure brain activity. For the study, participants in the experiment listened to podcasts while the AI attempted to transcribe their thoughts into text.

AI pioneer quits Google to warn about the technology’s ‘dangers’

Geoffrey Hinton, who has been called the ‘Godfather of AI,’ confirmed Monday that he left his role at Google last week to speak out about the “dangers” of the technology he helped to develop.

Hinton’s pioneering work on neural networks shaped artificial intelligence systems powering many of today’s products. He worked part-time at Google for a decade on the tech giant’s AI development efforts, but he has since come to have concerns about the technology and his role in advancing it.

“I console myself with the normal excuse: If I hadn’t done it, somebody else would have,” Hinton told the New York Times, which was first to report his decision.

Uncovering the Mystery of the Human Brain with Computational Neuroscience

Defining computational neuroscience The evolution of computational neuroscience Computational neuroscience in the twenty-first century Some examples of computational neuroscience The SpiNNaker supercomputer Frontiers in computational neuroscience References Further reading

The human brain is a complex and unfathomable supercomputer. How it works is one of the ultimate mysteries of our time. Scientists working in the exciting field of computational neuroscience seek to unravel this mystery and, in the process, help solve problems in diverse research fields, from Artificial Intelligence (AI) to psychiatry.

Computational neuroscience is a highly interdisciplinary and thriving branch of neuroscience that uses computational simulations and mathematical models to develop our understanding of the brain. Here we look at: what computational neuroscience is, how it has grown over the last thirty years, what its applications are, and where it is going.

Machine learning model finds genetic factors for heart disease

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 that can learn patterns from ECGs and MRIs simultaneously, and based on those patterns, predict characteristics of a patient’s . 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.

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