Lifeboat Foundation

The gravitational constant G determines the strength of gravity—the force that makes apples fall to the ground or pulls the Earth in its orbit around the sun. It is part of Isaac Newton’s law of universal gravitation, which he first formulated more than 300 years ago. The constant cannot be derived mathematically; it has to be determined through experiment.

Over the centuries, scientists have conducted numerous experiments to determine the value of G, but the scientific community isn’t satisfied with the current figure. It is still less precise than the values of all the other fundamental natural constants—for example, the speed of light in a vacuum.

One reason gravity is extremely difficult to quantify is that it is a very weak force and cannot be isolated: when you measure the gravity between two bodies, you also measure the effect of all other bodies in the world.

Waste heat is a promising source of energy conservation and reuse, by means of converting this heat into electricity—a process called thermoelectric conversion. Commercially available thermoelectric conversion devices are synthesized using rare metals. While these are quite efficient, they are expensive, and in the majority of cases, utilize toxic materials. Both these factors have led to these converters being of limited use. One of the alternatives is oxide-based thermoelectric materials, but the primary drawback these suffer from is a lack of evidence of their stability at high temperatures.

A team led by Professor Hiromichi Ohta at the Research Institute for Electronic Science at Hokkaido University has synthesized a barium cobalt oxide thermoelectric converter that is reproducibly stable and efficient at temperatures as high as 600°C. The team’s findings have been published in the journal ACS Applied Materials & Interfaces.

Thermoelectric conversion is driven by the Seebeck effect: When there is a temperature difference across a conducting material, an electric current is generated. However, efficiency of thermoelectric conversion is dependent on a figure called the thermoelectric figure of merit ZT. Historically, oxide-based converters had a low ZT, but recent research has revealed many candidates that have high ZT, but their stability at high temperatures was not well documented.

Moore’s Law needs a hug. The days of stuffing transistors on little silicon computer chips are numbered, and their life rafts—hardware accelerators—come with a price.

When programming an accelerator—a process where applications offload certain tasks to system hardware especially to accelerate that task—you have to build a whole new software support. Hardware accelerators can run certain tasks orders of magnitude faster than CPUs, but they cannot be used out of the box. Software needs to efficiently use accelerators’ instructions to make it compatible with the entire application system. This translates to a lot of engineering work that then would have to be maintained for a new chip that you’re compiling code to, with any programming language.

Now, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) created a new programming language called “Exo” for writing high-performance code on hardware accelerators. Exo helps low-level performance engineers transform very simple programs that specify what they want to compute, into very complex programs that do the same thing as the specification, but much, much faster by using these special accelerator chips. Engineers, for example, can use Exo to turn a simple matrix multiplication into a more complex program, which runs orders of magnitude faster by using these special accelerators.

As meetings shifted online during the COVID-19 lockdown, many people found that chattering roommates, garbage trucks and other loud sounds disrupted important conversations.

This experience inspired three University of Washington researchers, who were roommates during the pandemic, to develop better earbuds. To enhance the speaker’s voice and reduce background noise, “ClearBuds” use a novel microphone system and one of the first machine-learning systems to operate in real time and run on a smartphone.

Continue reading “First wireless earbuds that clear up calls using deep learning” »

Gregor Mendel, the Moravian monk, was indeed “decades ahead of his time and truly deserves the title of ‘founder of genetics.’” So concludes an international team of scientists as the 200th birthday of Mendel approaches on 20 July.

The team, from KeyGene in the Netherlands and the John Innes Centre in the UK, draw on newly-discovered historical information to conclude that, when his proposals are viewed in the light of what was known of cells in the mid-19th century, Mendel was decades ahead of his time.

“Uncovering hidden details about Mendel has helped to build a picture of the scientific and intellectual environment in which he worked. At the outset Mendel knew nothing about genetics and had to deduce it all for himself. How he went about this is highly instructive,” said Dr. Noel Ellis from the John Innes Centre, one of the contributors to the study.

Machine learning is transforming all areas of biological science and industry, but is typically limited to a few users and scenarios. A team of researchers at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has developed METIS, a modular software system for optimizing biological systems. The research team demonstrates its usability and versatility with a variety of biological examples.

Though engineering of biological systems is truly indispensable in biotechnology and synthetic biology, today machine learning has become useful in all fields of biology. However, it is obvious that application and improvement of algorithms, computational procedures made of lists of instructions, is not easily accessible. Not only are they limited by programming skills but often also insufficient experimentally-labeled data. At the intersection of computational and experimental works, there is a need for efficient approaches to bridge the gap between machine learning algorithms and their applications for biological systems.

Now a team at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has succeeded in democratizing machine learning. In their recent publication in Nature Communications, the team presented together with collaboration partners from the INRAe Institute in Paris, their tool METIS. The application is built in such a versatile and modular architecture that it does not require computational skills and can be applied on different biological systems and with different lab equipment. METIS is short from Machine-learning guided Experimental Trials for Improvement of Systems and also named after the ancient goddess of wisdom and crafts Μῆτις, or “wise counsel.”

A team of researchers at DeepMind, London, working with colleagues from the University of Exeter, University College London and the University of Oxford, has trained an AI system to find a policy for equitably distributing public funds in an online game. In their paper published in the journal Nature Human Behavior, the group describes the approach they took to training their system and discuss issues that were raised in their endeavor.

How a society distributes wealth is an issue that humans have had to face for thousands of years. Nonetheless, most economists would agree that no system has yet been established in which all of its members are happy with the status quo. There have always been inequitable levels of income, with those on top the most satisfied and those on the bottom the least satisfied. In this latest effort, the researchers in England took a new approach to solving the problem—asking a computer to take a more logical approach.

The researchers began with the assumption that democratic societies, despite their flaws, are thus far the most agreeable of those tried. They then enlisted the assistance of volunteers to play a simple resource allocation game —the players of the game decided together the best ways to share their mutual resources. To make it more realistic, the players received different amounts of resources at the outset and there were different distribution schemes to choose from. The researchers ran the game multiple times with different groups of volunteers. They then used the data from all of the games played to train several AI systems on the ways that humans work together to find a solution to such a problem. Next, they had the AI systems play a similar game against one another, allowing for tweaking and learning over multiple iterations.

When robots appear to engage with people and display human-like emotions, people may perceive them as capable of “thinking,” or acting on their own beliefs and desires rather than their programs, according to research published by the American Psychological Association.

“The relationship between anthropomorphic shape, human-like behavior and the tendency to attribute independent thought and intentional behavior to robots is yet to be understood,” said study author Agnieszka Wykowska, Ph.D., a principal investigator at the Italian Institute of Technology. “As artificial intelligence increasingly becomes a part of our lives, it is important to understand how interacting with a robot that displays human-like behaviors might induce higher likelihood of attribution of intentional agency to the robot.”

The research was published in the journal Technology, Mind, and Behavior.

It’s just a “speck of the universe.”

The first image from NASA’s James Webb Space Telescope offered humanity a stunning new view of the universe on Monday — a first-of-its-kind infrared image so distant in the cosmos that it shows stars and galaxies as they appeared 13 billion years ago.

President Joe Biden revealed the new image Monday at the White House alongside Vice President Kamala Harris and NASA officials. Dubbed “Webb’s First Deep Field,” it is the first full-color image from the $10 billion observatory that launched into space last year, and the highest-resolution infrared view of the universe yet captured.