Lifeboat Foundation

The field equations of Einstein’s General Relativity theory say that faster-than-light (FTL) travel is possible, so a handful of researchers are working to see whether a Star Trek-style warp drive, or perhaps a kind of artificial wormhole, could be created through our technology.

But even if shown feasible tomorrow, it’s possible that designs for an FTL system could be as far ahead of a functional starship as Leonardo da Vinci’s 16th century drawings of flying machines were ahead of the Wright Flyer of 1903. But this need not be a showstopper against human interstellar flight in the next century or two. Short of FTL travel, there are technologies in the works that could enable human expeditions to planets orbiting some of the nearest stars.

Certainly, feasibility of such missions will depend on geopolitical-economic factors. But it also will depend on the distance to nearest Earth-like exoplanet. Located roughly 4.37 light years away, Alpha Centauri is the Sun’s closest neighbor; thus science fiction, including Star Trek, has envisioned it as humanity’s first interstellar destination.

ABOVE: © ISTOCK.COM, CHRISTOPH BURGSTEDT

Artificial intelligence algorithms have had a meteoric impact on protein structure, such as when DeepMind’s AlphaFold2 predicted the structures of 200 million proteins. Now, David Baker and his team of biochemists at the University of Washington have taken protein-folding AI a step further. In a Nature publication from February 22, they outlined how they used AI to design tailor-made, functional proteins that they could synthesize and produce in live cells, creating new opportunities for protein engineering. Ali Madani, founder and CEO of Profluent, a company that uses other AI technology to design proteins, says this study “went the distance” in protein design and remarks that we’re now witnessing “the burgeoning of a new field.”

Proteins are made up of different combinations of amino acids linked together in folded chains, producing a boundless variety of 3D shapes. Predicting a protein’s 3D structure based on its sequence alone is an impossible task for the human mind, owing to numerous factors that govern protein folding, such as the sequence and length of the biomolecule’s amino acids, how it interacts with other molecules, and the sugars added to its surface. Instead, scientists have determined protein structure for decades using experimental techniques such as X-ray crystallography, which can resolve protein folds in atomic detail by diffracting X-rays through crystallized protein. But such methods are expensive, time-consuming, and depend on skillful execution. Still, scientists using these techniques have managed to resolve thousands of protein structures, creating a wealth of data that could then be used to train AI algorithms to determine the structures of other proteins. DeepMind famously demonstrated that machine learning could predict a protein’s structure from its amino acid sequence with the AlphaFold system and then improved its accuracy by training AlphaFold2 on 170,000 protein structures.

A NASA-led research team used satellite imagery and artificial intelligence methods to map billions of discrete tree crowns down to a 50-cm scale. The images encompassed a large swath of arid northern Africa, from the Atlantic to the Red Sea. Allometric equations based on previous tree sampling allowed the researchers to convert imagery into estimates of tree wood, foliage, root size, and carbon sequestration.

The new NASA estimation, published in the journal Nature, was surprisingly low. While the typical estimation of a region’s carbon stock might rely on counting small areas and extrapolating results upwards, the NASA demonstrated technique only counts the trees that are actually there, down to the individual tree. Jules Bayala and Meine van Noordwijk published a News & Views article in the same journal commenting on the NASA team’s work.

Continue reading “NASA captures sequestered carbon of 9.9 billion trees with deep-learning and satellite images” »

Short and sweet. Everyone needs a daily dose of Sabine.

Is science close to explaining everything about our universe? Physicist Sabine Hossenfelder reacts.

Continue reading “Is science about to end? | Sabine Hossenfelder” »

Two mathematicians from Australia and France have come up with a new, faster way to multiply extremely long numbers together.

In doing so, they have cracked an algorithmic puzzle that remained unsolved by some of the world’s best-known math minds, for almost fifty years.

Year 2022 face_with_colon_three

For more than 250 years, mathematicians have wondered if the Euler equations might sometimes fail to describe a fluid’s flow. A new computer-assisted proof marks a major breakthrough in that quest.

ALGORITHMS TURN PHOTO SHAPSHOTS INTO 3D VIDEO AND OR IMMERSIVE SPACE. This has been termed “Neural Radiance Fields.” Now Google Maps wants to turn Google Maps into a gigantic 3D space. Three videos below demonstrate the method. 1) A simple demonstration, 2) Google’s immersive maps, and 3) Using this principle to make dark, grainy photographs clear and immersive.

This technique is different from “time of flight” cameras which make a 3D snapshot based on the time light takes to travel to and from objects, but combined with this technology, and with a constellation of microsatellites as large as cell phones, a new version of “Google Earth” with live, continual imaging of the whole planet could eventually be envisioned.

Continue reading “NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images” »

Do you want to get started with Quantum Machine Learning? Have a look at Hands-On Quantum Machine Learning With Python.

This article will explain the most important parts of the Quantum Approximate Optimization Algorithm (QAOA). QAOA is a machine learning algorithm that you can use to solve combinatorial optimization problems.

Summary: Examining the cognitive abilities of the AI language model, GPT-3, researchers found the algorithm can keep up and compete with humans in some areas but falls behind in others due to a lack of real-world experience and interactions.

Source: Max Planck Institute.

Researchers at the Max Planck Institute for Biological Cybernetics in Tübingen have examined the general intelligence of the language model GPT-3, a powerful AI tool.