Lifeboat Foundation

We probably think we know gravity pretty well. After all, we have more conscious experience with this fundamental force than with any of the others (electromagnetism and the weak and strong nuclear forces). But even though physicists have been studying gravity for hundreds of years, it remains a source of mystery.

In our video Why Is Gravity Different? We explore why this force is so perplexing and why it remains difficult to understand how Einstein’s general theory of relativity (which covers gravity) fits together with quantum mechanics.

Continue reading “To Understand Gravity, Toss a Hard Drive into a Black Hole” »

When landing Apollo 11 in 1969, astronauts looked out the window for distinguishing features that they recognized from maps of the Moon and were able to steer the lander to avoid a disastrous touchdown on top of a rocky area. Now, 50 years later, the process can be automated. Distinguishing features, like known craters, boulders, or other unique surface characteristics, provide insight into surface hazards to help avoid them while landing.

NASA scientists and engineers are maturing technology for navigating and landing on planetary bodies by analyzing images during descent – a process called terrain relative navigation (TRN). This optical navigation technology is included on NASA’s newest Mars rover, Perseverance, which will test TRN when it lands on the Red Planet in 2021, paving the way for future crewed missions to the Moon and beyond. TRN was also being used during NASA’s recent Origins, Spectral Interpretation, Resources Identification, Security, Regolith Explorer (OSIRIS-REx) mission Touch-and-Go (TAG) event to collect samples of the asteroid Bennu in order to better understand the characteristics and movement of asteroids.

Since reaching Bennu in 2018, the OSIRIS-REx spacecraft has mapped and studied its surface, including its topography and lighting conditions, in preparation for TAG. Nightingale crater was chosen from four candidate sites based on its great amount of sampleable material and accessibility for the spacecraft.

The ability to cancel magnetic fields has benefits in quantum technology, biomedicine, and neurology.

A team of scientists including two physicists at the University of Sussex has found a way to circumvent a 178-year old theory which means they can effectively cancel magnetic fields at a distance. They are the first to be able to do so in a way that has practical benefits.

The work is hoped to have a wide variety of applications. For example, patients with neurological disorders such as Alzheimer’s or Parkinson’s might in the future receive a more accurate diagnosis. With the ability to cancel out ‘noisy’ external magnetic fields, doctors using magnetic field scanners will be able to see more accurately what is happening in the brain.

Let’s Encrypt has warned that older Android phones won’t support many secure websites after they lose key certificates by September 2021.

The newly discovered malware uses GitHub and Pastebin to house component code, and harbors 12 different initial attack vectors.

A small team of astronomers have found a new way to ‘see’ the elusive dark matter halos that surround galaxies, with a new technique 10 times more precise than the previous-best method. The work is published in Monthly Notices of the Royal Astronomical Society.

Scientists currently estimate that up to 85% of the mass in the universe is effectively invisible. This ‘dark matter’ cannot be observed directly, because it does not interact with light in the same way as the ordinary matter that makes up stars, planets, and life on Earth.

So how do we measure what cannot be seen? The key is to measure the effect of gravity that the dark matter produces.

This article was published as a part of the Data Science Blogathon.

Introduction

Computer Vision is evolving from the emerging stage and the result is incredibly useful in various applications. It is in our mobile phone cameras which are able to recognize faces. It is available in self-driving cars to recognize traffic signals, signs, and pedestrians. Also, it is in industrial robots to monitor problems and navigating around co-workers.

You’ve probably heard us say this countless times: GPT-3, the gargantuan AI that spews uncannily human-like language, is a marvel. It’s also largely a mirage. You can tell with a simple trick: Ask it the color of sheep, and it will suggest “black” as often as “white”—reflecting the phrase “black sheep” in our vernacular.

That’s the problem with language models: because they’re only trained on text, they lack common sense. Now researchers from the University of North Carolina, Chapel Hill, have designed a new technique to change that. They call it “vokenization,” and it gives language models like GPT-3 the ability to “see.”

It’s not the first time people have sought to combine language models with computer vision. This is actually a rapidly growing area of AI research. The idea is that both types of AI have different strengths. Language models like GPT-3 are trained through unsupervised learning, which requires no manual data labeling, making them easy to scale. Image models like object recognition systems, by contrast, learn more directly from reality. In other words, their understanding doesn’t rely on the kind of abstraction of the world that text provides. They can “see” from pictures of sheep that they are in fact white.

LG is betting on unorthodox forms of smartphones to attract customers.