Lifeboat Foundation

Open source is fertile ground for transformative software, especially in cutting-edge domains like artificial intelligence (AI) and machine learning. The open source ethos and collaboration tools make it easier for teams to share code and data and build on the success of others.

This article looks at 13 open source projects that are remaking the world of AI and machine learning. Some are elaborate software packages that support new algorithms. Others are more subtly transformative. All of them are worth a look.

A new longevity focused venture capital fund is preparing to announce its first investments, as it seeks to accelerate commercialisation in the field. Joining the likes of Maximon, Apollo and Korify, New York’s Life Extension Ventures (LifeX) has put together a $100 million fund specifically for companies developing solutions to extend the longevity of both humans and our planet. In a slight twist, the fund is predominantly looking to invest in companies that are leveraging software and data at the heart of their efforts to hasten the adoption of scientific breakthroughs in longevity.

Longevity. Technology: The longevity field is alive with innovation, and developments in AI and Big Data are just some of the software-led technologies driving progress throughout the sector. Co-founded by scientists-turned-entrepreneurs, Amol Sarva and Inaki Berenguer, LifeX Ventures’ investment philosophy draws on their combined experiences building software-led companies across a wide range of sectors. We caught up with Sarva to learn more.

Between them Sarva, a cognitive scientist by training, and Berenguer have led and/or founded several startups, such as CoverWallet, Virgin Mobile USA and Halo Neuroscience. The two have also invested personally in more than 150 startups before their interest turned more recently to longevity.

Using artificial intelligence, physicists have compressed a daunting quantum problem that until now required 100,000 equations into a bite-size task of as few as four equations—all without sacrificing accuracy. The work, published in the September 23 issue of Physical Review Letters, could revolutionize how scientists investigate systems containing many interacting electrons. Moreover, if scalable to other problems, the approach could potentially aid in the design of materials with sought-after properties such as superconductivity or utility for clean energy generation.

“We start with this huge object of all these coupled-together differential equations; then we’re using machine learning to turn it into something so small you can count it on your fingers,” says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute’s Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy.

The formidable problem concerns how electrons behave as they move on a gridlike lattice. When two electrons occupy the same lattice site, they interact. This setup, known as the Hubbard model, is an idealization of several important classes of materials and enables scientists to learn how electron behavior gives rise to sought-after phases of matter, such as superconductivity, in which electrons flow through a material without resistance. The model also serves as a testing ground for new methods before they’re unleashed on more complex quantum systems.

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The circuits are found in several forms and in different locations. Some offer faster creation of new AI models. They use multiple processing circuits in parallel to churn through millions, billions or even more data elements, searching for patterns and signals. These are used in the lab at the beginning of the process by AI scientists looking for the best algorithms to understand the data.

After 45 years of voicing one of the most iconic characters in cinema history, James Earl Jones has said goodbye to Darth Vader. At 91, the legendary actor recently told Disney he was “looking into winding down this particular character.” That forced the company to ask itself how do you even replace Jones? The answer Disney eventually settled on, with the actor’s consent, involved an AI program.

If you’ve seen any of the recent Star Wars shows, you’ve heard the work of Respeecher. It’s a Ukrainian startup that uses archival recordings and a “proprietary AI algorithm” to create new dialogue featuring the voices of “performers from long ago.” In the case of Jones, the company worked with Lucasfilm to recreate his voice as it had sounded when film audiences first heard Darth Vader in 1977.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

Marketers are standing at a precipice when it comes to strategy and automation. Advanced systems are seen as the next step in marketing, but for many businesses, the concept is still uncharted territory. However, those who don’t adopt the rapidly advancing technology into their marketing plans will quickly be at a huge disadvantage.

According to International Data Corporation (IDC), the U.S. market for AI-powered software, hardware and services is expected to break $120 billion by 2025. The marketing intelligence firm also found that banks and retailers were the biggest spenders on AI, with retail having already invested upwards of $5.9 billion in these systems for marketing alone in 2019. And spending on these advanced systems for this specific purpose has only increased since then. It is evident that algorithmic systems are the future of marketing, and those who don’t invest in them will be left behind.

Skoltech scientists modeled the behavior of nanobubbles appearing in van der Waals heterostructures and the behavior of substances trapped inside the bubbles. In the future, the new model will help obtain equations of state for substances in nano-volumes, opening up new opportunities for the extraction of hydrocarbons from rock with large amounts of micro-and nanopores. The results of the study were published in the Journal of Chemical Physics.

The van der Waals nanostructures hold much promise for the study of tiniest samples with volumes from 1 cubic micron down to several cubic nanometers. These atomically thin layers of two-dimensional materials, such as graphene, hexagonal boron nitride (hBN) and dichalcogenides of transition metals, are held together by weak van der Waals interaction only. Inserting a sample between the layers separates the upper and bottom layers, making the upper layer lift to form a nanobubble. The resulting structure will then become available for transmission electron and atomic force microscopy, providing an insight into the structure of the substance inside the bubble.

The properties exhibited by substances inside the van der Waals nanobubbles are quite unusual. For example, water trapped inside a nanobubble displays a tenfold decrease in its dielectric constant and etches the diamond surface, something it would never do under normal conditions. Argon which typically exists in liquid form when in large quantities can become solid at the same pressure if trapped inside very small nanobubbles with a radius of less than 50 nanometers.

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According to recent research from Sweden’s Lund University, the most commonly used analytical method in population genetics is deeply flawed. This could have caused incorrect results and misconceptions regarding ethnicity and genetic relationships. The method has been used in hundreds of thousands of studies, influencing findings in medical genetics and even commercial ancestry tests. The findings were recently published in the journal Scientific Reports.

The pace at which scientific data can be gathered is increasing rapidly, resulting in huge and very complex databases, which has been nicknamed the “Big Data revolution.” Researchers employ statistical techniques to condense and simplify the data while maintaining the majority of the important information in order to make the data more manageable. PCA (principal component analysis) is perhaps the most widely used approach. Imagine PCA as an oven with flour, sugar, and eggs serving as the input data. The oven may always perform the same thing, but the ultimate result, a cake, is highly dependent on the ratios of the ingredients and how they are mixed.

“It is expected that this method will give correct results because it is so frequently used. But it is neither a guarantee of reliability nor produces statistically robust conclusions,” says Dr. Eran Elhaik, Associate Professor in molecular cell biology at Lund University.