We trust you've had a suitably enjoyable week, especially those in the UK enjoying the hot weather. Let's journey back and look at some of the digital marketing stats you might have missed. The roundup includes news about GDPR, personalisation, AI, and lots more. As always, be sure to check out the Internet Statistics Compendium for further facts and figures. When it comes to digital experiences, personalisation is way down on the list of things consumers care about.

A new report by the Lords Select Committee in the UK claims that Britain is in a strong position to be a world leader in the development of artificial intelligence. But to get there--and to keep AI safe and ethical--tech firms should follow the Committee's newly proposed "AI Code." The new report was penned by the House of Lords Artificial Intelligence Committee, and it's titled "AI in the UK: Ready, Willing and Able?." The AI Committee is proposing a path for both the British government and UK-based businesses to move forward as AI increasingly expands in power and scope. The report is particularly timely given the recent scandal surrounding Cambridge Analytica's use of Facebook data and growing concerns that tech companies aren't working in the public's best interests.

Fintech startups around the world are changing the way people manage their money. Apps are reshaping entire payment systems, people are learning to live with chatbots, and innovative interfaces are challenging the way banking gets done. Much is happening abroad, but the impact of these trends on the U.S. market is very hard to predict. Our financial system is far more complex and well entrenched than systems in other countries, with thousands of institutions and a complex regulatory environment featuring countless state-level authorities and multiple federal agencies. U.S. banks tend to be slower to adopt technology, in part because of the gargantuan task of updating old, siloed systems.

Speaking at the Sorbonne in September 2017, French president Emmanuel Macron made clear his ambitions for Europe to become a global leader in the field of artificial intelligence (AI). The European Commission is presently working on a strategy on this and is set to deliver a communication on it in the coming months. But there is much uncertainty over how ambitions such as Macron's can be attained. Much will depend on how the European Court of Justice and member states interpret a number of key provisions of the General Data Protection Regulation (GDPR). This is the mammoth culmination of the European Union's five-year effort to make European data-protection law fit for the 21st century.

Over the past two decades, technological innovations have brought significant change for organisations throughout Ireland. Through the development of the internet, the explosion in the number of mobile devices, and the emergence of the sharing economy, technology is now central to all our lives – from how we share information, access services and interact with others. And that change is accelerating. We are on the verge of a new era of digital transformation. Emerging technologies ranging from Artificial Intelligence (AI), Virtual Reality (VR) Augmented Reality (AR), cloud computing and robotics, to machine learning will revolutionise how we work, live, play, and learn.

Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddings have shown promising results on tasks such as link prediction, entity recommendation, question answering, and triplet classification. However, only a few methods can compute low-dimensional embeddings of very large knowledge bases. In this paper, we propose KG2Vec, a novel approach to Knowledge Graph Embedding based on the skip-gram model. Instead of using a predefined scoring function, we learn it relying on Long Short-Term Memories. We evaluated the goodness of our embeddings on knowledge graph completion and show that KG2Vec is comparable to the quality of the scalable state-of-the-art approaches and can process large graphs by parsing more than a hundred million triples in less than 6 hours on common hardware.

Today, the amount of data that can be retrieved from communications networks is extremely high and diverse (e.g., data regarding users behavior, traffic traces, network alarms, signal quality indicators, etc.). Advanced mathematical tools are required to extract useful information from this large set of network data. In particular, Machine Learning (ML) is regarded as a promising methodological area to perform network-data analysis and enable, e.g., automatized network self-configuration and fault management. In this survey we classify and describe relevant studies dealing with the applications of ML to optical communications and networking. Optical networks and system are facing an unprecedented growth in terms of complexity due to the introduction of a huge number of adjustable parameters (such as routing configurations, modulation format, symbol rate, coding schemes, etc.), mainly due to the adoption of, among the others, coherent transmission/reception technology, advanced digital signal processing and to the presence of nonlinear effects in optical fiber systems. Although a good number of research papers have appeared in the last years, the application of ML to optical networks is still in its early stage. In this survey we provide an introductory reference for researchers and practitioners interested in this field. To stimulate further work in this area, we conclude the paper proposing new possible research directions.

For this year's report, I took a stab at learning all the CSS/JS that I've always wanted to know, and then went after the task of making a fully responsive report. I've succeeded in doing so, and so this PDF version isn't as good as the real thing. In the next few days I will be sharing a link to the real digital experience. But for now -- enjoy this static version of the report which has a few parts that couldn't render to static form. Because ... this year's report is truly computationally designed and therefore needs to be expressed appropriately (smile). Expect a video version on my new YouTube channel "John Maeda is Learning." What can I do about it? As the marginal return on computing power (a la Moore's law) diminishes and technology is less of a differentiating factor, the value of design has entered the foreground. Five (20%) of the top cumulative-funded VC- backed ventures that have raised additional capital since 2013 are noted to have designer co-founders.

A version of this article appeared in the Spring 2018 issue of strategy business. If you haven't noticed, a high-stakes global game of digital disruption is currently under way. It is fueled by the latest wave of technology: advances in artificial intelligence, data analytics, robotics, the Internet of Things, and new software-enabled industrial platforms that incorporate all these technologies and more. Every enterprise leader recognizes that, as a result, the prevailing business models in his or her industry could drastically and fundamentally change. A wide range of industries, such as entertainment and media, military contracting, and grocery retail have already been profoundly affected. No enterprise, including yours, can afford to ignore the threat. Yet most companies are still not moving fast enough to meet this change. Some leaders are still in denial about it, some are reluctant to upend the status quo in their companies, and some are unaware of the necessary steps to take. But these excuses are not good enough. You may not have needed a plan for the new digital age yet, if only because it didn't seem relevant to your industry. But you will need it now.

IoT (Internet of Things), the convergence of IT and OT, rapid application development, digital twin simulation models, cyber-physical systems, advanced robots and cobots, additive manufacturing, autonomous production, consistent engineering across the entire value chain, thorough data collection and provisioning, horizontal and vertical integration, the cloud, big data analytics, virtual/augmented reality and edge computing amidst a shift of intelligence towards the edge (artificial intelligence indeed): these are some of the essential technological components of the fourth industrial revolution. Those are quite a lot of terms and components indeed. Yet, Industry 4.0 is a rather vast vision and, increasingly, vast reality that also stretches beyond merely these technological aspects. It is an end-to-end industrial transformation. What makes it all the more fascinating (and at first sight complex) is that convergence of two worlds which have been disconnected thus far: Information ...