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Rice University researchers have discovered a hidden symmetry in the chemical kinetic equations scientists have long used to model and study many of the chemical processes essential for life.

The find has implications for drug design, genetics and biomedical research and is described in a study published on April 21, 2020, in the Proceedings of the National Academy of Sciences. To illustrate the biological ramifications, study co-authors Oleg Igoshin, Anatoly Kolomeisky and Joel Mallory of Rice’s Center for Theoretical Biological Physics (CTBP) used three wide-ranging examples: protein folding, enzyme catalysis and motor protein efficiency.

Igoshin said the symmetry “wasn’t that hard to prove, but no one noticed it before.”

Circa 2018

After 10 years, Prof. Raimar Wulkenhaar from the University of Münster’s Mathematical Institute and his colleague Dr. Erik Panzer from the University of Oxford have solved a mathematical equation which was considered to be unsolvable. The equation is to be used to find answers to questions posed by elementary particle physics. In this interview with Christina Heimken, Wulkenhaar looks back on the challenges encountered in looking for the formula for a solution and he explains why the work is not yet finished.

You worked on the solution to the equation for 10 years. What made this equation so difficult to solve?

Continue reading “Mathematician discusses solving a seemingly unsolvable equation” »

Some big M&A is afoot in Israel in the world of smart transportation. According to multiple reports and sources that have contacted TechCrunch, chip giant Intel is in the final stages of a deal to acquire Moovit, a startup that applies AI and big data analytics to track traffic and provide transit recommendations to some 800 million people globally. The deal is expected to close in the coming days at a price believed to be in the region of $1 billion.

We have contacted Nir Erez, the founder and CEO of Moovit, as well as Intel spokespeople for a comment on the reports and will update this story as we learn more. For now, Moovit’s spokesperson has not denied the reports and what we have been told directly.

“At this time we have no comment, but if anything changes I’ll definitely let you know,” Moovit’s spokesperson.

The triumph of Google’s AlphaGo in 2016 against Go world champion Lee Sedol by 4:1 caused quite the stir that reached far beyond the Go community, with over a hundred million people watching while the match was taking place. It was a milestone in the development of AI: Go had withstood the attempts of computer scientists to build algorithms that could play at a human level for a long time. And now an artificial mind had been built, dominating someone that had dedicated thousands of hours of practice to hone his craft with relative ease.

This was already quite the achievement, but then AlphaGoZero came along, and fed AlphaGo some of its own medicine: it won against AlphaGo with a margin of 100:0 only a year after Lee Sedol’s defeat. This was even more spectacular, and for more than the obvious reasons. AlphaGoZero was not only an improved version of AlphaGo. Where AlphaGo had trained with the help of expert games played by the best human Go players, AlphaGoZero had started literally from zero, working the intricacies of the game out without any supervision.

Continue reading “AlphaZero and the Beauty of the Artificial Mind” »

Rats and equations help researchers develop a theory of how our brains keep track of when events took place.

Education Saturday with Space Time.

It’s not surprising that the profound weirdness of the quantum world has inspired some outlandish explanations – nor that these have strayed into the realm of what we might call mysticism. One particularly pervasive notion is the idea that consciousness can directly influence quantum systems – and so influence reality. Today we’re going to see where this idea comes from, and whether quantum theory really supports it.

Continue reading “Does Consciousness Influence Quantum Mechanics?” »

Researchers at the University of Massachusetts and the Air Force Research Laboratory Information Directorate have recently created a 3D computing circuit that could be used to map and implement complex machine learning algorithms, such convolutional neural networks (CNNs). This 3D circuit, presented in a paper published in Nature Electronics, comprises eight layers of memristors; electrical components that regulate the electrical current flowing in a circuit and directly implement neural network weights in hardware.

“Previously, we developed a very reliable memristive device that meets most requirements of in-memory computing for artificial neural networks, integrated the devices into large 2-D arrays and demonstrated a wide variety of machine intelligence applications,” Prof. Qiangfei Xia, one of the researchers who carried out the study, told TechXplore. “In our recent study, we decided to extend it to the third dimension, exploring the benefit of a rich connectivity in a 3D neural network.”

Essentially, Prof. Xia and his team were able to experimentally demonstrate a 3D computing circuit with eight memristor layers, which can all be engaged in computing processes. Their circuit differs greatly from other previously developed 3D circuits, such as 3D NAND flash, as these systems are usually comprised of layers with different functions (e.g. a sensor layer, a computing layer, a control layer, etc.) stacked or bonded together.

The news: In a fresh spin on manufactured pop, OpenAI has released a neural network called Jukebox that can generate catchy songs in a variety of different styles, from teenybop and country to hip-hop and heavy metal. It even sings—sort of.

How it works: Give it a genre, an artist, and lyrics, and Jukebox will produce a passable pastiche in the style of well-known performers, such as Katy Perry, Elvis Presley or Nas. You can also give it the first few seconds of a song and it will autocomplete the rest.

Rice University researchers have discovered a hidden symmetry in the chemical kinetic equations scientists have long used to model and study many of the chemical processes essential for life.

The find has implications for drug design, genetics and biomedical research and is described in a study published this month in the Proceedings of the National Academy of Sciences. To illustrate the biological ramifications, study co-authors Oleg Igoshin, Anatoly Kolomeisky and Joel Mallory of Rice’s Center for Theoretical Biological Physics (CTBP) used three wide-ranging examples: protein folding, enzyme catalysis and motor protein efficiency.

In each case, the researchers demonstrated that a simple mathematical ratio shows that the likelihood of errors is controlled by kinetics rather than thermodynamics.