Lifeboat Foundation

Deep Learning systems can achieve remarkable, even super-human performance through supervised learning on large, labeled datasets. However, there are two problems: First, collecting ever more labeled data is expensive in both time and money. Second, these deep neural networks will be high performers on their task, but cannot easily generalize to other, related tasks, or they need large amounts of data to do so. In this blog post, Yann LeCun and Ishan Misra of Facebook AI Research (FAIR) describe the current state of Self-Supervised Learning (SSL) and argue that it is the next step in the development of AI that uses fewer labels and can transfer knowledge faster than current systems. They suggest as a promising direction to build non-contrastive latent-variable predictive models, like VAEs, but ones that also provide high-quality latent representations for downstream tasks.

OUTLINE:
0:00 — Intro & Overview.
1:15 — Supervised Learning, Self-Supervised Learning, and Common Sense.
7:35 — Predicting Hidden Parts from Observed Parts.
17:50 — Self-Supervised Learning for Language vs Vision.
26:50 — Energy-Based Models.
30:15 — Joint-Embedding Models.
35:45 — Contrastive Methods.
43:45 — Latent-Variable Predictive Models and GANs.
55:00 — Summary & Conclusion.

Continue reading “Yann LeCun — Self-Supervised Learning: The Dark Matter of Intelligence (FAIR Blog Post Explained)” »

Scientists have been able to trap antimatter particles using a combination of electric and magnetic fields. Antiprotons have been stored for over a year, while antimatter electrons have been stored for shorter periods of time, due to their lower mass. In 2011, researchers at CERN announced that they had stored antihydrogen for over 1,000 seconds.

While scientists have been able to store and manipulate small quantities of antimatter, they have not been able to answer why antimatter is so rare in the universe. According to Einstein’s famous equation E = mc², energy should convert into matter and antimatter in equal quantities. And, immediately after the Big Bang, there was a lot of energy. Accordingly, we should see as much antimatter as matter in our universe, and yet we don’t. This is a pressing unsolved mystery of modern physics.

Continue reading “Scientists Make Big Step Towards Making Antimatter Stand Still” »

A team of researchers from TU Delft managed to design one of the world’s most precise microchip sensors. The device can function at room temperature—a ‘holy grail’ for quantum technologies and sensing. Combining nanotechnology and machine learning inspired by nature’s spiderwebs, they were able to make a nanomechanical sensor vibrate in extreme isolation from everyday noise. This breakthrough, published in the Advanced Materials Rising Stars Issue, has implications for the study of gravity and dark matter as well as the fields of quantum internet, navigation and sensing.

One of the biggest challenges for studying vibrating objects at the smallest scale, like those used in sensors or quantum hardware, is how to keep ambient thermal noise from interacting with their fragile states. Quantum hardware for example is usually kept at near absolute zero (−273.15°C) temperatures, and refrigerators cost half a million euros apiece. Researchers from TU Delft created a web-shaped microchip sensor that resonates extremely well in isolation from room temperature noise. Among other applications, their discovery will make building quantum devices much more affordable.

#Universe #Documentary #Space.

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A dead star is spinning so rapidly, it officially has the fastest known spin rate of any star of its kind.

It’s a white dwarf star, named LAMOST J024048.51+195226.9 (J0240+1952 for short) and located 2,015 light-years away, and it has an insane rotation rate of just 25 seconds. That pips the previous record holder by a significant margin – CTCV J2056-3014, with a spin rate of 29 seconds.

It also bears a close similarity to another fast white dwarf, AE Aquarii, which has a spin rate of 33 seconds.

When we talk about the distance to an object in the expanding Universe, we’re always taking a cosmic snapshot — a sort of “God’s eye view” — of how things are at this particular instant in time: when the light from these distant objects arrives. We know that we’re seeing these objects as they were in the distant past, not as they are today — some 13.8 billion years after the Big Bang — but rather as they were when they emitted the light that arrives today.

But when we talk about, “how far away is this object,” we’re not asking how far away it was from us when it emitted the light we’re now seeing, and we aren’t asking how long the light has been in transit. Instead, we’re asking how far away the object, if we could somehow “freeze” the expansion of the Universe right now, is located from us at this very instant. The farthest observed galaxy GN-z11, emitted its now-arriving light 13.4 billion years ago, and is located some 32 billion light-years away. If we could see all the way back to the instant of the Big Bang, we’d be seeing 46.1 billion light-years away, and if we wanted to know the most distant object whose light hasn’t yet reached us, but will someday, that’s presently a distance of ~61 billion light-years away: the future visibility limit.

New ways to measure the top supercomputers’ smarts in the AI field include searching for dark energy, predicting hurricanes, and finding new materials for energy storage.

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A new analysis of the South Pole-based telescope’s cosmic microwave background observations has all but ruled out several popular models of inflation.

Physicists looking for signs of primordial gravitational waves by sifting through the earliest light in the cosmos – the cosmic microwave background (CMB) – have reported their findings: still nothing.

But far from being a dud, the latest results from the BICEP3 experiment at the South Pole have tightened the bounds on models of cosmic inflation, a process that in theory explains several perplexing features of our universe and which should have produced gravitational waves shortly after the universe began.

24 Canon lenses strapped together with the power of a refracting telescope 1.8 meters in diameter.

An international team of researchers has bundled groups of 24 Canon EF 400mm f/2.8 lenses together into what they call the Dragonfly Telephoto Array in order to capture photos of distant stars.

The Dragonfly Telephoto Array is a telescope that is equipped with multiple Canon 400mm f/2.8L IS II USM lenses. The telescope array was designed in 2013 by the team, also named Project Dragonfly, which is an international research team from Yale University and the University of Toronto. The Dragonfly Telephoto Array is capable of capturing images of galaxies that are so faint and large that they had escaped detection by even the largest conventional telescopes. Its mission is to study the low surface brightness universe to elucidate the nature of dark matter and to utilize the concept of distributed telescopes.

Continue reading “Researchers Bundle 24 400mm Lenses into Massive Telescope Array” »