Imagine a future where you are greeted by a robot as you pop into your local bank branch to deposit some cash. That vision has been a made a reality at the state-owned China Construction Bank (CCB) in Shanghai, where customers can gain access via face-scanning software and talking robots. Billed as China's first person-less bank, the fully-automated branch aims to make banking more convenient and efficient. Clients are greeted by a robot as they enter, who answers questions using voice-recognition software. Visitors can carry out the usual functions such as changing currency, withdrawing cash and renewing Communist party membership fees.

There are two global hotspots in the development of artificial intelligence: Silicon Valley and China. Today, the European Commission will lay out its plan to catch up, publishing a document outlining its long-term AI strategy. It casts Europe as a middle ground, with a stance somewhere between the state-backed firms of China and America's data-rich tech outfits, such as Google. The plan emphasises digital rights and ethics--the commission promises an ethics charter next year, and a global push for co-operation on standards co-ordinated through bodies such as the UN and the OECD. It also commits to spending €110m ($134m) to set up repositories of data and algorithms available to smallish firms getting started in AI. In total, the commission will put forward €14bn in its next budget to support research and development in AI.

One of the formidable challenges healthcare providers face is putting medical data to maximum use. Somewhere between the quest to unlock the mysteries of medicine and design better treatments, therapies, and procedures, lies the real world of applying data and protecting patient privacy. "Today, there are many barriers to putting data to work in the most effective way possible," observes Drew Harris, director of health policy and population health at Thomas Jefferson University's College of Population Health in Philadelphia, PA. "The goals of protecting patients and finding answers are frequently at odds." It is a critical issue and one that will define the future of medicine. Medical advances are increasingly dependent on the analysis of enormous datasets--as well as data that extends beyond any one agency or enterprise.

The ACM constitution provides that our Association hold a general election in the even-numbered years for the positions of President, Vice President, Secretary/Treasurer, and Members-at-Large. Biographical information and statements of the candidates appear on the following pages (candidates' names appear in random order). In addition to the election of ACM's officers--President, Vice President, Secretary/Treasurer--two Members-at-Large will be elected to serve on ACM Council. Please refer to the instructions posted at https://www.esc-vote.com/acm2018. To access the secure voting site, you will need to enter your email address (the email address associated with your ACM member record) and your unique PIN provided by Election Services Co. Should you wish to vote by paper ballot please contact Election Services Co. to request a paper copy of the ballot and follow the postal mail ballot procedures: [email protected] or 1-866-720-4357. Please return your ballot in the enclosed envelope, which must be signed by you on the outside in the space provided. The signed ballot envelope may be inserted into a separate envelope for mailing if you prefer this method. All ballots must be received by no later than 16:00 UTC on 24 May 2018. Validation by the Tellers Committee will take place at 14:00 UTC on 29 May 2018. Jack Davidson's research interests include compilers, computer architecture, system software, embedded systems, computer security, and computer science education. He is co-author of two introductory textbooks: C Program Design: An Introduction to Object-Oriented Programming and Java 5.0 Program Design: An Introduction to Programming and Object-oriented Design. Professionally, he has helped organize many conferences across several fields.

Please also do not insist on constantly changing the features just to sell a new version. We old(er) humans are simply not all that enamored of the latest and greatest tech (recall that, in many cases, we created it), nor are we impressed by the ability to add emojis to our digital correspondence. We have learned that talking is more satisfying than texting, and visits from grandchildren are better than Facebook. Do not pity us--though, if you like, you may envy us.

Whereas people learn many different types of knowledge from diverse experiences over many years, and become better learners over time, most current machine learning systems are much more narrow, learning just a single function or data model based on statistical analysis of a single data set. We suggest that people learn better than computers precisely because of this difference, and we suggest a key direction for machine learning research is to develop software architectures that enable intelligent agents to also learn many types of knowledge, continuously over many years, and to become better learners over time. In this paper we define more precisely this never-ending learning paradigm for machine learning, and we present one case study: the Never-Ending Language Learner (NELL), which achieves a number of the desired properties of a never-ending learner. NELL has been learning to read the Web 24hrs/day since January 2010, and so far has acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (e.g., servedWith(tea,biscuits)), while learning thousands of interrelated functions that continually improve its reading competence over time. NELL has also learned to reason over its knowledge base to infer new beliefs it has not yet read from those it has, and NELL is inventing new relational predicates to extend the ontology it uses to represent beliefs. We describe the design of NELL, experimental results illustrating its behavior, and discuss both its successes and shortcomings as a case study in never-ending learning. NELL can be tracked online at http://rtw.ml.cmu.edu, and followed on Twitter at @CMUNELL. Machine learning is a highly successful branch of artificial intelligence (AI), and is now widely used for tasks from spam filtering, to speech recognition, to credit card fraud detection, to face recognition. Despite these successes, the ways in which computers learn today remain surprisingly narrow when compared to human learning. This paper explores an alternative paradigm for machine learning that more closely models the diversity, competence and cumulative nature of human learning.

Communication with computing machinery has become increasingly'chatty' these days: Alexa, Cortana, Siri, and many more dialogue systems have hit the consumer market on a broader basis than ever, but do any of them truly notice our emotions and react to them like a human conversational partner would? In fact, the discipline of automatically recognizing human emotion and affective states from speech, usually referred to as Speech Emotion Recognition or SER for short, has by now surpassed the "age of majority," celebrating the 22nd anniversary after the seminal work of Daellert et al. in 199610--arguably the first research paper on the topic. However, the idea has existed even longer, as the first patent dates back to the late 1970s.41 Previously, a series of studies rooted in psychology rather than in computer science investigated the role of acoustics of human emotion (see, for example, references8,16,21,34). Blanton,4 for example, wrote that "the effect of emotions upon the voice is recognized by all people. Even the most primitive can recognize the tones of love and fear and anger; and this knowledge is shared by the animals. The dog, the horse, and many other animals can understand the meaning of the human voice. The language of the tones is the oldest and most universal of all our means of communication." It appears the time has come for computing machinery to understand it as well.28 This holds true for the entire field of affective computing--Picard's field-coining book by the same name appeared around the same time29 as SER, describing the broader idea of lending machines emotional intelligence able to recognize human emotion and to synthesize emotion and emotional behavior.

For a while now the DigitalGlobe GBDX team has been running machine learning-based object detection at a significant, continental scale. Each time we add a new model to GBDX we kick the tires and do some comparisons to discover advantages or disadvantages over existing capabilities. We keep our customer use cases in mind, which typically boil down to "monitoring and change" or "pattern of life" activities. Some things we monitor with the models we have today include detecting changes or activity in a parking lot or port. With that in mind we wanted to do a "state of the union" or "state of the map" about the current state of machine learning on satellite imagery.

The debate on Machine Learning vs. Deep Learning has gained considerable steam in the past few years. The fundamental strength of both these technologies lies in their ability to learn from available data. Though both of these offshoot AI technologies triumph in "learning algorithms," the manner in which Machine Learning (ML) algorithms learn is very different from the learning methods of the Deep Learning (DL) algorithms. While ML directly observes data patterns and establishes correlations, DL algorithms learn progressively from intricate layers of knowledge. DL is considered a subset of ML, where learning happens through a layered network of algorithms commonly known as an Artificial Neural Network (ANN).

S. C. Stuart is an award-winning digital strategist and technology commentator for ELLE China, Esquire Latino, Singularity Hub, and PCMag, covering: artificial intelligence; augmented, virtual, and mixed reality; DARPA; NASA; US Army Cyber Command; sci-fi in Hollywood (including interviews with Spike Jonze and Ridley Scott); and robotics (real-life... See Full Bio