Lifeboat Foundation

A new study led by Northern Illinois University geography professor Wei Luo calculates the amount of water needed to carve the ancient network of valleys on Mars and concludes the Red Planet’s surface was once much more watery than previously thought.

The study bolsters the idea that Mars once had a warmer climate and active hydrologic cycle, with water evaporating from an ancient ocean, returning to the surface as rainfall and eroding the planet’s extensive network of valleys.

Satellites orbiting Mars and rovers on its surface have provided scientists with convincing evidence that water helped shape the planet’s landscape billions of years ago. But questions have lingered over how much water actually flowed on the planet, and the ocean hypothesis has been hotly debated.

IBM, its Research Alliance partners Globalfoundries and Samsung, and equipment suppliers have developed an industry-first process to build silicon nanosheet transistors that will enable 5 nanometer (nm) chips. The details of the process will be presented at the 2017 Symposia on VLSI Technology and Circuits conference in Kyoto, Japan. In less than two years since developing a 7nm test node chip with 20 billion transistors, scientists have paved the way for 30 billion switches on a fingernail-sized chip.

The resulting increase in performance will help accelerate cognitive computing, the Internet of Things (IoT), and other data-intensive applications delivered in the cloud. The power savings could also mean that the batteries in smartphones and other mobile products could last two to three times longer than today’s devices, before needing to be charged.

Scientists working as part of the IBM-led Research Alliance at the SUNY Polytechnic Institute Colleges of Nanoscale Science and Engineering’s NanoTech Complex in Albany, NY achieved the breakthrough by using stacks of silicon nanosheets as the device structure of the transistor, instead of the standard FinFET architecture, which is the blueprint for the semiconductor industry up through 7nm node technology.

Watch the world’s first drone skydive!

Credit: Explorist

The subconscious communicates truths better than intellect.

Actor Ethan Hawke on ambiguity.

How old, black and white photos get colorized — and why artists do it despite criticism.

A ban on human-animal hybrids was just lifted.

ICYMI: A ban on human/animal hybrids has been lifted.

It will be the longest sea crossing in the world.

This foldable bike helmet can fit into a water bottle.

Rice University computer scientists have adapted a widely used technique for rapid data lookup to slash the amount of computation — and thus energy and time — required for deep learning, a computationally intense form of machine learning.

“This applies to any deep-learning architecture, and the technique scales sublinearly, which means that the larger the deep neural network to which this is applied, the more the savings in computations there will be,” said lead researcher Anshumali Shrivastava, an assistant professor of computer science at Rice.

The research will be presented in August at the KDD 2017 conference in Halifax, Nova Scotia. It addresses one of the biggest issues facing tech giants like Google, Facebook and Microsoft as they race to build, train and deploy massive deep-learning networks for a growing body of products as diverse as self-driving cars, language translators and intelligent replies to emails.