A group of veterans inspired by the need to keep schools and public spaces safer have created a new technology they say can detect guns and send out alerts before shots are ever fired. Active shooter situations have played out across the country – a gunman opened fire inside a Florida high school, shots rang out at a Texas Walmart and multiple people were shot to death in an office building in Virginia Beach. The nation's most recent school shooting happened Thursday morning – when a 16-year-old high school student in Santa Clarita, California, opened fire in the campus quad, shooting five classmates and killing two. What if the gun was detected early – so early, the shooter was never able to get inside to hurt anyone? The technology to do that exists, and only WUSA9 was there when it was tested in Northern Virginia.

Oracle says it is entitled to at least $8.8 billion in damages. The case, which the court will resolve by July, promises to reshape the U.S. legal protections for software code, particularly the interfaces that let programs and devices communicate with one another. Google contends the appeals court ruling would make it harder to use interfaces to develop new applications. The ruling "has upended the computer industry's long-standing expectation that developers are free to use software interfaces to build new computer programs," Google argued. The appeals court decision reversed a jury finding that Google's copying was a legitimate "fair use" of Oracle's Java programming language.

A New Biology for a New Century Obstacles to an Exponential Increase in Synthetic Biology Productivity Machine Learning's Predictive Capabilities Machine Learning Needs Automation To Be Truly Effective Predictive Synthetic Biology Will Dramatically Impact Biology and Inspire Computer Science Biology has changed radically in the past two decades, transitioning from a descriptive science into a design science. The discovery of DNA as the repository of genetic information, and of recombinant DNA as an effective way to modify it, has first led into the development of genetic engineering and later the field of synthetic biology. Synthetic biology(1) goes beyond the historical practice of a biological research based on describing and cataloguing (e.g., Linnaean taxonomic classification or phylogenetic tree development), and aims to design biological systems to a given specification (e.g., production of a given amount of a medical drug or targeted invasion of a specific type of cancer cell). This transition into an industrialized synthetic biology is expected to affect most human activities, from improving human health, to producing renewable biofuels to combat climate change.(2) Some examples commercially available now include synthetic leather and spider silk, renewable biodiesel that propels the Rio de Janeiro public bus system, vegan burgers with meat taste, and sustainable skin-rejuvenating cosmetics.

This week, VentureBeat launched a quarterly magazine. Like the AI Weekly, the special issue gives our editorial team a chance to reflect on important transformative technology influencing business, technology, and society. The first issue focuses on the relationship between power and AI. Power can shape AI, from how we define ethical use of artificial intelligence and protect personal data to how AI may change how we define inventions to how AI may change how we define inventions or used as both a tool or weapon. By design, the special issue drew upon topics that dwell in our lives and shape our collective future.

A new proof-of-concept study details how an automated system driven by artificial intelligence can design, build, test and learn complex biochemical pathways to efficiently produce lycopene, a red pigment found in tomatoes and commonly used as a food coloring, opening the door to a wide range of biosynthetic applications, researchers report. The results of the study, which combined a fully automated robotic platform called the Illinois Biological Foundry for Advanced Biomanufacturing with AI to achieve biomanufacturing, are published in the journal Nature Communications. "Biofoundries are factories that mimic the foundries that build semiconductors, but are designed for biological systems instead of electrical systems," said Huimin Zhao (BSD leader/CABBI/MMG), a University of Illinois chemical and biomolecular engineering professor who led the research. However, because biology offers many pathways to chemical production, the researchers assert that a system driven by AI and capable of choosing from thousands of experimental iterations is required for true automation. Previous biofoundry efforts have produced a wide variety of products such as chemicals, fuels, and engineered cells and proteins, the researchers said, but those studies were not performed in a fully automated manner.

There's much robots can achieve by observing human demonstrations, like the actions necessary to move a box of crackers from a counter to storage. But imitation learning is by no means a perfect science -- demonstrators often complete subgoals that distract systems from overarching tasks. To solve this, researchers at the University of Washington, Stanford University, the University of Illinois Urbana-Champaign, the University of Toronto, and Nvidia propose an "inverse planning" system that taps motions or low-level trajectories to capture the intention of actions. After evaluating their technique by collecting and testing against a corpus of video demonstrations conditioned on a set of kitchen goals, the team reports that their motion reasoning approach improves task success by over 20%. The researchers lay out the full extent of the problem in a preprint paper detailing their work.

Now that the majority of households in the U.S. own, watch and use a smart television set or TV-connected device -- with artificial intelligence-powered ability to control how they watch or use these products with their voice -- what's the next big thing consumers will do with these products? The answer: Monitor and control other aspects of their homes, from adjusting room temperatures to knowing who is at their front door. That was the consensus viewpoint of a new interfaces panel at last Tuesday's first-ever "Next: The Connected Future […] Source: Smart TVs are "Prime Real Estate" for Powering Households

Canada is on the verge of a silver tsunami, and our health-care sector isn't ready. The rapid aging of our population through the 2020s is about to strain our hospitals, clinics and long-term care facilities, and technology will bridge only part of the gap. As much hope and hype as we see in robotic caregivers, virtual physicians and wearable sensors, we'll need more humans in health care, and more human skills than ever. The past decade has shown how reliant health care is on skilled labour; it's been one of the fastest-growing sectors for employment, and shows no signs of letting up. A new report from RBC estimates only 17 per cent of health-care jobs are at significant risk of automation, compared with 34 per cent in the overall economy.

Astronomers took centuries to figure it out. But now, a machine-learning algorithm inspired by the brain has worked out that it should place the Sun at the centre of the Solar System, based on how movements of the Sun and Mars appear from Earth. The feat is one the first tests of a technique that researchers hope they can use to discover new laws of physics, and perhaps to reformulate quantum mechanics, by finding patterns in large data sets. The results are due to appear in Physical Review Letters 1. Physicist Renato Renner at the Swiss Federal Institute of Technology (ETH) in Zurich and his collaborators wanted to design an algorithm that could distill large data sets down into a few basic formulae, mimicking the way that physicists come up with concise equations like E mc 2. To do this, the researchers had to design a new type of neural network, a machine-learning system inspired by the structure of the brain. Conventional neural networks learn to recognize objects -- such as images or sounds -- by training on huge data sets.

The following post is from Neha Goel, Champion of student competitions and online data science competitions. She's here to promote a new Deep Learning challenge available to everyone. If you win, you get money, plus a bonus if you use MATLAB. We at MathWorks, in collaboration with DrivenData, are excited to bring you this challenge. Through this challenge you'll be working with a real-world dataset of drone aerial imagery (big images) for classification.