As artificial intelligence gains popularity, designers will need to adapt. Here's how to get started. It seems like everyone wants to invest in artificial intelligence (AI). And it's not just the tech giants: USAA is using AI to protect its users from identity theft and Under Armour has connected its health app, MyFitnessPal, to IBM Watson so users can get a more thorough read of their health. AI is already a $15 billion dollar industry, according to the MIT Technology Review, with more than 2,600 companies developing their own tech, and the value of AI is reported to rise to over $70 billion by 2020. Because of AI's business opportunities, hundreds of designers in digital agencies, people who were taught to create products and services that live on the Internet, are starting to build physical products that interact with us, respond to our moods, and make decisions for us.

AN official of a leading Japanese technological company recently said people must prepare for the bearing and consequences of artificial intelligence (AI). Fujitsu Ltd. Vice President Yoshikuni Takashige cited the warning in 2014 by British eminent scientist Stephen Hawking against "thinking machines". He cited for one the employment scenario in First World countries, such as the US, would drastically change as people will be replaced by machines in some areas of the work place. Takashige's warning came before the International Data Corp. (IDC) said the widespread adoption of cognitive systems and AI across a broad range of industries will drive worldwide revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020. IDC said in a statement on October 26 that the market for cognitive/AI solutions will experience a compound annual growth rate (CAGR) of 55.1 percent over the 2016-to-2020 forecast period.

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.

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.

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.

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.

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