If you're a programmer and you've been looking to get started with machine learning but aren't sure where to begin, these 13 resources are for you. Its one of those buzzwords that we've all heard whether we're programmers or not: machine learning. Unlike other trends in the past, machine learning isn't a fad, it really is the future. As AIs become more and more sophisticated, programmers need to get up to speed on what it is, how it works, and the latest trends in the field. Fortunately, these 13 free resources offer an excellent introduction to machine learning so you can get started with some basic machine learning tutorials right away.

Six titans of industry stood onstage at MIT's Kresge Auditorium yesterday, assembled to speak on a panel about artificial intelligence (AI), including David H. Koch Institute professor Robert Langer; Helen Greiner, cofounder of iRobot, the Bedford-based company perhaps best known for its line of autonomous vacuum cleaners; Xiao'ou Tang, founder of computer vision startup SenseTime, which last year raised $1.2 billion in venture capital at a valuation of more than $4.5 billion; and Eric Schmidt, former executive chairman of Google. The discussion capped off a three-day celebration of MIT's new Stephen A. Schwarzman College of Computing, which will offer its first classes in physics, economics, biology, economics, machine learning, and related disciplines this fall. The panelists shared thoughts on a range of topics, but one they repeatedly touched on was entrepreneurship. Entrepreneurs, Schmidt argued in his opening remarks, drive the economy -- they're spigots for ideas that form the basis of industries. "[Founders are] people who are filled with a vision -- something they care about -- and they personalize it, they believe in it, and they convince others to follow them," he said. But, he said, they're in "need [of] more juice."

Sure, technologies like machine learning, natural language processing, and automation could help reshape how your association works and serves its members. But finding the right opportunities, and staffing with those opportunities in mind, is the hard part, especially if there's resistance within your team. Overcoming these challenges could open up a lot of opportunities to take advantage of artificial intelligence down the line. Here are a few considerations for getting your AI ducks in a row. You don't necessarily need AI experts on your staff, said Amith Nagarajan, chairman of the AI-driven newsletter platform Rasa.io. What does matter is whether those staff members have a "growth mindset," a degree of curiosity, and focus on continued learning that isn't defined "by their list of prior job functions and tasks they've completed."

In the Hangar at the OC Fair & Event Center in Costa Mesa, high school students watched as robots rushed to fill spaceships with cargo before the next sandstorm arrived. The Orange County regional of the FIRST (For Inspiration and Recognition of Science and Technology) Robotics Competition drew more than 40 teams and their robots. Teams from Marina, Huntington Beach, Ocean View, Edison and Corona del Mar high schools were in attendance Friday. This year's theme is "Destination: Deep Space," inspired by the lunar landing in July 1969. The challenge is to have the robots affix plastic "hatch panels" to the sides of the rockets and cargo ships and fill each with orange balls (the cargo).

Edgar Perez is a great business speaker, a confident communicator and a world class motivator. Global executives have come to appreciate his wide-ranging insights on how they can better position their organizations for success through strong leadership and a comprehensive approach that links business strategy and disruptive technologies including artificial intelligence and deep learning, quantum computing and cyber security. A published author, keynote speaker and business consultant for private equity and hedge funds, he is Council Member at the Gerson Lehrman Group, Guidepoint Global Advisors and Internal Consulting Group. Mr. Perez is author of The AI Breakthrough, How Artificial Intelligence is Advancing Deep Learning and Revolutionizing Your World (2018), Knightmare on Wall Street, The Rise and Fall of Knight Capital and the Biggest Risk for Financial Markets (2013), and The Speed Traders, An Insider's Look at the New High-Frequency Trading Phenomenon That is Transforming the Investing World, published in English by McGraw-Hill Inc. (2011), 交易快手, published in Mandarin by China Financial Publishing House (2012), and Investasi Super Kilat, published in Bahasa Indonesia by Kompas Gramedia (2012). Mr. Perez has addressed thousands of top executives around the world through keynote speeches and corporate training programs on quantum computing, artificial intelligence, deep learning, cybersecurity and financial trading.

Building a good predictive model requires an array of activities such as data imputation, feature transformations, estimator selection, hyper-parameter search and ensemble construction. Given the large, complex and heterogenous space of options, off-the-shelf optimization methods are infeasible for realistic response times. In practice, much of the predictive modeling process is conducted by experienced data scientists, who selectively make use of available tools. Over time, they develop an understanding of the behavior of operators, and perform serial decision making under uncertainty, colloquially referred to as educated guesswork. With an unprecedented demand for application of supervised machine learning, there is a call for solutions that automatically search for a good combination of parameters across these tasks to minimize the modeling error. We introduce a novel system called APRL (Autonomous Predictive modeler via Reinforcement Learning), that uses past experience through reinforcement learning to optimize such sequential decision making from within a set of diverse actions under a time constraint on a previously unseen predictive learning problem. APRL actions are taken to optimize the performance of a final ensemble. This is in contrast to other systems, which maximize individual model accuracy first and create ensembles as a disconnected post-processing step. As a result, APRL is able to reduce up to 71\% of classification error on average over a wide variety of problems.

Catastrophic forgetting/interference is a critical problem for lifelong learning machines, which impedes the agents from maintaining their previously learned knowledge while learning new tasks. Neural networks, in particular, suffer plenty from the catastrophic forgetting phenomenon. Recently there has been several efforts towards overcoming catastrophic forgetting in neural networks. Here, we propose a biologically inspired method toward overcoming catastrophic forgetting. Specifically, we define an attention-based selective plasticity of synapses based on the cholinergic neuromodulatory system in the brain. We define synaptic importance parameters in addition to synaptic weights and then use Hebbian learning in parallel with backpropagation algorithm to learn synaptic importances in an online and seamless manner. We test our proposed method on benchmark tasks including the Permuted MNIST and the Split MNIST problems and show competitive performance compared to the state-of-the-art methods.

The announcement follows an executive order that President Donald Trump signed in February to increase AI research and development. The order calls for more training in the form of apprenticeships, skills programs and STEM education, with a focus on computer science. Georgetown joins at least two other U.S. colleges with big plans in AI. In October, MIT revealed its new, $1 billion College of Computing, which will integrate AI, computer science and data science throughout the institution. And in February, the State University of New York Polytechnic Institute, in Albany, announced a $2 billion investment from IBM to create an AI research center on the campus.

An new RAND report has concluded that using artificial intelligence in education shows promise, but only when it comes to supporting teachers with repetitive and predictable tasks. According to author Robert Murphy, senior policy researcher for RAND Education, "the work of teachers and the act of teaching" can't be "completely automated like repetitive tasks taking place on the manufacturing floor. After all, he pointed out, "Good teaching is complex and requires creativity, flexibility, improvisation and spontaneity." Teachers need the abilities to "think logically and apply common sense, compassion and empathy to deal with the everyday nonacademic issues and problems that arise in the classroom." These are abilities that even the most advanced AI systems lack.

Looking back to 18 years ago, many would have been hard-pressed then to believe the capabilities of the technology available today. "It's really about perspective," said Eric Patnoudes, the director of strategic initiatives at Otus and a featured speaker at the 2019 Illinois Computing Educators conference. "Think of it this way," Patnoudes said, holding his cellphone up. "This cellphone is the worst technology your kids will use in their lives." In a session titled "Are We Preparing Students for Their Future or Our Past?" Patnoudes asked attendees to imagine where technology will be when today's kindergarteners graduate from college.