"When my father was misdiagnosed and administered the wrong medication placing him in a coma nearly 20 years ago, I saw firsthand the need for technology to help physicians make accurate decisions," said Tanveer Syeda-Mahmood, IBM Fellow and Chief Scientist of the Medical Sieve Radiology Grand Challenge Project at IBM Research – Almaden in San Jose, Calif. This week in Chicago, Dr. Syeda-Mahmood's mission meets the real world as IBM Research debuts a new Watson-powered demo that shows the future of Artificial Intelligence (AI) in radiology. The demo is the result of a shared vision by Dr. Syeda-Mahmood and Dr. Eugene Walach from IBM Research – Haifa to help radiologists make accurate patient diagnoses quickly and easily. In any given day, radiologists can review up to thousands of medical images to make health diagnoses. To date, accuracy has relied mainly on medical professionals piecing together multiple sources of clinical information visually and manually to make critical decisions, including electronic health records, research publications and other data.

The next Angry Birds AI competition will be held at IJCAI 2017 in Melbourne, Australia, August 24-25, 2017. For more details please refer to the Call for Participation and the detailed Competition Rules. This year we will again have two competition tracks, the standard track and a competitive track where two AI agents try to solve the same level with alternating shots. Previous Angry Birds AI competitions were held in Sydney in December 2012 during the Australasian AI conference, in Beijing in August 2013 during the IJCAI conference, in Prague in August 2014 at the ECAI conference, in Buenos Aires in July 2015 at the IJCAI conference, and most recently at the IJCAI 2016 conference in New York. Further details about these past competitions can be found here.

Amazon Lex, Amazon Polly, and Amazon Rekognition are based on the same proven, highly scalable Amazon technology built by the thousands of deep learning and machine learning experts across the company. Amazon AI services all provide high-quality, high-accuracy AI capabilities that are scalable and cost-effective. Amazon AI services are fully managed services so there are no deep learning algorithms to build, no machine learning models to train, and no up-front commitments or infrastructure investments required. This frees developers to focus on defining and building an entirely new generation of apps that can see, hear, speak, understand, and interact with the world around them. To learn more about Amazon Lex, Amazon Polly, or Amazon Rekognition, visit: https://aws.amazon.com/amazon-ai

Artificial intelligence (AI) shows a lot of promise yet some of the most recent news seems a bit alarming. Two AI agents were programed to communicate privately and they created their own cryptography. AI is now improving its capabilities by dreaming. And AI predicted correctly that Trump would win the presidency. Naturally, these events are causing people to ask a lot of questions about AI.

Driven by a declining population, a trend for developing robotic babies has emerged in Japan as a means of encouraging couples to become "parents". The approaches taken vary widely and are driven by different philosophical approaches that also beg a number of questions, not least whether these robo-tots will achieve the aim of their creators. To understand all of this it is worth exploring the reasons behind the need to promote population growth in Japan. The issue stems from the disproportionate number of older people. Predictions from the UN suggest that by 2050 there will be about double the number of people living in Japan in the 70-plus age range compared to those aged 15-30.

Analyses performed using Spark of brain activity in a larval zebrafish: embedding dynamics of whole-brain activity into lower-dimensional trajectories. This open source computing framework unifies streaming, batch, and interactive big data workloads to unlock new applications. The growth of data volumes in industry and research poses tremendous opportunities, as well as tremendous computational challenges. As data sizes have outpaced the capabilities of single machines, users have needed new systems to scale out computations to multiple nodes. As a result, there has been an explosion of new cluster programming models targeting diverse computing workloads.1,4,7,10 At first, these models were relatively specialized, with new models developed for new workloads; for example, MapReduce4 supported batch processing, but Google also developed Dremel13 for interactive SQL queries and Pregel11 for iterative graph algorithms. In the open source Apache Hadoop stack, systems like Storm1 and Impala9 are also specialized. Even in the relational database world, the trend has been to move away from "one-size-fits-all" systems.18 Unfortunately, most big data applications need to combine many different processing types. The very nature of "big data" is that it is diverse and messy; a typical pipeline will need MapReduce-like code for data loading, SQL-like queries, and iterative machine learning. Specialized engines can thus create both complexity and inefficiency; users must stitch together disparate systems, and some applications simply cannot be expressed efficiently in any engine. In 2009, our group at the University of California, Berkeley, started the Apache Spark project to design a unified engine for distributed data processing.

