Lifeboat Foundation

This could lead to a truly random number generator making things much more secure.

Random numbers are crucial for computing, but our current algorithms aren’t truly random. Researchers at Brown University have now found a way to tap into the fluctuations of quasiparticles to generate millions of truly random numbers per second.

Random number generators are key parts of computer software, but technically they don’t quite live up to their name. Algorithms that generate these numbers are still deterministic, meaning that anyone with enough information about how it works could potentially find patterns and predict the numbers produced. These pseudo-random numbers suffice for low stakes uses like gaming, but for scientific simulations or cybersecurity, truly random numbers are important.

Continue reading “Quasiparticles used to generate millions of truly random numbers a second” »

Labeling data can be a chore. It’s the main source of sustenance for computer-vision models; without it, they’d have a lot of difficulty identifying objects, people, and other important image characteristics. Yet producing just an hour of tagged and labeled data can take a whopping 800 hours of human time. Our high-fidelity understanding of the world develops as machines can better perceive and interact with our surroundings. But they need more help.

Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Microsoft, and Cornell University have attempted to solve this problem plaguing vision models by creating “STEGO,” an algorithm that can jointly discover and segment objects without any human labels at all, down to the pixel.

Continue reading “Scientists create algorithm to assign a label to every pixel in the world, without human supervision” »

For centuries, mathematicians have tried to prove that Euler’s fluid equations can produce nonsensical answers. A new approach to machine learning has researchers betting that “blowup” is near.

Back in 1993, AI pioneer Jürgen Schmidhuber published the paperA Self-Referential Weight Matrix, which he described as a “thought experiment… intended to make a step towards self-referential machine learning by showing the theoretical possibility of self-referential neural networks whose weight matrices (WMs) can learn to implement and improve their own weight change algorithm.” A lack of subsequent practical studies in this area had however left this potentially impactful meta-learning ability unrealized — until now.

In the new paper A Modern Self-Referential Weight Matrix That Learns to Modify Itself, a research team from The Swiss AI Lab, IDSIA, University of Lugano (USI) & SUPSI, and King Abdullah University of Science and Technology (KAUST) presents a scalable self-referential WM (SRWM) that leverages outer products and the delta update rule to update and improve itself, achieving both practical applicability and impressive performance in game environments.

The proposed model is built upon fast weight programmers (FWPs), a scalable and effective method dating back to the ‘90s that can learn to memorize past data and compute fast weight changes via programming instructions that are additive outer products of self-invented activation patterns, aka keys and values for self-attention. In light of their connection to linear variants of today’s popular transformer architectures, FWPs are now witnessing a revival. Recent studies have advanced conventional FWPs with improved elementary programming instructions or update rules invoked by their slow neural net to reprogram the fast neural net, an approach that has been dubbed the “delta update rule.”

It’s easy to get caught up in the doom-and-gloom predictions about artificial intelligence wiping out millions of jobs. Here’s a reality check.

Neuroscientists from St. Petersburg University, led by Professor Allan V. Kalueff, in collaboration with an international team of IT specialists, have become the first in the world to apply the artificial intelligence (AI) algorithms to phenotype zebrafish psychoactive drug responses. They managed to train AI to determine—by fish response—which psychotropic agents were used in the experiment.

The research findings are published in the journal Progress in Neuro-Psychopharmacology and Biological Psychiatry.

The zebrafish (Danio rerio) is a freshwater bony fish that is presently the second-most (after mice) used model organism in biomedical research. The advantages for utilizing zebrafish as a model biological system are numerous, including low maintenance costs and high genetic and physiological similarity to humans. Zebrafish share 70% of genes with us. Furthermore, the simplicity of the zebrafish nervous system enables researchers to achieve more explicit and accurate results, as compared to studies with more complex organisms.

Existing quantum devices can actually do things that we cannot compute with classical computers. The question is only can we harness this computational power that is apparently there,” van Bijnen says. “Maybe doing arbitrary computational problems is a bit much to ask, so we are now looking at whether we can match problems well to available quantum hardware.” Many current experiments involving Rydberg atoms would likely not require any radical changes in instrumentation that is already being used, he adds.

A chip-based infection model developed by researchers in Jena, Germany, enables live microscopic observation of damage to lung tissue caused by the invasive fungal infection aspergillosis. The team developed algorithms to track the spread of fungal hyphae as well as the response of immune cells. The development is based on a “lung-on-chip” model also developed in Jena and can help reduce the number of animal experiments. The results were presented in the journal Biomaterials.

Aspergillosis is a mold infection caused by Aspergillus fumigatus, which often affects the lungs. The disease can be fatal, especially in immunocompromised individuals. In these cases, invasive aspergillosis usually occurs with fungal hyphae invading blood vessels. So far, there are only a few active substances that can combat such fungal infections. “That’s why it was so important for us to be able to represent this invasive growth in a model,” says Marie von Lilienfeld-Toal, who co-led the study. The internist is a professor at the Department of Internal Medicine II at Jena University Hospital and conducts research at the Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute (Leibniz-HKI) in Jena, Germany.

The new aspergillosis infection model should help to better observe both the growth of the fungus and the reaction of the immune system and to find possible new approaches for therapies. In addition, new active substances can be tested. The expertise for this is available in Jena: Organ chips have long been developed at the university hospital. The startup Dynamic42, which manufactures the lung chips used in the study, was founded there. First author Mai Hoang also joined the company after completing her doctorate.

When Dr. Shiran Barber-Zucker joined the lab of Prof. Sarel Fleishman as a postdoctoral fellow, she chose to pursue an environmental dream: breaking down plastic waste into useful chemicals. Nature has clever ways of decomposing tough materials: Dead trees, for example, are recycled by white-rot fungi, whose enzymes degrade wood into nutrients that return to the soil. So why not coax the same enzymes into degrading man-made waste?

Barber-Zucker’s problem was that these enzymes, called versatile peroxidases, are notoriously unstable. “These natural enzymes are real prima donnas; they are extremely difficult to work with,” says Fleishman, of the Biomolecular Sciences Department at the Weizmann Institute of Science. Over the past few years, his lab has developed computational methods that are being used by thousands of research teams around the world to design enzymes and other proteins with enhanced stability and additional desired properties. For such methods to be applied, however, a protein’s precise molecular structure must be known. This typically means that the protein must be sufficiently stable to form crystals, which can be bombarded with X-rays to reveal their structure in 3D. This structure is then tweaked using the lab’s algorithms to design an improved protein that doesn’t exist in nature.