Lifeboat Foundation

By making remarkable breakthroughs in a number of fields, unlocking new approaches to science, and accelerating the pace of science and innovation.

In 2020, Google’s AI team DeepMind announced that its algorithm, AlphaFold, had solved the protein-folding problem. At first, this stunning breakthrough was met with excitement from most, with scientists always ready to test a new tool, and amusement by some. After all, wasn’t this the same company whose algorithm AlphaGo had defeated the world champion in the Chinese strategy game Go, just a few years before? Mastering a game more complex than chess, difficult as that is, felt trivial compared to the protein-folding problem. But AlphaFold proved its scientific mettle by sweeping an annual competition in which teams of biologists guess the structure of proteins based only on their genetic code. The algorithm far outpaced its human rivals, posting scores that predicted the final shape within an angstrom, the width of a single atom. Soon after, AlphaFold passed its first real-world test by correctly predicting the shape of the SARS-CoV-2 ‘spike’ protein, the virus’ conspicuous membrane receptor that is targeted by vaccines.

The success of AlphaFold soon became impossible to ignore, and scientists began trying out the algorithm in their labs. By 2021 Science magazine crowned an open-source version of AlphaFold the “Method of the Year.” Biochemist and Editor-in-Chief H. Holden Thorp of the journal Science wrote in an editorial, “The breakthrough in protein-folding is one of the greatest ever in terms of both the scientific achievement and the enabling of future research.” Today, AlphaFold’s predictions are so accurate that the protein-folding problem is considered solved after more than 70 years of searching. And while the protein-folding problem may be the highest profile achievement of AI in science to date, artificial intelligence is quietly making discoveries in a number of scientific fields.

Continue reading “AI is Ushering In a New Scientific Revolution” »

Leveraging big data & artificial intelligence to solve unmet medical needs — andrea de souza — eli lilly & co.

Andrea De Souza, is Associate Vice President, Research Data Sciences and Engineering, at Eli Lilly & Company (https://www.lilly.com/) where over the past three years her work has focused around empowering the Lilly Research Laboratories (LRL) organization with greater computational, analytics-intense experimentation to raise the innovation of their scientists.

Continue reading “Andrea De Souza — Eli Lilly — Leveraging Big Data & Artificial Intelligence For Unmet Medical Needs” »

The Ingenuity chopper on Mars has lost an instrument that helps it navigate. Flight controllers have found a work-around.

Things are getting challenging for the Ingenuity helicopter on Mars. The latest news from Håvard Grip, its chief pilot, is that the “Little Chopper that Could” has lost its sense of direction thanks to a failed instrument. Never mind that it was designed to make only a few flights, mostly in Mars spring. Or that it’s having a hard time staying warm now that winter is coming. Now, one of its navigation sensors, called an inclinometer, has stopped working. It’s not the end of the world, though. “A nonworking navigation sensor sounds like a big deal – and it is – but it’s not necessarily an end to our flying at Mars,” Grip wrote on the Mars Helicopter blog on June 6. It turns out that the controllers have options.

Like other NASA planetary missions, Ingenuity sports a fair amount of redundancy in its systems. It has an inertial measurement unit (IMU) that measures accelerations and angular rates of ascent and descent in three directions. In addition, there’s a laser rangefinder that measures the distance to the ground. Finally, the chopper has a navigation camera. It gives visual evidence of where Ingenuity is during flight or on the ground. An algorithm takes data from these instruments and uses it during flight. But, it needs to know the chopper’s roll and pitch attitude, and that’s what the inclinometer supplies.

Continue reading “Ingenuity has Lost its Sense of Direction, but It’ll Keep on Flying” »

After nine years working at NASA Jet Propulsion Laboratory, Oliver Toupet is developing cutting-edge AI algorithms that enable the self-driving zoox vehicle to understand and make decisions based on its surroundings, and to optimize trajectories to reach its destination safely and comfortably.

