Lifeboat Foundation

Experimental facilities around the globe are facing a challenge: their instruments are becoming increasingly powerful, leading to a steady increase in the volume and complexity of the scientific data they collect. At the same time, these tools demand new, advanced algorithms to take advantage of these capabilities and enable ever-more intricate scientific questions to be asked—and answered. For example, the ALS-U project to upgrade the Advanced Light Source facility at Lawrence Berkeley National Laboratory (Berkeley Lab) will result in 100 times brighter soft X-ray light and feature superfast detectors that will lead to a vast increase in data-collection rates.

To make full use of modern instruments and facilities, researchers need new ways to decrease the amount of data required for scientific discovery and address data acquisition rates humans can no longer keep pace with. A promising route lies in an emerging field known as autonomous discovery, where algorithms learn from a comparatively little amount of input data and decide themselves on the next steps to take, allowing multi-dimensional parameter spaces to be explored more quickly, efficiently, and with minimal human intervention.

“More and more experimental fields are taking advantage of this new optimal and autonomous data acquisition because, when it comes down to it, it’s always about approximating some function, given noisy data,” said Marcus Noack, a research scientist in the Center for Advanced Mathematics for Energy Research Applications (CAMERA) at Berkeley Lab and lead author on a new paper on Gaussian processes for autonomous data acquisition published July 28 in Nature Reviews Physics. The paper is the culmination of a multi-year, multinational effort led by CAMERA to introduce innovative autonomous discovery techniques across a broad scientific community.

And it did so on its own without a tether.

Cassie, a bipedal robot that’s all legs, has successfully run five kilometers on a single charge, all without having a tether. The machine serves as the basis for Agility Robotics’ delivery robot Digit, as TechCrunch notes, though you may also remember it for “blindly” navigating a set of stairs. Oregon State University engineers were able to train Cassie in a simulator to enable it to go up and down a flight of stairs without the use of cameras or LIDAR. Now, engineers from the same team were able to train Cassie to run using a deep reinforcement learning algorithm.

Continue reading “Watch Cassie the bipedal robot run a 5K” »

Links to biological age calculators:
Levine’s PhenoAge calculator is embedded as an Excel file:

https://michaellustgarten.com/2019/09/09/quantifying-biological-age/

Continue reading “Quantifying Biological Age: Blood Test #3 in 2021” »

There are many reasons for drones to be quick. The professional drone racing circuit aside, speed bodes well when you are searching for survivors on a disaster site, or delivering cargo, or even inspecting critical infrastructure. But how do you get something done in the shortest possible time with limited battery life when you have to navigate through obstacles, changing speeds, and altitude? You use an algorithm.

For decades, researchers around the world have searched for ways to use solar power to generate the key reaction for producing hydrogen as a clean energy source—splitting water molecules to form hydrogen and oxygen. However, such efforts have mostly failed because doing it well was too costly, and trying to do it at a low cost led to poor performance.

Now, researchers from The University of Texas at Austin have found a low-cost way to solve one half of the equation, using sunlight to efficiently split off oxygen molecules from water. The finding, published recently in Nature Communications, represents a step forward toward greater adoption of hydrogen as a key part of our energy infrastructure.

As early as the 1970s, researchers were investigating the possibility of using solar energy to generate hydrogen. But the inability to find materials with the combination of properties needed for a device that can perform the key chemical reactions efficiently has kept it from becoming a mainstream method.

The tool next examines how one protein’s amino acids interact with another within the same protein, for example, by examining the distance between two distant building blocks. It’s like looking at your hands and feet fully stretched out, versus in a backbend measuring the distance between those extremities as you “fold” into a yoga pose.

Finally, the third track looks at 3D coordinates of each atom that makes up a protein building block—kind of like mapping the studs on a Lego block—to compile the final 3D structure. The network then bounces back and forth between these tracks, so that one output can update another track.

The end results came close to those of DeepMind’s tool, AlphaFold2, which matched the gold standard of structures obtained from experiments. Although RoseTTAFold wasn’t as accurate as AlphaFold2, it seemingly required much less time and energy. For a simple protein, the algorithm was able to solve the structure using a gaming computer in about 10 minutes.