As in the global survey, we are concerned with platform business models and the design choices that allow these business models to grow. We find the term platform, which is well-established in economic and management literature, offers a more useful and accurate term than some of the terms that have been used such as "share economy companies," "internet companies" or, even more broadly, "tech companies." Network effects are a key characteristic that distinguish platforms from other business models. As more users engage with a platform, the more attractive the platform becomes to potential new users. When more users attract more users, a dynamic is created that in turn triggers a self-reinforcing cycle of growth.

It had always been Katherine Goble's great talent to be in the right place at the right time. In August 1952, 12 years after leaving graduate school to have her first child, that right place was in Marion, Virginia, at the wedding of her husband, Jimmy Goble's, little sister Patricia. Pat, a vivacious college beauty queen just two months graduated from Virginia State College, was marrying her college sweetheart, a young army corporal named Walter Kane. Jimmy's other sister and brother-in-law, Margaret and Eric Epps, had journeyed from Newport News, and the newlyweds planned to accompany the Eppses back to the coast, hitching a ride to their honeymoon at Hampton's segregated Bay Shore Beach resort. "Why don't y'all come home with us too?" Eric asked Katherine. "I can get Snook a job at the shipyard," he said, using Jimmy's family nickname. "In fact, I can get both of you jobs." There's a government facility in Hampton that's hiring black women, Eric told Katherine, and they're looking for mathematicians. It's a civilian job, he told her, but attached to Langley Memorial Aeronautical Laboratory--the oldest outpost of the National Advisory Committee for Aeronautics, or NACA. Katherine listened intently as her brother-in-law described the work, her thumb cradling her chin, her index finger extended along her cheek, the signal that she was listening carefully. She and Jimmy made a living as public school teachers, but their paychecks were modest. The needs of their three growing daughters seemed greater by the day, and the couple could only just cover their basics and squeeze out a little extra for piano lessons or Girl Scouts. Deft with a sewing machine, Katherine bought fabric from the dry goods store and stayed up nights making school outfits for the girls and dresses for herself.

Biologically based computational modeling promises virtual characters capable of face-to-face human interaction. Of all the experiences we have in life, face-to-face interaction fills many of our most meaningful moments. The complex interplay of facial expressions, eye gaze, head movements, and vocalizations in quickly evolving "social interaction loops" has enormous influence on how a situation will unfold. From birth, these interactions are a fundamental element of learning and lay the foundation for successful social and emotional functioning through life. What are the underlying processes from which this most human form of interaction emerges? Will we be able to interact with computers in a face-to-face way that feels natural? This article discusses the unique challenges of realistically simulating the appearance and behavior of the face to create interactive autonomous virtual human models that support naturalistic learning and have the "illusion of life." We describe our recent progress toward this goal with "BabyX," an autonomously animated psycho-biological model of a virtual infant. While we explore drivers of facial behavior, we also expect this foundational approach has the potential for more "human" computer interfaces. We also describe our work on our "Auckland Face Simulator" we are developing to broaden this work beyond infants and give a more realistic face and a greater biological basis to adult conversational agents. Simulating the face has great potential for human-computer interaction (HCI), as it increases the available communication channels between humans and machines in an intuitive, accessible way. But it is also a vehicle with which to explore our own nature.

In each issue of Communications, we'll publish selected posts or excerpts. Early September saw the 2016 International Computing Education Research (ICER) conference in Melbourne, Australia. The conference was terrific, as is usual for ICER. Two of its papers were meta-papers studying the ICER community itself. They found the community has healthy levels of newcomers and collaboration, and features methodological rigor and strong theoretical foundations.