Learn why he says the work he’s doing at Zoox is, in some ways, more challenging than his previous work.

Continue reading “The next frontier in robotics” »

Understanding the early universe has been a goal for scientists for decades. And, now with NASA’s James Webb space telescope, and other technology, we’re finally making some decent strides. A new simulation on early galaxy formation could be another key stepping stone, too.

Researchers created the simulation using machine learning. It then completed over 100,000 hours of computations to create the one-of-a-kind simulation. The researchers named the algorithm responsible for the project Hydo-BAM. They published a paper with the simulation’s findings earlier this year.

Creating a simulation of early galaxy formation has allowed researchers to chart the earliest moments of our universe. These important moments began just after the Big Bang set everything into motion. Understanding these key moments of the formation of the early universe could help us better understand how galaxies form in the universe today.

A team of researchers from the University of Shanghai for Science and Technology and the University of Dayton has developed a way to bend light into a vortex ring using mirrors, lasers and lenses. In their study, published in the journal Nature Photonics, the group built on work done by other teams in which vortex rings were observed incidentally, and then mathematically designed a system that could generate them on demand.

In 2016, another team of researchers discovered that under the right circumstances, strong pulses of light swirling around a central pipe-shaped pulse, could sometimes form into a donut-shaped vortex. Intrigued by the finding, the researchers with this new effort began to wonder if it might be possible to create such vortex rings on demand.

They started by studying the properties and conditions that had led to the formations observed by the team in 2016 and applied mathematics to the problem. They found solutions that appeared to show how such rings could be made—solutions to Maxwell’s equations, in particular, they found, could be used to generate the kind of conformal mapping required.

Canadian agri-tech company Solinftec has announced that it will expand the launch of its new cutting-edge AgTech robotic platform, Solix Ag Robotics, into Canada in partnership with Stone Farms and the University of Saskatchewan. The new robot is state-of-art technology built to scan and monitor fields providing detailed real-time data.

Solinftec’s new technology aims to provide farmers and agronomists with a new level of information to increase yields, improve the usage of inputs, lower environmental impact, and support the global demand for food supply. The robot should be commercially available for use in wheat crops in time for next year’s growing season.

Solinftec’s Solix Ag Robot will autonomously move back and forth through farmers’ fields on four wheels. It will use onboard cameras and other sensors – along with AI-based software – to check the health of plants and assess their nutritional content. Solix Ag is integrated with the company’s artificial intelligence platform, ALICE A.I. Programmed with a neurological network featuring a complex detection algorithm, the new in-field robotic device has the ability not only to scan for crop health and nutrition, insects, and weeds but is built to monitor the entire field ecosystem and provide real-time insights.

Spiking neural networks (SNNs) capture the most important aspects of brain information processing. They are considered a promising approach for next-generation artificial intelligence. However, the biggest problem restricting the development of SNNs is the training algorithm.

To solve this problem, a research team led by Prof. Zeng Yi from the Institute of Automation of the Chinese Academy of Sciences has proposed backpropagation (BP) with biologically plausible spatiotemporal adjustment for training deep spiking neural networks.

The associated study was published in Patterns on June 2.

Marketing and the need for data rules

Legislators and decision-makers worldwide have also been active in regulating data although it’s almost impossible to keep pace with change in many places. The genuine exploitation of data requires rules and regulations, as growth always increases the potential for misuse. The task of technology companies is to build data pipelines that ensure the trust and security of AI and analytics.

Data is the new currency for businesses, and the overwhelming growth rate of it can be intimidating. The key challenge is to harness data in a way that benefits both marketers and consumers who produce it. And in doing this, manage the “big data” in an ethically correct and consumer-friendly way. Luckily, there are many great services for analyzing data, effective regulation to protect consumers’ rights and a never-ending supply of information at our hands to make better products and services. The key for businesses is to embrace these technologies so that they can avoid sinking in their own data.