A trio of teens was charged with a violent plot to "kill everyone and anyone" at their Michigan middle school, according to court records. Lapeer County Assistant Prosecutor David Campbell read chilling words allegedly written by Gunnar Rice in Lapper County District Court on Monday, detailed what he allegedly planned to undertake at Zemmer Middle School in Lapeer, a city of roughly 8,000 about 20 miles east of Flint. Rice, 14, wrote that he wanted to "exterminate all the [expletive] animals at this school," Campbell said during Monday's arraignment, MLive.com "We'll kill everyone and anyone of our choosing." Rice was charged as an adult on charges of conspiracy to commit first-degree murder, using computers to commit a crime, conspiracy to commit terrorism and a false report of terrorism.

Artificial Intelligence is already impacting every industry through automation and machine learning, bringing concerns that AI is on the fast track to replacing many jobs. But these fears aren't new, says Dan Jackson, director of Enterprise Technology at Crestron, a company that designs workplace technology. "I'd argue this is no different than when we moved from an agricultural to an industrial economy at the turn of the last century. The percentage of people working in agriculture significantly decreased, and it was a big shift, but we still have plenty of jobs 100 years later," he says. Anytime society experiences a major technological advancement, we need to be prepared for it to change the way we live and work.

Los Angeles start-up Coin-In develops mobile games, pictured above, aimed at driving gamblers back into casinos. It's among companies recently backed by Tech Coast Angeles members. Los Angeles start-up Coin-In develops mobile games, pictured above, aimed at driving gamblers back into casinos. It's among companies recently backed by Tech Coast Angeles members. He says concern about missing the boat on Los Angeles' next big thing is the most noticeable local trend during the first quarter.

It's been 15 years since a degenerative eye disease forced Erich Manser to stop driving. Today, he commutes to his job as an accessibility consultant via commuter trains and city buses, but he has trouble locating empty seats sometimes and must ask strangers for guidance. A step toward solving Manser's predicament could arrive as soon as next year. Manser's employer, IBM, and an independent carmaker called Local Motors are developing a self-driving, electric shuttle bus that combines artificial intelligence, augmented reality, and smartphone apps to serve people with vision, hearing, physical, and cognitive disabilities. The buses, dubbed "Olli," are designed to transport people around neighborhoods at speeds below 35 miles per hour and will be sold to cities, counties, airports, companies, and universities.

CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data. In this paper, we introduce an industrial strength solution with model named Large Scale Piece-wise Linear Model (LS-PLM). We formulate the learning problem with $L_1$ and $L_{2,1}$ regularizers, leading to a non-convex and non-smooth optimization problem. Then, we propose a novel algorithm to solve it efficiently, based on directional derivatives and quasi-Newton method. In addition, we design a distributed system which can run on hundreds of machines parallel and provides us with the industrial scalability. LS-PLM model can capture nonlinear patterns from massive sparse data, saving us from heavy feature engineering jobs. Since 2012, LS-PLM has become the main CTR prediction model in Alibaba's online display advertising system, serving hundreds of millions users every day.

Machine Learning, Edge Computing and Serverless are the three key technologies that will redefine the Cloud Computing platforms. Machine Learning (ML) is becoming an integral part of modern applications. From the web to mobile to IoT, ML is powering the new breed of applications through natural user experiences and inbuilt intelligence. After virtualization and containerization, Serverless is emerging as the next wave of compute services. Serverless or Functions as a Service (FaaS) attempts to simplify the developer experience by minimizing the operational overhead in deploying and managing code.

In our world, many of the commonly encountered problems are classification problems. We are often confused between definite values or rigid choices of things. In this article, we will discuss about an algorithm used to solve simple classification problems effectively using Machine Learning. Also, we will analyze a hypothetical Binary Class problem involving Grad-School outcomes based on the Entrance Exam Marks and the Undergrad Marks. Supervised Learning is a machine learning technique in which we associate our inputs with our targets in the given dataset. We already have a definite intuition regarding our final output.

Let's take a breath: Robots and artificial intelligence systems are nowhere near displacing the human workforce. Nevertheless, no less a voice than Bill Gates has asserted just the opposite and called for a counterintuitive, preemptive strike on these innovations. His proposed weapon of choice? Taxes on technology to compensate for losses that haven't happened. David Kenny (@davidwkenny) is IBM's senior vice president for Watson and the company's cloud platform.

This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python. One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don't know enough about the Numpy stack in order to turn those concepts into code. Even if I write the code in full, if you don't know Numpy, then it's still very hard to read. This course is designed to remove that obstacle - to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science. This forms the basis for everything else.

Data science and machine learning have long been interests of mine, but now that I'm working on Fuzzy.ai and trying to make AI and machine learning accessible to all developers, I need to keep on top of all the news in both fields. My preferred way to do this is through listening to podcasts. I've listened to a bunch of machine learning and data science podcasts in the last few months, so I thought I'd share my favorites: Every other week, they release a 10–15 minute episode where hosts, Kyle and Linda Polich give a short primer on topics like k-means clustering, natural language processing and decision tree learning, often using analogies related to their pet parrot, Yoshi. This is the only place where you'll learn about k-means clustering via placement of parrot droppings. Hosted by Katie Malone and Ben Jaffe of online education startup Udacity, this weekly podcast covers diverse topics in data science and machine learning: teaching specific concepts like Hidden Markov Models and how they apply to real-world problems and datasets.