Lifeboat Foundation

HBP researchers have trained a large-scale model of the primary visual cortex of the mouse to solve visual tasks in a highly robust way. The model provides the basis for a new generation of neural network models. Due to their versatility and energy-efficient processing, these models can contribute to advances in neuromorphic computing.

Modeling the brain can have a massive impact on artificial intelligence (AI): since the brain processes images in a much more energy-efficient way than artificial networks, scientists take inspiration from neuroscience to create neural networks that function similarly to the biological ones to significantly save energy.

In that sense, brain-inspired neural networks are likely to have an impact on future technology, by serving as blueprints for visual processing in more energy-efficient neuromorphic hardware. Now, a study by Human Brain Project (HBP) researchers from the Graz University of Technology (Austria) showed how a large data-based model can reproduce a number of the brain’s visual processing capabilities in a versatile and accurate way. The results were published in the journal Science Advances.

You can’t move a pharmaceutical scientist from a lab to a kitchen and expect the same research output. Enzymes behave exactly the same: They are dependent upon a specific environment. But now, in a study recently published in ACS Synthetic Biology, researchers from Osaka University have imparted an analogous level of adaptability to enzymes, a goal that has remained elusive for over 30 years.

Enzymes perform impressive functions, enabled by the unique arrangement of their constituent amino acids, but usually only within a specific cellular environment. When you change the cellular environment, the enzyme rarely functions well—if at all. Thus, a long-standing research goal has been to retain or even improve upon the function of enzymes in different environments; for example, conditions that are favorable for biofuel production. Traditionally, such work has involved extensive experimental trial-and-error that might have little assurance of achieving an optimal result.

Artificial intelligence (a computer-based tool) can minimize this trial-and-error, but still relies on experimentally obtained crystal structures of enzymes—which can be unavailable or not especially useful. Thus, “the pertinent amino acids one should mutate in the enzyme might be only best-guesses,” says Teppei Niide, co-senior author. “To solve this problem, we devised a methodology of ranking amino acids that depends only on the widely available amino acid sequence of analogous enzymes from other living species.”

A team of researchers from Korea University, Ajou University and Hanyang University, all in the Republic of Korea, has created a tiny aquabot propelled by fins made of a porous hydrogel imbued with nanoparticles. In their paper published in the journal Science Robotics, the group describes how the hydrogel works to power a tiny boat and reveals how much voltage was required.

Scientists and engineers have been working for several years to build tiny, soft robots for use in medical applications and have found that hydrogels are quite suitable for the task. Unfortunately, such materials also have undesirable characteristics, most notably, poor electro-connectivity. In this new effort, the researchers took a new approach to making hydrogels more amenable for use with electricity as a power source —adding conductive nanoparticles.

Continue reading “Incorporating nanoparticles into a porous hydrogel to propel an aquabot with minimal voltage” »

Demand is growing for effective new technologies to cool buildings, as climate change intensifies summer heat. Now, scientists have just designed a transparent window coating that could lower the temperature inside buildings, without expending a single watt of energy. They did this with the help of advanced computing technology and artificial intelligence. The researchers report the details today (November 2) in the journal ACS Energy Letters.

Cooling accounts for about 15% of global energy consumption, according to estimates from previous research studies. That demand could be lowered with a window coating that could block the sun’s ultraviolet and near-infrared light. These are parts of the solar spectrum that are not visible to humans, but they typically pass through glass to heat an enclosed room.

Energy use could be even further reduced if the coating radiates heat from the window’s surface at a wavelength that passes through the atmosphere into outer space. However, it’s difficult to design materials that can meet these criteria simultaneously and at the same time can also transmit visible light, This is required so they don’t interfere with the view. Eungkyu Lee, Tengfei Luo, and colleagues set out to design a “transparent radiative cooler” (TRC) that could do just that.

The last decade has marked a profound change in how we perceive and talk about Artificial Intelligence. The concept of learning, once confined in the corner of AI, has now become so important some people came up with the new term “Machine Intelligence”[1][2][3] as to make clear the fundamental role of Machine Learning in it and further depart form older symbolic approaches.

Recent Deep Learning (DL) techniques have literally swept away previous AI approaches and have shown how beautiful, end-to-end differentiable functions can be learned to solve incredibly complex tasks involving high-level perception abilities.

Yet, since DL techniques have been proven shining only with a large number of labeled examples, the research community has now shifted his attention towards Unsupervised and Reinforcement Learning, both aiming to solve equivalently complex tasks but without (or less as possible) explicit supervision.

Researchers built an AI that gives out money based on who started with less resources—and humans preferred it.

A new tool can determine whether a collection of building blocks will assemble into a mechanically sound structure.

One small step for a machine… one giant leap for the singularity.

This AI actually improved a key algorithm that makes it run even faster.

Continue reading “This New AI is a Game Changer!” »

Dr. Michael Rose is an evolutionary biologist and authority in gerontology. His many years of research and keen insight establish unique methods to frame the problems of aging. Michael made scientific history with experiments manipulating the life spans of fruit flies. As a pragmatist, Michael sees beyond today’s quick fixes to examine what could be the most important changes in the longevity industry to slow down and stop aging. His view is that genomics in conjunction with machine learning is the future of longevity.