Machine learning is creating a foothold in the business world, especially when it comes to innovative digital experience, and Web Content Management (WCM) players are diving headfirst into machine learning in the aim of supporting a smart experience across industries. As Forrester's recent industry overview said, "The web CMS market is changing because more organizations recognize the necessity of contextual digital experiences. Every vendor in this landscape is tracking toward this goal."¹ As contextual experiences increasingly become brand differentiators, the ability of machine learning to provide these experiences at scale is massively advantageous. The broad statement that machine learning and other AI technologies are going to infiltrate all corners of our lives, while likely true, paints an often dystopian picture that can be a bit overwhelming.

The concept of artificial intelligence (AI) has always fascinated humans. The ancient Greek myths of Hephaestus and Pygmalion incorporated the idea of intelligent robots and artificial beings. In the 1950s, British scientist Alan Turing proposed the now-famous Turing Test as a measure of machine intelligence, and Isaac Asimov published his Three Laws of Robotics. The Turing Test has been long passed, and AI is an everyday occurrence with Siri and Google Now. At the same time SMS messaging has become the most popular method of written communication in history, with an estimated 6 billion humans now using mobile messaging to talk to each other around the world.

"Learning", "thinking", "intelligence", even "cognition"… Such words were once reserved for humans (and to a lesser extent, other highly complex animals), but have now seemingly been extended to a "species" of machines, machines infused with artificial intelligence or "AI". In October 2015, a computer program developed by Google DeepMind, named AlphaGo, defeated the incumbent European champion at the complex ancient Chinese board game of Go. In March 2016, AlphaGo went on to defeat the world champion, Lee Sedol. This seminal moment caught the world's attention, the media have since been incessantly covering every AI-related story, and companies from all walks of life have since been on a mission to add "artificial intelligence" to their business description. At Platinum we have been closely following the major technological trends for many years.

The way we age is deeply fascinating. Indeed, knowing how our faces will look in 20, 30, or 40 years' time is a trick that many would find captivating. A number of techniques exist that can do this. But they are time-consuming and hence expensive. So a cheap and quick way to age faces in photographs would be a handy trick.

A week hasn't even passed since the inauguration, but television news is saturated with the flurry of activity from President Donald Trump's administration. Trump, via Twitter, promised to launch an investigation into illegal voting and threatened to "send in the Feds" if Chicago police can't fix the "carnage." And that was just between Tuesday and Wednesday. This heightened scrutiny compelled the Internet Archive, a repository of everything posted on the web, to launch its Trump Archive in early January. You, perhaps, digitally time-traveled with the Internet Archive's Wayback Machine, or checked out free books, movies and software. The Trump Archive, which draws content from The Internet Archive's TV News Archive, includes more than 520 hours of televised Trump speeches, interviews, debates and other broadcasts tracing back to 2009.

Discovering causal relations is fundamental to reasoning and intelligence. In particular, observational causal discovery algorithms estimate the cause-effect relation between two random entities $X$ and $Y$, given $n$ samples from $P(X,Y)$. In this paper, we develop a framework to estimate the cause-effect relation between two static entities $x$ and $y$: for instance, an art masterpiece $x$ and its fraudulent copy $y$. To this end, we introduce the notion of proxy variables, which allow the construction of a pair of random entities $(A,B)$ from the pair of static entities $(x,y)$. Then, estimating the cause-effect relation between $A$ and $B$ using an observational causal discovery algorithm leads to an estimation of the cause-effect relation between $x$ and $y$. For example, our framework detects the causal relation between unprocessed photographs and their modifications, and orders in time a set of shuffled frames from a video. As our main case study, we introduce a human-elicited dataset of 10,000 pairs of casually-linked pairs of words from natural language. Our methods discover 75% of these causal relations. Finally, we discuss the role of proxy variables in machine learning, as a general tool to incorporate static knowledge into prediction tasks.

Learning rates for regularized least-squares algorithms are in most cases expressed with respect to the excess risk, or equivalently, the $L_2$-norm. For some applications, however, guarantees with respect to stronger norms such as the $L_\infty$-norm, are desirable. We address this problem by establishing learning rates for a continuous scale of norms between the $L_2$- and the RKHS norm. As a byproduct we derive $L_\infty$-norm learning rates, and in the case of Sobolev RKHSs we actually obtain Sobolev norm learning rates, which may also imply $L_\infty$-norm rates for some derivatives. In all cases, we do not need to assume the target function to be contained in the used RKHS. Finally, we show that in many cases the derived rates are minimax optimal.

Advisors may feel threatened by so-called robo-advisors, but they should be more concerned about artificial intelligence, WealthManagement.com AI, a computer science that analyses data to make predictions or solve problems, could replace up to 45% of U.S. jobs within 20 years, according to Oxford University. Those include higher-complexity roles like accountants and financial advisors. "Financial advisors give advice based on the market and based on long-term trends, and that's exactly what machines are good at," AI expert James Barrat tells WealthManagement.com. Already, an IBM Watson team is working on applying the system to wealth management--from identifying clients' investment preferences to identifying those who are likely to leave an advisor.

Harriet Green, GM, Watson IoT, Commerce & Education and Mona Abutaleb, SVP of Services, Ricoh Americas with the voice controlled interactive whiteboard in Munich, Germany. Imagine if the whiteboard in your next corporate meeting could take notes when you talked and add comments from your teammates in the meeting. IBM and Ricoh Europe have announced an interactive whiteboard with artificial intelligence (AI) that puts cognitive computing right in the middle of a meeting room. The IBM Watson-powered whiteboard looks like any other run-of-the-mill meeting room whiteboard. Anyone in the meeting room or remotely joining by conference call, can control what's on the whiteboard with voice commands.

Project Description: Forensic facial reconstruction research is at the crossroads of art, medicine and science. It can be employed in the context of forensic investigation and for creating three-dimensional portraits of people from the past, from dead bodies and ancient Egyptian mummies to digital animations [1]. Previously, forensic anatomists and artists have relied on manually creating a 3D face clay model or through using computerized 3D forensic facial reconstruction software to manually reconstruct the face [2]. The aim of this multi-disciplinary PhD project is to develop advanced machine-learning and image-processing methods to learn how to automatically and accurately predict a facial'photograph' of a person from their skull (reconstructed using MRI), which can contribute to advancing both forensic medicine and science [2] as well as surface shape reconstruction from photos [3]. Developing such predictive models could be made in a fully quantitative manner with statistical uncertainty bounds on the predicted facial features as well as dynamic morphing of the predicted face within these bounds to aid recognition.