Lifeboat Foundation

Last week, I wrote about the diversity problem in AI and why we need to fix it. I asked you to tell us about your experiences as a Black person in AI or share the names of Black colleagues you admire. Thank you to everyone who responded. It was heart-warming to hear from so many of you.

Many of you shared your frustration with the lack of mentors who understand your challenges, the alienation of being the only Black face at professional meetings, and the struggle to overcome economic and social inequalities. Black women, especially, wrote about the difficulties of building a career in AI. Some of you described your efforts to support Black people in science and technology and provide tech resources to underserved communities. Thank you for sharing with us your dreams and also your disappointments.

We will feature some of your stories in our Working AI blog series. Please stay tuned.

A research team from ETH Zurich and the Max Planck Institute for Solar System Research in Göttingen counted over 136,000 rockfalls on the moon caused by asteroid impacts. Even billions of years old landscapes are still changing.

In October 2015, a spectacular rockfall occurred in the Swiss Alps: in the late morning hours, a large, snow-covered block with a volume of more than 1500 cubic meters suddenly detached from the summit of Mel de la Niva. It fell apart on its way downslope, but a number of boulders continued their journey into the valley. One of the large boulders came to a halt at the foot of the summit next to a mountain hut, after traveling more than 1.4 kilometers and cutting through woods and meadows.

On the moon, time and again boulders and blocks of rock travel downslope, leaving behind impressive tracks, a phenomenon that has been observed since the first unmanned flights to the moon in the 1960s. During the Apollo missions, astronauts examined a few such tracks on site and returned displaced rock block samples to Earth. However, until a few years ago, it remained difficult to gain an overview of how widespread such rock movements are and where exactly they occur.

And they have hardly been deeper in the last 400 million years than now.

Yay I can ride a trex or something: p.

Jurassic Park eat your heart out. The smallest dinosaur on record has been found stuck in amber. When we think about dinosaurs, the creatures we picture are usually quite large, such as the Apatosaurus or the T-Rex. We know others are smaller, such as Velociraptors, but even those were around 180 pounds. Some dinosaurs were a lot smaller than that, a fact which recently demonstrated when scientists recently reported finding the smallest dinosaur ever discovered, trapped in a chunk of amber, according to the BBC. The scientists published their findings in the journal Nature.

Continue reading “Smallest Dinosaur Ever Discovered Found Perfectly Trapped in Amber” »

We have heard of the latest advancements in the field of deep learning due to the usage of different neural networks. Most of these achievements are simply astonishing and I find myself amazed after reading every new article on the advancements in this field almost every week. At the most basic level, all such neural networks are made up of artificial neurons that try to mimic the working of biological neurons. I had a curiosity about understanding how these artificial neurons compare to the structure of biological neurons in our brains and if possibly this could lead to a way to improve neural networks further. So if you are curious about this topic too, then let’s embark on a short 5-minute journey to understand this topic in detail…

Researchers at the Nanoscience Center and at the Faculty of Information Technology at the University of Jyväskylä in Finland have demonstrated that new distance-based machine learning methods developed at the University of Jyväskylä are capable of predicting structures and atomic dynamics of nanoparticles reliably. The new methods are significantly faster than traditional simulation methods used for nanoparticle research and will facilitate more efficient explorations of particle-particle reactions and particles’ functionality in their environment. The study was published in a Special Issue devoted to machine learning in the Journal of Physical Chemistry on May 15, 2020.

The new methods were applied to ligand-stabilized metal nanoparticles, which have been long studied at the Nanoscience Center at the University of Jyväskylä. Last year, the researchers published a method that is able to successfully predict binding sites of the stabilizing ligand molecules on the nanoparticle surface. Now, a new tool was created that can reliably predict potential energy based on the atomic structure of the particle, without the need to use numerically heavy electronic structure computations. The tool facilitates Monte Carlo simulations of the atom dynamics of the particles at elevated temperatures.

Potential energy of a system is a fundamental quantity in computational nanoscience, since it allows for quantitative evaluations of system’s stability, rates of chemical reactions and strengths of interatomic bonds. Ligand-stabilized metal nanoparticles have many types of interatomic bonds of varying chemical strength, and traditionally the energy evaluations have been done by using the so-called density functional theory (DFT) that often results in numerically heavy computations requiring the use of supercomputers. This has precluded efficient simulations to understand nanoparticles’ functionalities, e.g., as catalysts, or interactions with biological objects such as proteins, viruses, or DNA. Machine learning methods, once trained to model the systems reliably, can speed up the simulations by several orders of magnitude.

After SpaceX’s eighth Starlink internet satellite launch, the company released a video of its Falcon 9 rocket jettisoning two $3 million fairings.

Applications are now open for the nanodegree program, which will help Udacity train developers on the Microsoft Azure cloud infrastructure.

Black holes are the dark remnants of collapsed stars, regions of space cut off from the rest of the universe. If something falls into a black hole, it can never come back out. Not even light can escape, meaning black holes are invisible even with powerful telescopes. Yet physicists know black holes exist because they’re consistent with time-tested theories, and because astronomers have observed how matter behaves just outside a black hole.

Naturally, science fiction loves such an enigmatic entity. Black holes have played starring roles in popular books, movies and television shows, from “Star Trek” and “Doctor Who” to the 2014 blockbuster “Interstellar.”

But black holes aren’t quite as menacing as they are commonly portrayed. “They definitely do not suck,” says Daryl Haggard, an astrophysicist at McGill University in Montreal. “A black hole just sits there, passively. Things can fall onto it, just as meteors can fall to Earth, but it doesn’t pull stuff in.”