A combination of satellite images and deep learning computer algorithms can spot elephants from space as well as the human eye.
Very interesting.
Albert Einstein’s theory of general relativity profoundly changed our thinking about fundamental concepts in physics, such as space and time. But it also left us with some deep mysteries. One was black holes, which were only unequivocally detected over the past few years. Another was “wormholes” – bridges connecting different points in spacetime, in theory providing shortcuts for space travellers.
New technology from Stanford scientists finds long-hidden quakes, and possible clues about how earthquakes evolve.
Tiny movements in Earth’s outermost layer may provide a Rosetta Stone for deciphering the physics and warning signs of big quakes. New algorithms that work a little like human vision are now detecting these long-hidden microquakes in the growing mountain of seismic data.
Measures of Earth’s vibrations zigged and zagged across Mostafa Mousavi’s screen one morning in Memphis, Tenn. As part of his PhD studies in geophysics, he sat scanning earthquake signals recorded the night before, verifying that decades-old algorithms had detected true earthquakes rather than tremors generated by ordinary things like crashing waves, passing trucks or stomping football fans.
I like this idea. I don’t want AI to be a black box, I want to know what’s happening and how its doing it.
The field of artificial intelligence has created computers that can drive cars, synthesize chemical compounds, fold proteins, and detect high-energy particles at a superhuman level.
However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation.
Continue reading “How Explainable Artificial Intelligence Can Help Humans Innovate” »
https://www.youtube.com/watch?v=SV774u0LrOA
Samsung’s memory technology innovates artificial intelligence and Big Data analytics to bring impactful change to the way we live, work, and interact with each other. Through next-generation memory technology that enables faster and more complex tasks in AI and Big Data, Samsung takes part in the revolutionary advancement of technology that is enriching our everyday lives.
https://www.youtube.com/watch?v=syFLnFSa6tg&feature=youtu.be
Oneskin — the first skin cream that destroys senescent cells:
Longevity, Health, Long Lifespans, and Halthspans, Psychology, Spirituality — I and Carolina Reis Oliveira talk about all these things in relation to the skin. Find out how you can have very healthy skin with OneSkin!
A new algorithm capable of inferring goals and plans could help machines better adapt to the imperfect nature of human planning.
In a classic experiment on human social intelligence by psychologists Felix Warneken and Michael Tomasello (see video below), an 18-month old toddler watches a man carry a stack of books towards an unopened cabinet. When the man reaches the cabinet, he clumsily bangs the books against the door of the cabinet several times, then makes a puzzled noise.
Researchers at Osaka City University use quantum superposition states and Bayesian inference to create a quantum algorithm, easily executable on quantum computers, that accurately and directly calculates energy differences between the electronic ground and excited spin states of molecular systems in polynomial time.
Understanding how the natural world works enables us to mimic it for the benefit of humankind. Think of how much we rely on batteries. At the core is understanding molecular structures and the behavior of electrons within them. Calculating the energy differences between a molecule’s electronic ground and excited spin states helps us understand how to better use that molecule in a variety of chemical, biomedical and industrial applications. We have made much progress in molecules with closed-shell systems, in which electrons are paired up and stable. Open-shell systems, on the other hand, are less stable and their underlying electronic behavior is complex, and thus more difficult to understand. They have unpaired electrons in their ground state, which cause their energy to vary due to the intrinsic nature of electron spins, and makes measurements difficult, especially as the molecules increase in size and complexity.
Weird, right?
The team’s critical insight was to construct a “viral language” of sorts, based purely on its genetic sequences. This language, if given sufficient examples, can then be analyzed using NLP techniques to predict how changes to its genome alter its interaction with our immune system. That is, using artificial language techniques, it may be possible to hunt down key areas in a viral genome that, when mutated, allow it to escape roaming antibodies.
It’s a seriously kooky idea. Yet when tested on some of our greatest viral foes, like influenza (the seasonal flu), HIV, and SARS-CoV-2, the algorithm was able to discern critical mutations that “transform” each virus just enough to escape the grasp of our immune surveillance system.
Artificial intelligence and machine learning are already an integral part of our everyday lives online. For example, search engines such as Google use intelligent ranking algorithms, and video streaming services such as Netflix use machine learning to personalize movie recommendations.
As the demands for AI online continue to grow, so does the need to speed up AI performance and find ways to reduce its energy consumption.
Now a University of Washington-led team has come up with a system that could help: an optical computing core prototype that uses phase-change material. This system is fast, energy efficient and capable of accelerating the neural networks used in AI and machine learning. The technology is also scalable and directly applicable to cloud computing.
