A Land Rover team of engineers is helping the Land Rover BAR team make its America's Cup entry faster. America's Cup is the pinnacle of sailing competitiveness. Teams spend upwards of 100 million just to sniff success and rumor has it Oracle Team USA owner Larry Ellison blew past that by nearly double when his team won it in 2013. However, in an attempt to bring the Cup to Great Britain for the first time ever, one team is looking not to outspend its opponents, but to outthink them. The Land Rover BAR team is using the brains from one of its sponsors – Land Rover – to build a faster boat … faster because it's smarter.

Learn how to use Machine Learning in your projects using actual production-ready python frameworks, namely Python Scikit-Learn (for most code) and TensorFlow (for neural nets). This book favors a practical approach using real-life production-ready tools, and it builds up instincts quickly using concrete examples and minimal theory (avoiding spending too much time on excessive theory and unnecessary details of every algorithm).

In the past couple of years, artificial intelligence, or AI, has come to the fore as an emerging technology that will disrupt the way business is done. This has happened before, several times, since the 1980s, with AI becoming the hot new thing, only to cool off when it's realized there is a lack of business cases. This time may or may not be different, but it's instructional to take a look at what its impact could be on insurance operations. In a new report, NTT DATA Consulting did just that, examining the potential impact on agents and underwriters in the new age of digital personal assistants and robots and bots. AI may not be a perfect fit for insurance companies – at least not yet.

Last Friday, at NYU's Skirball Center, the White House hosted a symposium on Artificial Intelligence, ethics, health, and machine learning. Led by Kate Crawford, a prinicipal researcher at Microsoft Research, and Meredith Whittaker, lead for Google Open Source Research Group. The day time events (invitation only) consisted of lightening talks from researchers at IBM Watson, Microsoft, policy makers, lawyers, artists and data visualizers such as Jer Thorp (blprnt). It was an incredibly diverse crowd, from careers to gender to race, and was something that the organizers had intended and carefully curated for the event itself. To create and germinate better discussions around AI, and to make better artificial intelligence, the group better be diverse, and AINow beyond succeeded with that.

According to a new release, "Zendesk, Inc. today announced the launch of Automatic Answers, a feature powered by machine learning within Zendesk that helps customers solve their inquiries faster and enables businesses to have more efficient support teams. Zendesk is one of the first customer service platforms implementing machine learning to natively auto-respond to customer tickets with relevant knowledge base articles, helping solve and deflect customer inquiries before they ever reach an agent. Automatic Answers was developed in Australia by Zendesk's Melbourne-based development team, who previously brought Satisfaction Prediction to market and was awarded the 2016 Victorian iAward for Big Data Innovation of the Year."

Most companies know by now that the key to making smart and strategic decisions is to look at both current and past data as a cornerstone for future business. Business intelligence teams and other analysts are brought on to enable more efficient decision making across every department. This can lead to visible changes for customers or viable improvements to process for employees. Advances in computer vision have opened up opportunities to apply data like never before. As artificial intelligence has become an increasingly popular topic of late and corresponding neural networks have improved, it's a great time to revisit how – and when – your company is applying its data.

Data scientists building new models in R or Python often want to use the latest runtime and package versions, which have the newest features and bug fixes. They might also have existing production models they have to maintain that rely on older versions. Now Azure Machine Learning provides support for multiple R and Python versions. Choose a newer version when building a new experiment, or update existing scripts to run under a newer version. Or keep using an old version if a legacy model depends on it.

Today, companies compete for analysts, engineers and data scientists who have hard technical skills and knowledge of distributed computing systems and analytical tools.3 But in the world of machine-reengineering, workers will also need other skills that could be as unconventional as the processes they support.4 These include the ability to program "belief spaces"--advanced probabilistic models that help robots deal with uncertainty--and to work well with intelligent machines.5 Mercedes-Benz, for example, has replaced large, inflexible factory robots with people and smaller, more flexible robots. Now Mercedes-Benz needs a workforce that can teach robots how to collaborate closely with employees on the factory floor.

The workshop will be led by two AI security experts with years in the field, and will demystify Artificial Intelligence before exploring its application in cyber defense. Dr. Kalyan Veeramachaneni is currently a research scientist at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and has been conducting research in the field for years. He will be joined by Dr. Ignacio Arnaldo, the Chief Data Scientist at PatternEx and former researcher at MIT CSAIL. Together they will facilitate an interactive, vendor agnostic discussion which will provide a foundation for understanding emerging artificial intelligence solutions in InfoSec. "During the time that we have been talking with security leaders from around the world we have found a strong interest in the potential of AI in InfoSec, but there is too much jargon and marketing speak out there," comments Travis Reed, CMO for PatternEx.

Big data may promise a world of new insight, but if it can't be analyzed, you can kiss that potential goodbye. Enter Teradata and Nuix, which on Tuesday teamed up to bring so-called "dark data" to light. Dark data is generally considered any data that gets overlooked and underused, often because employees don't know it's there or don't know how to access it. It's widely thought that dark data accounts for a majority of most companies' information assets. Through their partnership, the companies will integrate Nuix's namesake data processing and indexing engine with Teradata's Aster Analytics software, giving organizations a new way to uncover their dark data and analyze it on the spot.