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Microsoft Vision AI Developer Kit Simplifies Building Vision-Based Deep Learning Projects

Computer vision is one of the most popular applications of artificial intelligence. Image classification, object detection and object segmentation are some of the use cases of computer vision-based AI. These techniques are used in a variety of consumer and industrial scenarios. From face recognition-based user authentication to inventory tracking in warehouses to vehicle detection on roads, computer vision is becoming an integral part of next-generation applications.

Computer vision uses advanced neural networks and deep learning algorithms such as Convolutional Neural Networks (CNN), Single Shot Multibox Detector (SSD) and Generative Adversarial Networks (GAN). Applying these algorithms requires a thorough understanding of neural network architecture, advanced mathematics and image processing techniques. For an average ML developer, CNN remains to be a complex branch of AI.

Apart from the knowledge and understanding of algorithms, CNNs demand high end, expensive infrastructure for training the models, which is out of reach for most of the developers.

Technology firms vie for billions in data-analytics contracts

Consultants at Gartner recently calculated that in 2021 “ai augmentation” will create $2.9trn of “business value” and save 6.2bn man-hours globally. A survey by McKinsey last year estimated that ai analytics could add around $13trn, or 16%, to annual global gdp by 2030. Retail and logistics stand to gain most.


Two surprising leaders have emerged from the pack.

Amazon CEO Jeff Bezos shares insights on Rivian’s $700 million investment

During Amazon’s all-hands meeting in March, CEO Jeff Bezos stated that he is fascinated by the emerging trends in the auto industry. Bezos noted that it was this fascination that ultimately played a part in Amazon’s hefty $700 million investment in electric truck startup Rivian Automotive back in February.

“If you think about the auto industry right now, there’s so many things going on with Uber-ization, electrification, the connected car — so it’s a fascinating industry. It’s going to be something very interesting to watch and participate in, and I’m very excited about that whole industry,” Bezos said.

Bezos’ optimism for emerging industries extends beyond the electric car market. Apart from Rivian, Amazon has also invested in self-driving startup Aurora, hinting that the CEO is also looking to capitalize on autonomous driving technology for the e-commerce giant’s operations in the future. If its investment in Aurora pans out, for example, Amazon would likely gain an optimized solution that would allow the company to deliver shipments to its customers using self-driving machines.

Tesla battery researcher unveils new cell that could last 1 million miles in ‘robot taxis’

When talking about the economics of Tesla’s future fleet of robotaxis at the Tesla Autonomy Event, Tesla CEO Elon Musk emphasized that the vehicles need to be durable in order for the economics to work:

“The cars currently built are all designed for a million miles of operation. The drive unit is design, tested, and validated for 1 million miles of operation.”

But the CEO admitted that the battery packs are not built to last 1 million miles.

NASA Television to Air Launch, Capture of Cargo Ship to Space Station

A Japanese cargo spacecraft loaded with more than four tons of supplies, spare parts and experiment hardware is scheduled to launch from the Tanegashima Space Center in southern Japan to the International Space Station at 5:33 p.m. EDT Tuesday, Sept. 10 (6:33 a.m. Sept. 11 in Japan). Live coverage of the launch and capture will air on NASA Television and the agency’s website.

The Japan Aerospace Exploration Agency (JAXA) unpiloted H-II Transport Vehicle-8 (HTV-8) will launch on a Japanese H-IIB rocket on the tenth anniversary of the first HTV cargo spacecraft launch. Live coverage will begin at 5 p.m.

The spacecraft will arrive at the station Saturday, Sept. 14. Live coverage of the spacecraft rendezvous and capture will begin at 5:30 a.m. Expedition 60 Flight Engineer Christina Koch of NASA, backed up by her NASA crewmate Andrew Morgan, will operate the station’s Canadarm2 robotic arm from the station’s cupola to capture the 12-ton spacecraft as it approaches from below. Robotics flight controllers will then take over the operation of the arm to install HTV-8 to the Earth-facing port of the Harmony module where it will spend a month attached. Flight Engineer Luca Parmitano of ESA (European Space Agency) will monitor HTV-8 systems during its approach to the station.

As computers play a bigger role in warfare, the dangers to humans rise

T HE CONTEST between China and America, the world’s two superpowers, has many dimensions, from skirmishes over steel quotas to squabbles over student visas. One of the most alarming and least understood is the race towards artificial-intelligence-enabled warfare. Both countries are investing large sums in militarised artificial intelligence (AI), from autonomous robots to software that gives generals rapid tactical advice in the heat of battle. China frets that America has an edge thanks to the breakthroughs of Western companies, such as their successes in sophisticated strategy games. America fears that China’s autocrats have free access to copious data and can enlist local tech firms on national service. Neither side wants to fall behind.

Scientists develop a deep learning method to solve a fundamental problem in statistical physics

A team of scientists at Freie Universität Berlin has developed an Artificial Intelligence (AI) method that provides a fundamentally new solution of the “sampling problem” in statistical physics. The sampling problem is that important properties of materials and molecules can practically not be computed by directly simulating the motion of atoms in the computer because the required computational capacities are too vast even for supercomputers. The team developed a deep learning method that speeds up these calculations massively, making them feasible for previously intractable applications. “AI is changing all areas of our life, including the way we do science,” explains Dr. Frank Noé, professor at Freie Universität Berlin and main author of the study. Several years ago, so-called deep learning methods bested human experts in pattern recognition—be it the reading of handwritten texts or the recognition of cancer cells from medical images. “Since these breakthroughs, AI research has skyrocketed. Every day, we see new developments in application areas where traditional methods have left us stuck for years. We believe our approach could be such an advance for the field of statistical physics.” The results were published in Science.

Statistical Physics aims at the calculation of properties of materials or molecules based on the interactions of their constituent components—be it a metal’s melting temperature, or whether an antibiotic can bind to the molecules of a bacterium and thereby disable it. With statistical methods, such properties can be calculated in the computer, and the properties of the material or the efficiency of a specific medication can be improved. One of the main problems when doing this calculation is the vast computational cost, explains Simon Olsson, a coauthor of the study: “In principle we would have to consider every single structure, that means every way to position all the atoms in space, compute its probability, and then take their average. But this is impossible because the number of possible structures is astronomically large even for small molecules.