Get Surfshark VPN at https://surfshark.deals/undecided and enter promo code UNDECIDED for 83% off and 3 extra months for free! What if we could take a battery pack’s weight out of the equation? Imagine a car that has no battery pack because the car’s structural battery is the pack? Let’s explore massless energy storage and how a recent breakthrough could be a dramatic shift in how we can store energy in phones, planes, cars… you name it. Watch Exploring When Solid State Batteries Will Arrive: https://youtu.be/3PyXQ0UXk9w?list=PLnTSM-ORSgi7UWp64ZlOKUPNXePMTdU4dSimulation from FLOW-3D®, developed by Flow Science, Inc. (www.flow3d.com).Video script and citations:
https://undecidedmf.com/episodes/exploring-massless-energy-b…hFollow-up podcast:
Video version — https://www.youtube.com/channel/UC4-aWB84Bupf5hxGqrwYqLA
Audio version — http://bit.ly/stilltbdfm.
👋 Support Undecided on Patreon!
https://www.patreon.com/mattferrell.
⚙️ Gear & Products I Like.
Tesla and smart home gear:
https://kit.co/undecidedmfUndecided Amazon store front:
http://bit.ly/UndecidedAmazonFun, nerdy t-shirts:
http://bit.ly/UndecidedShirtsAbstract Ocean Tesla Accessories:
15% Discount — Code: “Undecided“
http://bit.ly/UndecidedAOJeda Tesla Wireless Charger/USB Hub:
http://bit.ly/UndecidedJedaTesla Referral Code:
Get 1000 free supercharging miles.
or a discount on Tesla Solar & Powerwalls.
https://ts.la/matthew84515Visit my Energysage Portal:
Research solar panels and get quotes for free!
http://www.energysage.com/p/undecided/ Or find community solar near you:
https://communitysolar.energysage.com/?rc=p-undecided.
👉 Follow Me.

https://twitter.com/mattferrell.
https://twitter.com/undecidedMFInstagram.
https://www.instagram.com/mattferrell.
https://www.instagram.com/undecidedmfFacebook.
https://www.facebook.com/undecidedMF/Website.
https://undecidedmf.com.
📺 YouTube Tools I Recommend.
Audio file(s) provided by Epidemic Sound.
http://bit.ly/UndecidedEpidemicTubeBuddy.
https://www.tubebuddy.com/undecidedVidIQ
https://vidiq.com/undecided.
I may earn a small commission for my endorsement or recommendation to products or services linked above, but I wouldn’t put them here if I didn’t like them. Your purchase helps support the channel and the videos I produce. Thank you.

A new set of equations can precisely describe the reflections of the Universe that appear in the warped light around a black hole.

The proximity of each reflection is dependent on the angle of observation with respect to the black hole, and the rate of the black hole’s spin, according to a mathematical solution worked out by physics student Albert Sneppen of the Niels Bohr Institute in Denmark.

This is really cool, absolutely, but it’s not just really cool. It also potentially gives us a new tool for probing the gravitational environment around these extreme objects.

Sky surveys are invaluable for exploring the universe, allowing celestial objects to be catalogued and analyzed without the need for lengthy observations. But in providing a general map or image of a region of the sky, they are also one of the largest data generators in science, currently imaging tens of millions to billions of galaxies over the lifetime of an individual survey. In the near future, for example, the Vera C. Rubin Observatory in Chile will produce 20 TB of data per night, generate about 10 million alerts daily, and end with a final data set of 60 PB in size.

As a result, sky surveys have become increasingly labor-intensive when it comes to sifting through the gathered datasets to find the most relevant information or new discovery. In recent years machine learning has added a welcome twist to the process, primarily in the form of supervised and unsupervised algorithms used to train the computer models that mine the data. But these approaches present their own challenges; for example, supervised learning requires image labels that must be manually assigned, a task that is not only time-consuming but restrictive in scope; at present, only about 1% of all known galaxies have been assigned such labels.

Continue reading “Self-supervised machine learning adds depth, breadth and speed to sky surveys” »