My interest in ml stems back to 2014 when I started reading articles about it on Hacker News. I simply found the idea of teaching machines stuff by looking at data appealing. At the time I wasn't even a professional developer, but a hobby coder who'd done a couple of small projects. So I began watching the first few chapters of Udacity's Supervised Learning course, while also reading all articles I came across on the subject. This gave me a little bit of conceptual understanding, though no practical skills.

This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and entirely new tasks emerge. Experimental results show that variational continual learning outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.

We introduce a novel repeated Inverse Reinforcement Learning problem: the agent has to act on behalf of a human in a sequence of tasks and wishes to minimize the number of tasks that it surprises the human by acting suboptimally with respect to how the human would have acted. Each time the human is surprised, the agent is provided a demonstration of the desired behavior by the human. We formalize this problem, including how the sequence of tasks is chosen, in a few different ways and provide some foundational results.

At the start of any machine learning project, you face an important choice: Which language or software should I use? Well, you have many options to choose from. Python, R, SAS, MATLAB… the list goes on. But first, you'll actually need to make another choice: Should I go with open source or commercial software? Open source code is "freely available and may be redistributed and modified."

A report by Human Rights Watch and the Harvard Law School International Human Rights Clinic calls for humans to remain in control over all weapons systems at a time of rapid technological advances. It says that requiring humans to remain in control of critical functions during combat, including the selection of targets, saves lives and ensures that fighters comply with international law. 'Machines have long served as instruments of war, but historically humans have directed how they are used,' said Bonnie Docherty, senior arms division researcher at Human Rights Watch, in a statement. 'Now there is a real threat that humans would relinquish their control and delegate life-and-death decisions to machines.' Some have argued in favor of robots on the battlefield, saying their use could save lives.

In this Introduction to IoT Programming with JavaScript training course, expert author Patrick Catanzariti will teach you how to create interactions with connected devices and dashboards. This course is designed for users that already have experience with web development, JavaScript, and Node. You will start by learning how to build your first dashboard, including setting up a modular Node server and getting your server onto the web. From there, Patrick will show you how to set up an Arduino, display Arduino data, and go wireless with Arduino Yun and node-serialport. This video tutorial also covers Spark, Tessel, pairing Android and JavaScript using on{X}, and voice recognition with Wit.

Like it or not, it appears that the continuing skills gap that continues to plague many sections of the software world, including development, testing and more, has found a new victim: digital transformation through the use of machine learning. A survey conducted by ServiceNow looked at the eagerness of organizations to incorporate machine learning as part of their digital transformation. Mainly, senior executives want to buy into machine learning in order to support faster and more accurate decision making. But the survey polled some interesting numbers that point to what appears to be a significant lack of machine learning skills needed to manage intelligent machines within organizations. The report shows that 72% of CIOs surveyed said they are leading their company's digitalization efforts, and just over half agree that machine learning plays a critical role in that. Nearly half (49%) say their companies are using machine learning and 40% said that they plan to adopt.

Editor's Note: The Falling Walls Conference is an annual, global gathering of forward thinking individuals from 80 countries organized by the Falling Walls Foundation. Each year, on November 9--the anniversary of the fall of the Berlin Wall--20 of the world's leading scientists are invited to Berlin to present their current breakthrough research. The aim of the conference is to address two questions: Which will be the next walls to fall? And how will that change our lives? The author of the following essay is speaking at this year's Falling Walls gathering.

This course is all about A/B testing. A/B testing is used everywhere. A/B testing is all about comparing things. If you're a data scientist, and you want to tell the rest of the company, "logo A is better than logo B", well you can't just say that without proving it using numbers and statistics. Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions. In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.

Sooner or later, the US will face mounting job losses due to advances in automation, artificial intelligence, and robotics. Automation has emerged as a bigger threat to American jobs than globalization or immigration combined. A 2015 report from Ball State University attributed 87 percent of recent manufacturing job losses to automation. Soon enough, the number of truck and taxi drivers, postal workers, and warehouse clerks will shrink. What will the 60 percent of the population that lacks a college degree do?