Until recently, the field of plant breeding looked a lot like it did in centuries past. A breeder might examine, for example, which tomato plants were most resistant to drought and then cross the most promising plants to produce the most drought-resistant offspring. This process would be repeated, plant generation after generation, until, over the course of roughly seven years, the breeder arrived at what seemed the optimal variety. Researchers at ETH Zürich use standard color images and thermal images collected by drone to determine how plots of wheat with different genotypes vary in grain ripeness. Image credit: Norbert Kirchgessner (ETH Zürich, Zürich, Switzerland). Now, with the global population expected to swell to nearly 10 billion by 2050 (1) and climate change shifting growing conditions (2), crop breeder and geneticist Steven Tanksley doesn’t think plant breeders have that kind of time. “We have to double the productivity per acre of our major crops if we’re going to stay on par with the world’s needs,” says Tanksley, a professor emeritus at Cornell University in Ithaca, NY. To speed up the process, Tanksley and others are turning to artificial intelligence (AI). Using computer science techniques, breeders can rapidly assess which plants grow the fastest in a particular climate, which genes help plants thrive there, and which plants, when crossed, produce an optimum combination of genes for a given location, opting for traits that boost yield and stave off the effects of a changing climate. Large seed companies in particular have been using components of AI for more than a decade. With computing power rapidly advancing, the techniques are now poised to accelerate breeding on a broader scale. AI is not, however, a panacea. Crop breeders still grapple with tradeoffs such as higher yield versus marketable appearance. And even the most sophisticated AI …

In 2018 the United Nations Secretary-General identified responsible research and innovation (RRI) in science and technology as an approach for academia, the private sector and governments to work on the mitigation of risks that are posed by new technologies. This report explores how RRI could help to address the humanitarian and strategic risks that may result from the development, diffusion and military use of artificial intelligence (AI) and thereby achieve arms control objectives on the military use of AI. The report makes recommendations on how the arms control community could build on existing responsible AI initiatives and export control and compliance systems to engage with academia and the private sector in the governance of risks to international peace and security posed by the military use of AI. Luke Richards is a Research Assistant working on emerging military and security technologies. Kolja Brockmann is a Researcher in the SIPRI Dual-Use and Arms Trade Control programme.

"Hell is when other people are fake" -- Jean-Paul Sartre writing in 2020. Have you ever read Jean-Paul Sartre's famous play No Exit? The main character, Garcin, cries out "Hell is--other people!" after realizing hell is not torture racks and fire, but interpersonal strife manufactured by Lucifer to push sufferers to the brink of psychological collapse. Physical torture would be infinitely easier to bear, Garcin declares, than the vicissitudes of continuous socialization. If you had roommates in quarantine, you might relate.

The sheer volume of rhetoric and shill-like babble about military AI is already gigantic. The constant stream of new AI in military roles is making headlines every day. Everyone has an opinion; answers to questions, maybe not. The likely effect of super-lethal AI-operated weapons is one of those questions. "Slaughter" is the more usual answer.

Early this spring as the pandemic began accelerating, AJ Venkatakrishnan took genetic data from 10,967 samples of the novel coronavirus and fed it into a machine. The Stanford-trained data scientist did not have a particular hypothesis, but he was hoping the artificial intelligence would pinpoint possible weaknesses that could be exploited to develop therapies. He was awed when the program reported back that the new virus appeared to have a snippet of DNA code - "RRARSAS" - distinct from its predecessor coronaviruses. This sequence, he learned, mimics a protein that helps the human body regulate salt and fluid balance. Venkatakrishnan, director of scientific research and partnerships at AI start-up Nference, wondered whether this change might allow the virus to act as a kind of Trojan horse. Could this explain its high infection and transmission rates?

FREMONT, CA: Cybercriminals are developing new and sophisticated ways to access controls, firewalls, and jeopardizing highly secure networks. AI-powered solutions are necessary to solve security issues and provide opportunities to develop more robust solutions. AI will substantially boost security systems, minimizing criminal intelligence using million of resources in case of an attack or malicious behavior. AI can enhance and track important processes in the data center. Its calculative powers and constant monitoring abilities offer insights into what would optimize hardware and infrastructure efficiency and security.

Distraction behind the wheel, even for a split second, can mean serious injury or death. The Federal Motor Carrier Safety Administration (FMCSA) indicates that distraction or inattention is the second most common driver-related cause of fatalities for commercial truck operators. Data analyzed in 2019 from San Diego-based Lytx's client base showed that drivers who multi-task –such as eating, drinking, smoking and using a phone – increase their risk of an accident by 100%. The company's Vice President of Safety Services Del Lisk, who has been in the safety industry for 30 years, has witnessed how technology has become more sophisticated in preventing distracted driving incidents. Lisk spoke with Waste360 about machine-based learning, artificial intelligence and how data gathered through video telematics can aid companies with risk reduction.

Master Python By Implementing Face Recognition & Image Processing In Python Created by Emenwa Global Students also bought Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs Python for Computer Vision with OpenCV and Deep Learning Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Autonomous Cars: Deep Learning and Computer Vision in PythonPreview this course Udemy GET COUPON CODE Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner.

New confirmed cases of the coronavirus in the U.S. have climbed to an all-time high of more than 86,000 per day on average, in a glimpse of the worsening crisis that lies ahead for the winner of the presidential election. Cases and hospitalizations are setting records all around the country just as the holidays and winter approach, demonstrating the challenge that either President Donald Trump or former Vice President Joe Biden will face in the coming months. Daily new confirmed coronavirus cases in the U.S. have surged 45% over the past two weeks, to a record 7-day average of 86,352, according to data compiled by Johns Hopkins University. Deaths are also on the rise, up 15 percent to an average of 846 deaths every day. The total U.S. death toll is already more than 232,000, and total confirmed U.S. cases have surpassed 9 million.

The Brazilian government has published a National Innovation Policy (NIP) setting out plans to encourage and develop innovative products, processes and services across the country. The areas include improving skills; widening the innovation talent pool; encouraging international engagement; and stimulating research, development and innovation within the Brazilian private sector. The government says the NIP will promote the coordination and distribution of public funds towards the advancement of innovation. An Innovation Committee, managed by the Ministry of Science, Technology and Innovations (MCTI) and chaired by the presidential office, will oversee the wide-ranging project. It is due to publish a detailed National Innovation Strategy in the near future, the technology website reported.