Lifeboat Foundation

Summary: Researchers investigate brain region synchronization in order to assist control of brain-machine interfaces.

Source: UPF Barcelona.

Just a few decades ago, the possibility of connecting the brain with a computer to convert neural signals into concrete actions would have seemed like something from science fiction.

Even a couple of years ago, the idea that artificial intelligence might be conscious and capable of subjective experience seemed like pure science fiction. But in recent months, we’ve witnessed a dizzying flurry of developments in AI, including language models like ChatGPT and Bing Chat with remarkable skill at seemingly human conversation.

Given these rapid shifts and the flood of money and talent devoted to developing ever smarter, more humanlike systems, it will become increasingly plausible that AI systems could exhibit something like consciousness. But if we find ourselves seriously questioning whether they are capable of real emotions and suffering, we face a potentially catastrophic moral dilemma: either give those systems rights, or don’t.

Experts are already contemplating the possibility. In February 2022, Ilya Sutskever, chief scientist at OpenAI, publicly pondered whether “today’s large neural networks are slightly conscious.” A few months later, Google engineer Blake Lemoine made international headlines when he declared that the computer language model, or chatbot, LaMDA might have real emotions. Ordinary users of Replika, advertised as “the world’s best AI friend,” sometimes report falling in love with it.

Wake up and smell the cortex.

ABOVE: © ISTOCK.COM, CHRISTOPH BURGSTEDT

Artificial intelligence algorithms have had a meteoric impact on protein structure, such as when DeepMind’s AlphaFold2 predicted the structures of 200 million proteins. Now, David Baker and his team of biochemists at the University of Washington have taken protein-folding AI a step further. In a Nature publication from February 22, they outlined how they used AI to design tailor-made, functional proteins that they could synthesize and produce in live cells, creating new opportunities for protein engineering. Ali Madani, founder and CEO of Profluent, a company that uses other AI technology to design proteins, says this study “went the distance” in protein design and remarks that we’re now witnessing “the burgeoning of a new field.”

Proteins are made up of different combinations of amino acids linked together in folded chains, producing a boundless variety of 3D shapes. Predicting a protein’s 3D structure based on its sequence alone is an impossible task for the human mind, owing to numerous factors that govern protein folding, such as the sequence and length of the biomolecule’s amino acids, how it interacts with other molecules, and the sugars added to its surface. Instead, scientists have determined protein structure for decades using experimental techniques such as X-ray crystallography, which can resolve protein folds in atomic detail by diffracting X-rays through crystallized protein. But such methods are expensive, time-consuming, and depend on skillful execution. Still, scientists using these techniques have managed to resolve thousands of protein structures, creating a wealth of data that could then be used to train AI algorithms to determine the structures of other proteins. DeepMind famously demonstrated that machine learning could predict a protein’s structure from its amino acid sequence with the AlphaFold system and then improved its accuracy by training AlphaFold2 on 170,000 protein structures.

Hydrogen power!

The first flight of a hydrogen-powered airplane was a success this week, showing zero-emissions air travel could be on the horizon.

Researchers at Kyushu University, the National Institute of Advanced Industrial Science and Technology (AIST) and Osaka University in Japan have recently introduced a new strategy for synthesizing multi-layer hexagonal boron nitride (hBN), a material that could be used to integrate different 2D materials in electronic devices, while preserving their unique properties. Their proposed approach, outlined in a paper published in Nature Electronics, could facilitate the fabrication of new highly performing graphene-based devices.

“The atomically flat 2D insulator hBN is a key material for the integration of 2D materials into electronic devices,” Hiroki Ago, one of the researchers who carried out the study, told Tech Xplore. “For example, the highest carrier mobility in monolayer graphene is achieved only when it is sandwiched by multilayer hBN. Superconductivity observed in twisted bilayer graphene also needs multilayer hBN to isolate from environment.”

In addition to its value for fabricating graphene-based devices, hBN can also be used to integrate transition metal dichalcogenides (TMDs) in devices, achieving strong photoluminescence and high carrier mobility. It can also be valuable for conducting studies focusing on moiré physics.

How long is the present? The answer, Cornell researchers suggest in a new study, depends on your heart.

They found that our momentary perception of time is not continuous but may stretch or shrink with each heartbeat.

The research builds evidence that the heart is one of the brain’s important timekeepers and plays a fundamental role in our sense of time passing—an idea contemplated since ancient times, said Adam K. Anderson, professor in the Department of Psychology and in the College of Human Ecology (CHE).

The soot produced by unburnt hydrocarbon flames is the second largest contributor to global warming, while also harming human health. Researchers have developed state-of-the-art, high-speed imaging techniques to study turbulent flames, yet they are limited to an imaging rate of million-frames-per-second. Physicists are therefore keen to obtain a complete picture of flame-laser interactions via single-pulse imaging.

In a new report published in Light: Science & Applications, Yogeshwar Nath Mishra and a research team at the Caltech Optical Imaging Laboratory, the NASA Jet propulsion lab, department of physics, and the Institute of Engineering Thermodynamics in the U.S., and Germany, used single-shot laser-sheet comprised ultrafast photography per billion frames per second, for the first time, to observe the dynamics of laser-flames.

Continue reading “Single-pulse real-time billion-frames-per-second planar imaging of ultrafast nanoparticle-laser dynamics” »

Lek-Heng Lim uses tools from algebra, geometry and topology to answer questions in machine learning.