Machine learning and algorithms have become so intelligent and complex that they are shaped by forces beyond our control. We use these tools to help us in our everyday lives. They help us make business decisions, they generate accurate search results, they show us articles on Facebook that we actually find interesting, they match us with prospective employers, and even match us with prospective partners. In many ways, they improve our businesses, our health, our education, and our lives. But can these programs and software be discriminatory? Although there is a widespread belief that these computer programs and algorithms are objective, there is no doubt that there is a significant degree of human influence involved.

I'm Julie, Director of Ops, back for a quick tutorial of two awesome new offerings we've built for you. I'm on the non-technical / cat loving side of things so I break it down a bit. I also run the Machine Learning Without a PhD group on LinkedIn where jargon isn't allowed. Feel free to join if you'd like #machinelearning4everyone So today you're getting a 2-for-1 (or as my dad likes to call it a "TooFer"). Today's topic will be the 2016 Tony Awards that aired this past Sunday night. Let's begin by heading to your dashboard.

You can almost hear the whooshing sound as the technology industry is sprinting to the marketplace with new solutions to help the enterprise garner useful and intelligent insights from their data. Analytics, Machine Learning (ML) and Artificial Intelligence (AI) delivered in a simplistic form is what companies are demanding. To meet this mandate, Hewlett Packard Enterprise Co. (HPE) has developed Haven OnDemand, which offers the latest technology in a simplified platform for developers. Jeff Veis, VP of Big Data Platform Solutions, HP Software, at HPE, spoke to John Furrier and Dave Vellante, cohosts of theCUBE, from the SiliconANGLE Media team, during HPE Discover 2016 Las Vegas to discuss Big Data and the needs of the enterprise. Furrier began the interview by asking Veis about the driving force behind the need for machine learning.

Could someone give me example code which uses Tensorflow to mimic a single layer neural network for regression and gives an r2 value of the result? An example of the type of data I'm working with is the airfoil self noise data set, I would import training and test sets.

While it already feels that AlphaGo is ancient history in the fast moving AI world, I think it makes a powerful case for how human-machine collaboration could help us improve our own mastery of complex tasks. Google DeepMind also made the headlines for the data sharing agreement they signed with three hospitals that are part of UK's National Health Service. The data on 1.6 million patients includes live and historical medical records stretching back 5 years. Its stated use is for "real time clinical analytics, detection, diagnosis and decision support", with an initial focus on the Company's Streams app for measuring the risk of acute kidney injury. While many engaged in heated debate over data privacy (see this headline, courtesy of The Daily Mail), my view is that medicine should be moving towards real-time monitoring of health and prediction of future conditions.

Ryan Jones is a well-known SEO and Manager of Search Strategy & Analytics at SapientNitro. Ryan Jones is a veteran of SEJ Summit Chicago, having spoken there last year. This year, I'm so excited for Ryan's presentation on machine learning and how it affects search, which is a topic I'm sure most of you want to learn more about. Check out Ryan's insight below and feel free to ask questions in the comment section! I hate to spoil the presentation here, but the short answer is: if you've been doing SEO properly RankBrain doesn't change anything.

Pretty much every cinematic portrayal of artificial intelligence has been less than encouraging. HAL 9000 kills the crew members on the Discovery in 2001: A Space Odyssey, making us all a little bit afraid of handing the reins over to computers. Sonny kills his creator in I, Robot, increasing worldwide scepticism about the integration of humans and their smart robots. Even real life AI has given us pause. For example, when an IBM computer defeated Russian chess Grandmaster Garry Kasparov in the 1990s, it was definitely a cause for concern. For the most part, though, AI has been more accepted in everyday practice.

Analysts are saying that Apple's upcoming updates to its Siri voice assistant should help the company address criticisms that it can't compete on artificial intelligence. Apple is now opening Siri to apps made by other companies, and like Google and Microsoft, it's bringing the digital assistant to desktop and laptop computers. It's also making Siri smarter by using what Apple calls differential privacy. Patrick Moorhead of Moor Insights & Strategy explains it as Apple using non-personal information in aggregate to teach Siri new tricks, then having all the personalization take place on the individual device. It's in contrast to Google's approach of doing everything over the internet -- that is, in the "cloud."

Aerial drones get all the attention, but a new terrestrial drone named the Pegasus:Multiscope is an autonomous treaded vehicle that its makers call "the first unmanned ground vehicle (UGV) for off-road use." Use cases for the Pegasus:Multiscope include surveying challenging terrain for civil engineering projects or agriculture, or in hazardous areas such as near nuclear power stations or in conflict zones. The UGV's treads reduce ground pressure at any one point, allowing the vehicle, which weighs just under 2000 pounds, to traverse any type of terrain, including mud, sand or snow. Contractor Oshkosh Defense designs solutions to turn existing military vehicles into UGV.

Neural networks are already beating us at games, organizing our smartphone photos, and answering our emails. Eventually, they could be filling jobs in Hollywood. Over at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), a team of six researchers created a machine-learning system that matches sound effects to video clips. Before you get too excited, the CSAIL algorithm can't do its audio work on any old video, and the sound effects it produces are limited. For the project, CSAIL PhD student Andrew Owens and postgrad Phillip Isola recorded videos of themselves whacking a bunch of things with drumsticks: stumps, tables, chairs, puddles, banisters, dead leaves, the dirty ground.