The perception of what artificial intelligence was capable of began to change when chess grand master and world champion Garry Kasparov lost to Deep Blue, IBM's chess-playing program, in 1997. Significantly more complex, requiring even more strategic thinking, and featuring an intricate interweaving of tactical and strategical components, it posed an even greater challenge to artificial intelligence. With a number of possible moves per turn an order of magnitude greater than chess, any algorithm trying to evaluate all possible future moves was expected to fail. Led by Hiroaki Kitano and Manuela Veloso, the ambitious goal set that year was to have by 2050 a team of humanoid robots able to play a game of football against the world champion team according to FIFA rules, and win.

In my job as a data scientist, I spend my time driving machine learning research and making it work in the real world. NEW for 2016: Featuring a comprehensive market overview, the top questions to ask providers, and full one-page profiles of the top-28 vendors for your enterprise. The challenge with cleaning data for use in all types of machine learning is that each data set is different, and understanding a certain data set typically requires considerable "tribal" knowledge. He provides guidance for SAS and its customers on deriving substantive value from machine learning technologies and designs new data mining & machine learning approaches, focusing on neural networks and clustering.

MIT developed a deep learning algorithm capable of assessing physical interactions in videos and the sounds resulting from those interactions. The algorithm was then fed muted video clips of a drumstick hitting various objects and instructed to produce sound effects appropriate for each video. But the visual Turing Test is just one component of fully passing the Turing Test. MIT is currently developing an algorithm for passing the visual Turing Test, which is great for the machine learning community and is definitely a step towards having machines understand the consequences of interacting with different environments.

The General Data Protection Regulation (GDPR), approved at the end of April and scheduled to enter into force in 2018, does not only deal with EU citizens' privacy. That means that all EU citizens will be able to ask to know the rationale governing companies' algorithmic decisions. As an Oxford paper explains, this is theoretically a good thing: exposing the workings and criteria behind algorithmic behaviors can help to ensure that such criteria are fair and not, for instance, informed by discriminatory ideas. However, a little bit of hand-wringing on the techie's part is understandable: explaining why an AI algorithm acts the way it does is not only an industrial secret, it is also very hard, given the intrinsic complexity of machine-learning technology.

With consumers now shopping and managing their services online, retailers are racing to find ways to improve their customer service and shopping experiences while transforming how they communicate with their customers. Also, as of mid-April, Facebook opened Messenger platform to chatbot services. Rogers Communications is also wading into the bot game by using Facebook Messenger for customers to chat with service representatives to change account information, add a phone line or deal with other service information, and are on the cusp of implementing a bot. In many ways it makes sense for retailers to incorporate bots in areas they can be controlled, like automating everyday customer service tasks (imagine never sitting on hold again).

Key players in machine learning include big cloud players like Amazon (NASDAQ:AMZN), Microsoft (NASDAQ:MSFT), and IBM. A recent study by AI language company Narrative Science found that 80% of executives believed that AI solutions boosted worker performance and created new jobs. Nuance developed the technology that powers Apple's (NASDAQ:AAPL) Siri, which preceded other voice assistants like Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google Now, Microsoft's Cortana, and Amazon's Alexa. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon.com, The Motley Fool owns shares of Microsoft and has the following options: long January 2018 90 calls on Apple and short January 2018 95 calls on Apple.

We all know about the incredible progress that deep learning has made in recent years. The previous technological revolution, the Internet revolution, turned out pretty well. The world that the Internet created has turned out to be open --amazingly open, in fact, a world that empowers individuals like never before. Much like almost anyone today can create a web app, because the tools and technologies underlying the web are easy to use, open-source, completely free, and learning resources are available everywhere with an Internet connection --again, for free.

Automation and artificial intelligence (AI) is a growing necessity and benefit in the business world. As we enter the Fourth Industrial Revolution, the technological advances at the centre of this great business change will differ greatly from past industrial leaps forward. People experience constant changes and refinement in their understanding, and their ability to respond to situations. With advances and the growth of Big Data, organisations now have a rich feed of business, customer and decision-making data to feed into platforms powered by machine-learning algorithms.

Notably, the company acquired Mojang for 2.5 billion in September 2015 and then later purchased MinecraftEdu in January this year with an announcement that Minecraft Education Edition would be available in summer. Originally designed as an internal tool, the company announced that it would be made fully available under an open-source license. Now, with a new name, the company has publicly unveiled Project Malmo, which has been described as "a platform that uses the world of Minecraft as a testing ground for advanced artificial intelligence research." It is comprised of two components: a mod for the Java version of Minecraft and additional code that enables AI agents to become aware of and operate within the environment.

"Because we'll need something tasty to swill when our robot overlords finally come into their full artificial intelligence, a company in the UK is attempting to figure out if robots can help humans brew a better beer," reports The Consumerist website. "While there won't be robots stirring batches of wort or sorting hops, artificial intelligence will play a big part in London-based firm IntelligentX's plan to brew beer, CNET reports. And, you can found out that information by clicking here.