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The AI-powered robot is named “Polly” and will pollinate truss tomato plants in Costa’s tomato glasshouse facilities in Guyra, New South Wales.

In its commercial application, Costa wrote on its website that these robotic pollinators will drive between the rows, detect flowers that are ripe for pollination utilizing artificial intelligence, and then emit air pulses to vibrate the flowers in a certain way that mimics buzz pollination that is carried out by bumblebees.

Compared to using insects, like bees, and the human laborers that are occasionally required to aid with the growth of particular crops, pollination robots could provide future farmers with a major advantage, which is to improve productivity.

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Berkeley Lab scientists assess the technology landscape for developing a domestic source of lithium.

If you had a jar of marbles of many different colors but wanted only the green ones, how could you efficiently pick them out? What if it wasn’t marbles but a jar of glitter, and there was sand, glue, and mud mixed in? That begins to describe the complexity of the brine pumped out from beneath California’s Salton Sea as part of geothermal energy production.

For geothermal fields around the world, produced geothermal brine has been simply injected back underground, but now it’s become clear that the brines produced at the Salton Sea geothermal field contain an immense amount of lithium, a critical resource need for low-carbon transportation and energy storage. Demand for lithium is skyrocketing, as it is an essential ingredient in lithium-ion batteries. Currently there is very little lithium production in the U.S. and most lithium is imported; however, that may change in the near future.

Researchers from the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have recently published a comprehensive review of past and current technologies for extracting minerals from geothermal brine. The review, published in the journal Energies, discusses and evaluates a broad array of technologies used for extraction of lithium from brines. The review finds that geothermal brines in the Salton Sea region of California are expected to be a major domestic source of lithium in the future but that significant technical challenges need to be overcome.

Continue reading “Sizing Up the Challenges in Extracting Lithium from Geothermal Brine” | >

EPFL researchers have used swarms of drones to measure city traffic with unprecedented accuracy and precision. Algorithms are then used to identify sources of traffic jams and recommend solutions to alleviate traffic problems.

Given the wealth of modern technology available—roadside cameras, big-data algorithms, Bluetooth and RFID connections, and smartphones in every pocket—transportation engineers should be able to accurately measure and forecast . However, current tools advance towards the direction of showing the symptom but systematically fail to find the root cause, let alone fix it. Researchers at EPFL utilize a monitoring tool that overcomes many problems using drones.

“They provide excellent visibility, can cover large areas and are relatively affordable. What’s more, they offer greater precision than GPS technology and eliminate the behavioral biases that occur when people know they’re being watched. And we use drones in a way that protects people’s identities,” says Manos Barmpounakis, a post-doc researcher at EPFL’s Urban Transport Systems Laboratory (LUTS).

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NVIDIA introduces QODA, a new platform for hybrid quantum-classical computing, enabling easy programming of integrated CPU, GPU, and QPU systems.

The past decade has seen quantum computing leap out of academic labs into the mainstream. Efforts to build better quantum computers proliferate at both startups and large companies. And while it is still unclear how far we are away from using quantum advantage on common problems, it is clear that now is the time to build the tools needed to deliver valuable quantum applications.

To start, we need to make progress in our understanding of quantum algorithms. Last year, NVIDIA announced cuQuantum, a software development kit (SDK) for accelerating simulations of quantum computing. Simulating quantum circuits using cuQuantum on GPUs enables algorithms research with performance and scale far beyond what can be achieved on quantum processing units (QPUs) today. This is paving the way for breakthroughs in understanding how to make the most of quantum computers.

In addition to improving quantum algorithms, we also need to use QPUs to their fullest potential alongside classical computing resources: CPUs and GPUs. Today, NVIDIA is announcing the launch of Quantum Optimized Device Architecture (QODA), a platform for hybrid quantum-classical computing with the mission of enabling this utility.

Continue reading “Introducing QODA: The Platform for Hybrid Quantum-Classical Computing” | >

Inspired by research into how infants learn, computer scientists have created a program that can pick up simple physical rules about the behaviour of objects — and express surprise when they seem to violate those rules. The results were published on 11 July in Nature Human Behaviour1.

Developmental psychologists test how babies understand the motion of objects by tracking their gaze. When shown a video of, for example, a ball that suddenly disappears, the children express surprise, which researchers quantify by measuring how long the infants stare in a particular direction.

Luis Piloto, a computer scientist at Google-owned company DeepMind in London, and his collaborators wanted to develop a similar test for artificial intelligence (AI). The team trained a neural network — a software system that learns by spotting patterns in large amounts of data — with animated videos of simple objects such as cubes and balls.

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