The industry ministry has launched an initiative to support joint artificial intelligence projects between startups and large firms. Twenty projects have already been selected for financial assistance of up to ¥200 million. The ministry hopes that the support will lead to the creation of innovative business models that can be used worldwide. It chose the 20 projects from about 70 applications. The projects include collaboration between data analysis startup Grid Inc. and major plant engineering firm Chiyoda Corp. Grid and Chiyoda plan to jointly develop a system to enhance the operating efficiency of liquefied natural gas plants using deep learning, a technology in which AI obtains a vast amount of data and improves its performance on its own.

Integrating people into machine processes will have a significant influence in how ML is employed in business. Machine learning (ML) is gaining an increasing share of the public imagination, but its limitations are also becoming apparent. ML solutions can provide important new capabilities across a wide operational space, but we are still nowhere near creating an artificial general intelligence. Current ML solutions are sophisticated and may be combined to create broader applications, but they lack the real-world knowledge and human experience needed to create valid and acceptable outcomes on their own. An increasing part of the ML solution is human-in-the-loop capabilities where the machine matches a pattern but human input determines its validity and helps to refine the result.

Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. It is designed to make valuable machine learning skills more accessible to individuals with a strong math background, including software developers, experimental scientists, engineers and financial professionals. The 30 lectures in the course are embedded below, but may also be viewed in this YouTube playlist.

AI is the newest rock star on the technology scene. But just because artificial intelligence (AI) is the hottest new thing, that doesn't mean it can survive the inherent data challenges that come with it. These challenges include data accessibility, selection, timeliness and trust. So just as "video killed the radio star," data is threatening to kill the AI star. According to a recent Infosys survey, 49 percent of organizations reported that they will not be able to deploy AI because of data challenges.

In the future, you could fly a plane piloted by robots. The US military invented a robot arm that can fly commercial airplanes using AI. The robot pilot was developed by engineers at DARPA, the Pentagon's research unit. Last year, the robot flew and landed a simulated 737 and has successfully flown an array of small planes. The combination of machine learning with the robotic arm means the robot can act much more like a human pilot than existing autopilot software.

AIMagnifi is an Artificial Intelligence and Computer Vision based company based in New Delhi. AIMagnifi aims to target the unexplored combination of retail sector with the power of Artificial Intelligence. AIMagnifi was founder in 2017 by Neeraj Kumar and Devarshi Mishra with an essence of expertise in the areas of Artificial Intelligence, Computer Vision, Facial Recognition, Machine Learning, Deep Learning and Data Analytics. Ambhoj Shukla from IncubateIND recently caught with Neeraj and spoke to him about the future of shopping and artificial intelligence.

Strategically placed recycled cell phones are combating deforestation by sending notifications to rangers when chainsaw noises are recorded. Algorithms similar to those used by Homeland Security are being developed to provide effective routes for ranger patrols in their battle against poaching. And drones are delivering sylvatic plague vaccines to prairie dog populations in an effort to save the Black Footed Ferret, a highly endangered predator of prairie dogs, from extinction. What happens when wildlife biologists join forces with computer scientists? A new era in wildlife conservation is born!

AI can help scientists spot tiny folding protein crystals, and thus one day potentially assist eggheads in designing new drugs, according to a paper published in PLOS One. To demonstrate this form of boffinry is possible, a large team of researchers from academia and industry, including bods at Duke University in the US and Google and British pharmaceutical giant GlaxoSmithKline, built a convolutional neural network to recognize microscopic protein crystals. The project was launched by the Machine Recognition of Crystallization Outcomes (MARCO) initiative, an international effort to collect pictures of protein crystals from X-ray crystallography experiments. Over time, 493,214 images of protein crystals were harvested, and the dataset was shared with Google researchers to train and test the neural network. After testing it on 50,284 images, the system was found to be 94 per cent accurate in detecting the presence of protein crystals in a solution.

Discovering and getting started with Machine Learning can be daunting. Perhaps you have a vague project idea and are looking for a place to start and adapt from. Or you're looking for inspiration and want to get a sense of what's possible. Today we're launching Seedbank, a place to discover interactive machine learning examples which you can run from your browser, no set-up required. Each example is a little seed to inspire you that you can edit, extend, and grow into your own projects and ideas, from data analysis problems to art projects.

Competitors at this year's World Human Powered Speed Challenge are going to have to contend with this--a bullet-shaped bike designed by an artificially intelligent software program. In 2012, a bicycle screamed across a flat, open road of the Nevada Desert at an astounding 88.13 miles per hour, or 133.78 km/hr. This record, established by a Dutch team at the annual World Human Powered Speed Challenge, could now be in danger, owing to a new bike designed by researchers at IUT Annecy, with the help of computer scientists at Neural Concept, a Subsidiary of the Swiss Federal Institute of Technology in Lausanne (EPFL). To be fair, the IUT Annecy researchers can't take full credit for the bike's sleek, aerodynamic shape, nor can any human for that matter. You see, this machine was, in part, designed by another machine--an artificially intelligent program developed by researchers at Neural Concept, who are presenting their findings today in Stockholm, Sweden, at the International Conference on Machine Learning.