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Researchers from the University of Pisa developed a quantum subroutine to improve matrix multiplication for AI and machine learning applications.

When you multiply two large matrices—this is a common task in fields like machine learning, but it can be time-consuming, even for powerful computers…

In a recent study published in IEEE Access, a team of researchers from the University of Pisa introduced a quantum subroutine designed to streamline matrix multiplication. This subroutine is a new feature in the toolbox of matrix multiplication that could improve computational efficiency, particularly in applications like machine learning and data processing.

It’s A Matrix World And We’re Just Living In It

Continue reading “Matrix Re-Reloaded: Quantum Subroutine Improves Efficiency of Matrix Multiplication for AI and Machine Learning Applications” | >

Imagine you’re sitting across from a friend, having a conversation.

I’m a die-hard Beach Boys fan. In one of their most famous songs, they sing about “pickin’ up good vibrations” from a girl. We’ve all felt those “good vibes” when we’re connecting with someone new. I used to think that feeling was a mysterious, mystical experience — something I couldn’t fully explain that bonded me with some friends and strangers more easily than others.

It turns out that “good vibes” aren’t as mysterious as I thought.

Pioneering neuroscientists have begun investigating how the brain works when we are interact ing with others — a technique they call hyperscanning. Neuroscientists have been using existing scanning methods, like MRI and EEG, to monitor the brain activity of two or more people as they do something together: for example, performing music, learning a poem, or having a conversation.

Continue reading “Picking up good vibes from a stranger? That might be your neurons aligning” | >

Learning and a spectrum of other behavioral competencies allow organisms to rapidly adapt to dynamically changing environmental variations. The emerging field of diverse intelligence seeks to understand what systems, besides ones with complex brains, exhibit these capacities. Here, we tested predictions of a general computational framework based on the free energy principle in neuroscience but applied to aneural biological process as established previously, by demonstrating and manipulating pattern recognition in a simple aneural organism, the green algae Volvox. Our studies of the adaptive photoresponse in Volvox reveal that aneural organisms can distinguish between patterned and randomized inputs and indicate how this is achieved mechanistically.

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As crazy or stupid as it may sound to some people there are some dreams and ideas I will never Give up no matter how impossible it may seem now.

One account stated that Clarke’s laws were developed after the editor of his works in French started numbering the author’s assertions. [ 2 ] All three laws appear in Clarke’s essay “Hazards of Prophecy: The Failure of Imagination”, first published in Profiles of the Future (1962); [ 3 ] however, they were not all published at the same time. Clarke’s first law was proposed in the 1962 edition of the essay, as “Clarke’s Law” in Profiles of the Future.

The second law is offered as a simple observation in the same essay but its status as Clarke’s second law was conferred by others. It was initially a derivative of the first law and formally became Clarke’s second law where the author proposed the third law in the 1973 revision of Profiles of the Future, which included an acknowledgement. [ 4 ] It was also here that Clarke wrote about the third law in these words: “As three laws were good enough for Newton, I have modestly decided to stop there”

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