Advances in machine learning and deep learning systems are bring us much closer to developing true artificial intelligence (AI) than ever before. One major limitation to these systems, though, is the effort required to teach them, with most requiring thousands or even hundreds of thousands of examples before they can "learn" something new. Self-driving car systems absorb miles of traffic data to learn basic driving lessons, and this scary image generator had to be fed 200,000 images for it to recognize a normal face. However, a new development from the team at Google DeepMind may be the start of leveling out that steep learning curve for AI systems. To speed up the learning process, Google DeepMind researcher Oriol Vinyals added a memory component to a deep-learning system.

Ben Dickson is a software engineer and the founder of TechTalks. As artificial intelligence and machine learning mature and manifest their potential to take on complicated tasks, we've become somewhat expectant that robots can succeed where humans have failed -- namely, in putting aside personal biases when making decisions. But as recent cases have shown, like all disruptive technologies, machine learning introduces its own set of unexpected challenges and sometimes yields results that are wrong, unsavory, offensive and not aligned with the moral and ethical standards of human society. While some of these stories might sound amusing, they do lead us to ponder the implications of a future where robots and artificial intelligence take on more critical responsibilities and will have to be held responsible for the possibly wrong decisions they make. At its core, machine learning uses algorithms to parse data, extract patterns, learn and make predictions and decisions based on the gleaned insights.

Facebook has been forced to end a hugely controversial data sharing agreement with WhatsApp. The decision would have seen WhatsApp hand out information on all of its users to Facebook, letting the latter use data about people's chats to inform its advertising. It would also have gone the other way – allowing companies to send WhatsApp's to people based on things they've bought on Facebook, for instance. But now the UK's Information Commissioner's Office has told the company that it needs to bring that arrangement to an end because it does not have "valid consent" from its users. Facebook had looked to gain permission from its users to have their data used as part of the deal.

Stanford CoreNLP provides a set of natural language analysis tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract open-class relations between mentions, etc. Stanford CoreNLP is an integrated framework. Its goal is to make it very easy to apply a bunch of linguistic analysis tools to a piece of text. A CoreNLP tool pipeline can be run on a piece of plain text with just two lines of code. It is designed to be highly flexible and extensible.

You're working in the garden when you notice your tomato plant is stunted and wrinkled. What is your next step? A team of researchers from Penn State University and the Swiss Federal Institute of Technology in Lausanne (EPFL) believes you should reach for your phone. They are building a free app called PlantVillage that can recognize plant disease from a mobile phone photo. Behind the app, expected to be available in early 2017, is a database of 150,000 photographs of diseased plants--a number the team intends to grow to three million.

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Want to take your programming skills to the next level? You've come to the right place! Machine Learning can sound daunting, but I'm here to show you how it can be a very fun and rewarding journey! This course streamlines your learning of the material and how you implement it in future projects. Machine learning brings together computer science and statistics to harness predictive power. It's a great skill to have and brings a whole new perspective to problem solving.

We are just starting to discover the countless ways we can apply cognitive computing to healthcare,

Chat applications are becoming a mainstream trend and our preferred way of interacting with colleagues, friends and family. From the early days of SMS to the favorite snaps of our children, real-time online conversations are everywhere and here to stay. The acquisition of WhatAapp by Facebook in 2014 for a hefty $22 Billion price tag made it clear and promising as TechCrunch noticed it one year later. But although TechCrunch saw messaging apps as the future of mobile portal, they remained more or less next to the Internet, without a direct impact, except their increasing audience. The recent surge of interest in Bots and AI is changing the game and we'll be witnessing the second major fragmentation of the Internet.

A Barbie doll that uses artificial intelligence to communicate interactively. Oren Etzioni, a well-known AI researcher, complains about news coverage of potential long-term risks arising from future success in AI research (see "No, Experts Don't Think Superintelligent AI is a Threat to Humanity"). After pointing the finger squarely at Oxford philosopher Nick Bostrom and his recent book, Superintelligence, Etzioni complains that Bostrom's "main source of data on the advent of human-level intelligence" consists of surveys on the opinions of AI researchers. He then surveys the opinions of AI researchers, arguing that his results refute Bostrom's. It's important to understand that Etzioni is not even addressing the reason Superintelligence has had the impact he decries: its clear explanation of why superintelligent AI may have arbitrarily negative consequences and why it's important to begin addressing the issue well in advance. Bostrom does not base his case on predictions that superhuman AI systems are imminent.