Lifeboat Foundation

Qualcomm recently released a white paper titled, “The Future of AI is Hybrid.” In the paper, they outline a clear case that for AI to develop to its maximum capabilities, it needs to be processed both on the cloud and the edge. Computing at the edge would improve issues like cost, energy use, reliability, latency issues, privacy—all of the things that make scaling and growing a technology difficult. And they’re right: for AI to optimize fully, it needs more than one partner, more than one solution. But the greater lesson here is: that’s true for all technology moving forward.

When we hear the term “hybrid,” many of us think of hybrid cars—cars that run on both gasoline and electricity. We in the tech space eventually grabbed that term to refer to things like hybrid cloud —a situation where companies may process some of their data on the public cloud, private cloud, or data center in some type of mix. The goal in creating these hybrid models in technology was the same as it was with hybrid cars—to reduce energy consumption, improve costs, enhance performance.

The hybrid cars grew in popularity because they allowed users the enjoy the best qualities of both types of cars—gas and electric. Gas engines allow the hybrid to refuel quickly and move longer distances before needing fuel. The electric side helps cut emissions and save money. A similar concept is true for AI. AI needs somewhere powerful and stable for model training and inference, which require huge amounts of space for processing complex workloads. That’s where the cloud comes in. At the same time, AI also needs to happen fast. For it to be useful, it needs to process closer to where the action actually happens—the edge of a mobile device.

“Our model, based on laboratory experiments, confirms that the majority of chromium in Mercury is concentrated within its core.”

Mercury is the closest planet to the Sun, yet it is also the least understood. Mercury.

This odd planet features some of the most enduring mysteries, for instance, scientists aren’t sure where Mercury came from. How did its metallic core get so dense and big? Or how does this small planet, so near to the Sun, sustain even a thin atmosphere?

“Siri, play gold dust by flux pavilion”

The James Webb Space Telescope detected a cosmic phenomenon known as a kilonova, which can create gold, platinum, and uranium in space.

In 2003, the data visualization expert Edward Tufte traced that year’s Columbia disaster—in which seven astronauts died when their shuttle disintegrated—to a piece of software. It was PowerPoint, he argued, that prevented people at NASA from understanding the gravity of the risks facing the shuttle. PowerPoint all but forced “breaking up narratives and data into … minimal fragments,” “a preoccupation with format not content,” and “a smirky commercialism that turns information into a sales pitch.” Serious dangers got buried at the bottom of a multilevel hierarchy of bullet points under a bigger, sunnier title. If only the information had been delivered in a proper technical report, Tufte implied, the astronauts might still be alive.

Twenty years later, there’s a new office tool keeping us from fully expressing and processing important information: the digital whiteboard. These boards are vast canvases on which you can add and drag around virtually limitless quantities of text, images, tables, diagrams, emoji, and shapes. In their typical state, they are mostly covered with sticky notes on which people have written a word or three. What the words signify in context can quickly become hard to remember, but that’s OK. Like books used as decorations, they get their value from the fact that they signify something.

A recent discovery shows that the moon’s far side was volcanically active. A team of geologists found a large granite deposit beneath a long-extinct lunar volcano, supporting the theory that the moon’s surface glowed with lava billions of years ago.

The lunar find was under Compton-Belkovich, a rumored volcanic structure on the moon’s surface. The feature is thought to have developed from the lava that cooled after fueling the violent eruptions of lunar volcanoes some 3.5 billion years ago, according to Space.com.

Although the discovery of volcanic leftovers in this area was not predicted, the study team was taken aback by the extent of the cooled magma patch. Its breadth is around 31 miles (50 kilometers) wide. This large mass of granite in the Compton-Belkovich volcanic complex may shed light on how the lunar crust formed early in the moon’s history.

NASA’s James Webb Space Telescope has photographed a fiery spiral galaxy that has a very violent past and is located 131 million light-years away.

A scorching hot world where metal clouds rain drops of titanium is the most reflective planet ever observed outside of our Solar System, astronomers said on Monday.

This strange world, which is more than 260 light years from Earth, reflects 80 percent of the light from its host star, according to new observations from Europe’s exoplanet-probing Cheops space telescope.

Continue reading “‘Like a mirror’: Astronomers identify most reflective exoplanet” »

The near-perfect roundness deepens the mystery behind how the universe came to be filled with matter as opposed to antimatter.

Reservoir computing is a promising computational framework based on recurrent neural networks (RNNs), which essentially maps input data onto a high-dimensional computational space, keeping some parameters of artificial neural networks (ANNs) fixed while updating others. This framework could help to improve the performance of machine learning algorithms, while also reducing the amount of data required to adequately train them.

RNNs essentially leverage recurrent connections between their different processing units to process sequential data and make accurate predictions. While RNNs have been found to perform well on numerous tasks, optimizing their performance by identifying parameters that are most relevant to the task they will be tackling can be challenging and time-consuming.

Jason Kim and Dani S. Bassett, two researchers at University of Pennsylvania, recently introduced an alternative approach to design and program RNN-based reservoir computers, which is inspired by how programming languages work on computer hardware. This approach, published in Nature Machine Intelligence, can identify the appropriate parameters for a given network, programming its computations to optimize its performance on target problems.