Ford teamed up with Domino's last year to test out self-driving cars for pizza delivery. And while a hot cheesy pie is certainly delicious, the autonomous technology is now being put to even better use. On Wednesday, Ford announced that it is now using Postmates delivery service to bring Walmart products to customers' homes via self-driving Ford vehicles. Ford works with Argo AI to power the self-driving part of the car. SEE ALSO: Ford and Domino's team up for self-driving pizza deliveries Starting in the Miami area, where Domino's is still testing autonomous pizza delivery and Ford has developed a urban self-driving car proving ground, the service will kick off with Walmart employees putting groceries into the car.

A "smart" wildfire sensor developed by two high school students in Cupertino, CA (Photo: Google) Google has set aside $25 million to fund research work by schools and other organizations using machine learning for "social good." Besides cash, the company's "AI for Social Good" project is also offering support from its artificial intelligence experts, credits and consulting from Google Cloud. Those chosen will also join a "launchpad" accelerator program with mentoring, support and access to Silicon Valley experts. Projects seeking funding need to address a societal challenge and have a clear plan to deploy the AI model for real-world impact. Organizations will have until the end of January 21, 2019 to submit their applications.

A "smart" wildfire sensor developed by two high school students in Cupertino, CA (Photo: Google) Google has set aside a $25 million pool to fund research work by schools and other organizations using machine learning for "social good." Besides cash, the company's "AI for Social Good" project is also offering support from its artificial intelligence experts, credits and consulting from Google Cloud. Those chosen will also join a "launchpad" accelerator program with mentoring, support and access to Silicon Valley experts. Projects seeking funding need to address a societal challenge and have a clear plan to deploy the AI model for real-world impact. Organizations will have until the end of January 21, 2019 to submit their applications.

Data center operators deploying tools that rely on machine learning today are benefiting from initial gains in efficiency and reliability, but they've only started to scratch the surface of the full impact machine learning will have on data center management. Machine learning, a subset of Artificial Intelligence, is expected to optimize every facet of future data center operations, including planning and design, managing IT workloads, ensuring uptime, and controlling costs. By 2022, IDC predicts that 50 percent of IT assets in data centers will be able to run autonomously because of embedded AI functionality. "This is the future of data center management, but we are still in the early stages," Rhonda Ascierto, VP of research at Uptime Institute, said. Creating smarter data centers becomes increasingly important as more companies adopt a hybrid environment that includes the cloud, colocation facilities, and in-house data centers and will increasingly include edge sites, Jennifer Cooke, research director of IDC's Cloud to Edge Datacenter Trends service, said.

In this tutorial, you'll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you'll be comfortable applying it to your deep learning models. Facebook launched PyTorch 1.0 early this year with integrations for Google Cloud, AWS, and Azure Machine Learning. In this tutorial, I assume that you're already familiar with Scikit-learn, Pandas, NumPy, and SciPy. These packages are important prerequisites for this tutorial. Deep learning is a subfield of machine learning with algorithms inspired by the working of the human brain.

The next generation of unmanned drones will act more like birds than machines, thanks to new study by researchers from RMIT University in Melbourne and ISAE-Supaéro in Toulouse. The study includes experiments with drones that can sense wind gusts and thermals, then use them to gain speed or altitude, just like birds do. Dr Abdulghani Mohamed, who leads a large research program into bio-inspired technology in RMIT's Unmanned Aircraft Systems (UAS) research team, said the world-first project had exceeded expectations. "The results of our gust soaring system were remarkable and represent a big leap in energy harvesting for drones," Mohamed said. "This technology not only allows a drone to gain kinetic energy to fly faster but also means less work and more efficiency for the propulsion system, potentially enabling the next generation of drones to increase their flight time on limited resources."

A double lesson by two masters of thought to understand future scenarios: philosophical, but also practical. Some A.I. scientists believe that one day machines will have consciousness, but Roger Penrose said that would never happen as a machine, no matter how bright it was, would never be aware. A computer can take in data and give out information or new data. It can record light, heat, colour, and can detect taste. It could become so sophisticated that it might even look a human and have thoughts, ideas, emotions, be emphatic, but it would still not be conscious, says Roger Penrose.

Recently, analysts have argued that emerging technologies with military applications may undermine nuclear stability (see here, here, and here), but the logic of these arguments is debatable and overlooks a more straightforward reason why new technology might cause nuclear conflict: by upending the existing balance of power among nuclear-armed states. This latter concern is more probable and dangerous and demands an immediate policy response. For more than 70 years, the world has avoided major power conflict, and many attribute this era of peace to nuclear weapons. In situations of mutually assured destruction (MAD), neither side has an incentive to start a conflict because doing so will only result in its own annihilation. The key to this model of deterrence is the maintenance of secure second-strike capabilities--the ability to absorb an enemy nuclear attack and respond with a devastating counterattack.

Using our imagination is easy. We can all close our eyes, and think of ice cream, or cake, or even better, cake and ice cream. But teaching AI to imagine things has been very difficult up until a few years ago, with the advent of Dueling Neural Networks, also known as Generative Adversarial Networks or GANS. GANs is the next frontier in Machine Learning and it involves using two Neural Networks with opposing objectives to train one another, resulting in mind-blowing results. The first network is the generator which is programmed to generate images from random noise, with the goal to fool the other network called the discriminator, which is programmed to detect whether images are real or fake.

Huawei has unveiled its new smart cities digital platform utilising artificial intelligence (AI) and Internet of Things (IoT) capabilities, which it said could be used across smart public safety, environmental protection, transportation, government, education, and agriculture. Huawei's AI Digital Platform connects what it calls the brain, or command centre; the central nervous system, or network; and the peripheral nervous system, made up of sensors across a city. "Just like an operating system, the platform is compatible with different city sensors, creates a city digital twin, and supports diverse city applications," Huawei Enterprise Business Group VP Ma Yue said. The smart cities digital platform combines AI, IoT, big data, a geographic information system, video, cloud, converged communications, and security. "Huawei has also developed a middleware platform to provide services to software application partners. This is designed to help application partners quickly develop upper-layer applications to accelerate transformation and innovation in city management, city services, and industry development," the Chinese networking giant added.