BIO Meor Amer is the author of Nuts About Data. He is the founder of Edsquirrel, with a mission to develop data-driven professionals & youths via an engaging learning experience. He has previously worked with clients in over fifteen countries for deploying telecommunications data analytics solutions & running training & enablement programs. For his MSc, he worked on machine learning techniques in biomedical engineering. Check our Meor's book Nuts About Data here: https://edsquirrel.com/nad/

Research models use data from Toronto's Don River and Calgary's Bow River TORONTO, November 11, 2019 – Using complex models based on artificial intelligence (AI) and data from the Don River in Toronto and Bow River in Calgary, researchers at the Lassonde School of Engineering can now predict the water levels in rivers days in advance of floods. "We've created methods to predict real-time flood risk," says Usman T. Khan, professor in the Department of Civil Engineering at York's Lassonde School of Engineering. "These results outline an approach that can be used to create models with higher accuracy and lower data requirements, which translates to improved flood early warning systems. Early warning systems are considered the most effective way to mitigate flood induced hazards." The study, led by Khan, was published today in the Journal of Hydrology.

In this tutorial, you will learn how to automatically detect natural disasters (earthquakes, floods, wildfires, cyclones/hurricanes) with up to 95% accuracy using Keras, Computer Vision, and Deep Learning. I remember the first time I ever experienced a natural disaster -- I was just a kid in kindergarten, no more than 6-7 years old. We were outside for recess, playing on the jungle gym, running around like the wild animals that young children are. Rain was in the forecast. My mother had given me a coat to wear outside, but I was hot and unconformable -- the humidity made the cotton/polyester blend stick to my skin.

The use of an environment means that there is no fixed training dataset, rather a goal or set of goals that an agent is required to achieve, actions they may perform, and feedback about performance toward the goal. Some machine learning algorithms do not just experience a fixed dataset. For example, reinforcement learning algorithms interact with an environment, so there is a feedback loop between the learning system and its experiences.

In a world where inequality between men and women rules in many sectors of activity, the power of AI could help identify, address and possibly solve those inequalities. Only 22% of AI professionals globally and only 12% of the leading machine-learning researchers are female, according to recent international reports. Because algorithms learn from real-world data, AI can potentially adopt and reinforce existing social biases. Developers could unconsciously integrate gender biases into their AI systems and perpetrate them in recruiting tools, search engines, face recognition systems, medical diagnosis and loan approval tools. AI digital assistants, obedient and obliging machines that pretend to be women are entering our homes, cars and offices and provide a powerful illustration of gender biases coded into mass market products.

Despite the hype surrounding machine learning and artificial intelligence(AI) most efforts in the enterprise remain in a pilot stage. Part of the reason for this phenomenon is the natural experimentation associated with machine learning projects but also there is a significant component related to the lack of maturity of machine learning architectures. This problem is particularly visible in enterprise environments in which the new application lifecycle management practices of modern machine learning solutions conflicts with corporate practices and regulatory requirements. What are the key architecture building blocks that organizations should put in place when adopting machine learning solutions? The answer is not very trivial but recently we have seen some efforts from research labs and AI data science that are starting to lay down the path of what can become reference architectures for large scale machine learning solutions.

Ms. Payne, who does not have a background in education, developed the course of study with input from computer science teachers and researchers at the Harvard Graduate School of Education. Her "unplugged" curriculum mainly uses pen, paper and craft supplies so that teachers can adapt it for their classrooms, regardless of budget or technological know-how. Each 45-minute lesson typically includes a short lecture and demonstration, followed by a group activity and open-ended discussion. In one exercise, for example, students wrote an algorithm to build a better peanut butter and jelly sandwich. Ms. Payne plans to do a second run at several summer workshops in the Boston area, where children will get about four hours of AI education daily.

John Keefe is the investigations editor at Quartz and leads the Quartz AI Studio. Keefe also teaches classes on product prototyping, design, and development at the Craig Newmark Graduate School of Journalism at CUNY and runs a product tinkering company called Really Good Smarts LLC. Before joining Quartz, he was Senior Editor for Data News at public radio station WNYC, leading a team of journalists who specialize in data reporting, coding, and design for visualizations and investigations. He was previously WNYC's news director for nearly a decade. A self-described "professional beginner," Keefe is the author of Family Projects for Smart Objects: Tabletop Projects That Respond to Your World from Maker Media, which grew from his effort to make something new every week for a year.

It is undoubtedly a given that Artificial Intelligence (AI) is the next evolutionary step and the future of businesses. However, many do not realize that the future is already here or much closer than foreseen. AI incorporates machine learning as well as decision making abilities that were once exclusive for human minds only. However, now technology has advanced to the point that even computers can do and think the unthinkable with the help of a group of algorithms that can automate repetitive tasks and produce usable output data. A company has many projects from software to logistics to finances and every one of them require planning, managing as well as monitoring.

Minecraft has sold 176 million copies across all platforms, making it the best-selling video game of all time. I first learned of it in 2013, when its popularity was peaking, just before it was acquired by Microsoft when it bought the indie studio Mojang for $2.5 billion. While surviving against monsters, starvation, other players, and environmental conditions can be part of the game, the main object is to build things. Players extract resources from the environment and combine them to make construction materials, armor, weapons, and more. All the nerdiest boys in my seventh-grade class were obsessed with it; they all wore T-shirts and backpacks with the game's pixelated characters, and I would overhear them making plans to meet up in the game after class.