Does every painter leave a fingerprint? Accurately distinguishing the artwork of a master from a forgery can mean a difference in millions of dollars. In the Painter by Numbers playground competition hosted by Kiri Nichol (AKA small yellow duck), Kagglers were challenged to identify whether pairs of paintings were created by the same artist. In this winner's interview, Nejc Ilenič takes us through his first place solution to this painter recognition challenge. His combination of unsupervised and supervised learning methods helped him achieve a final AUC of 0.9289.

Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.

Most media companies increasingly rely on data to inform their decision making processes on both strategic and tactical levels. Yet with the widespread adoption of the Internet of Things (IoT) and Artificial Intelligence the amount of data companies can potentially harvest is set to rocket. So what data should they be focusing on, and how should they use it to make judgements about the content they produce? Steffen Konrath is the CEO of Liquid Newsroom, a company that uses technology to power its data-driven approach to content marketing, which it claims helps companies grow their B2B prospect and client list. Here Steffen, who will be speaking at DIS 2017 on'why listening is so important to creating content strategies,' offers insight into how data will shape the future of publishing. He explains why he thinks that the value of data is higher than the value of content, why technology will change the way publishers approach real time events and what role journalists will have in media offices powered by Artificial Intelligence. Digital Innovators' Summit 2017 takes place from 19-21 March (main Summit on 20 and 21 March) in Berlin, Germany.

The major roadblock is applying the right set of tools, which can pull powerful insights from this stockpile of data. But first, a big data system requires identifying and storing of digital information (lots of!!). Using Machine learning and Artificial Intelligence algorithms, businesses can optimize and uncover new statistical patterns which form the backbone of predictive analytics. Organization with huge data can begin analytics. Before beginning data scientists should make sure that predictive analytics fulfills their business goals and is appropriate for the big data environment.

In today's digital world, real estate shopping has moved almost exclusively online. Real estate listing portals, such as Zillow or Trulia, have helped usher in the era of visual search as the future of buying or selling your home online. They've gotten so popular in fact, that many of the top real estate portals are managing over one million new images per day as sellers and realtors want to provide the best presentation of their home and users demand to see every nook and cranny before actually visiting the property. In order to keep pace with the onslaught of new property listings and unprecedented traffic from potential home buyers, real estate portals have been desperately looking for solutions to manage their visual assets – ie: photos and videos of properties uploaded by users. You have to realize that along with the benefits of user-generated content (ie: access to millions upon millions of uploaded photos for virtual property tours), real estate portals are faced with the unmanageable task of collecting, organizing and displaying that content in an efficient and easy to navigate manner. First, as a real estate portal, how will you go about sorting through all of those images?

How will AI disrupt our lives in 2017? Artificial Intelligence is the next Big Thing. What to expect from home to the office? Didier Delmer is a Multilingual Digital Marketer & Developer.

In this special guest feature, Peter Cnudde, VP of Engineering at Yahoo, provides a bird's eye view for the many ways that Yahoo is using machine learning at scale. This concept is driven by the irrational, yet popular notion that one day, machines will take over the jobs of humans. As Vice President of Engineering for Yahoo, Peter oversees the company's big data and machine learning platforms. He is particularly interested in large scale machine learning and its impact on our society. In the past, Peter has worked at several wireless telecommunications companies including Alcatel and RF Micro Devices.

Aim is to build an ecosystem around the smart home device. Google wants to make its Assistant smarter so it is opening it up to third-party developers. The Google Assistant, which brings together technologies such as Knowledge Graph and Natural Language Processing, is to have its API made open so that the company can build an ecosystem of developers around it. This will hopefully mean that it will be able to connect to more apps and services, making it a more appealing system to customers. In October the company previewed Actions on Google, the developer platform for the Google Assistant, now developers will be able to build Conversation Actions for Google Home, the company's smart home speaker.

Talent acquisition can be one of the most time consuming and frustrating aspects of business. Harsh deadlines and specific requirements, not to mention the piles of applications and resumes, is tough for any recruiter. Tack on retention accountability, candidate experience and employer branding and the job becomes even harder. The emerging HR technology throughout the last decade has strived to take away these many frustrations while improving candidate experience and quality of hire. The buzz around artificial intelligence this year is being shrugged off by many as just a new word HR got ahold of, but what would happen if AI was actually embraced by the recruiting and hiring world?

You have to admit; it sounds a little worrying, right? Google's multiple AI's that make up a large part of the Google Brain Project have been taught not only how to create their own encrypted messages but to also use them with one another when no one else can read them. Alice, Bob, and Eve are three neural networks that were created as part of the Google Brain project in an attempt to get to the bottom of deep learning techniques. Every day they are in operation, they are getting smarter and smarter, and now it seems they have just mastered encryption. During testing, the task that Alice was set was to create a simple form of encryption and work alongside Bob to devise a key made up of an agreed set of numbers.