A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from only one source. This practical concern of accessibility is ubiquitous in various academic and industrial applications. That's what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data, i.e., the extensibility for incrementally available observational data, the adaptability for extra domain adaptation problem except for the imbalance between treatment and control groups, and the accessibility for an enormous amount of data. In this position paper, we formally define the problem of continual treatment effect estimation, describe its research challenges, and then present possible solutions to this problem. Moreover, we will discuss future research directions on this topic.

Our work is aimed at service robots deployed in human environments that will need many specialized object manipulation skill. We believe robots should leverage end-users to quickly and efficiently learn the affordances of objects in their environment. Prior work has shown that this approach is promising because people naturally focus on showing salient rare aspects ofthe objects (Thomaz and Cakmak 2009). We replicate these prior results and build on them to create a semi-supervised combination of self and guided learning.We compare three conditions: (1) learning through self-exploration, (2) learning from demonstrations providedby 10 naive users, and (3) self-exploration seeded with the user demonstrations. Initial results suggests benefits of a mixed initiative approach.

Researchers from the University of Toronto and University of Cambridge are looking into the ways ageism – prejudice against individuals based on age – can be encoded into technologies such as artificial intelligence, which many of us now encounter daily. This age-related bias in AI, also referred to as "digital ageism," is explored in a new paper led by Charlene Chu, an affiliate scientist at the Toronto Rehabilitation Institute's KITE research arm, part of the University Health Network (UHN), and an assistant professor at the Lawrence S. Bloomberg Faculty of Nursing. The paper was recently published in The Gerontologist, the leading journal of gerontology. "The COVID-19 pandemic has heightened our awareness of how dependent our society is on technology," says Chu says. "Huge numbers of older adults are turning to technology in their daily lives which has created a sense of urgency for researchers to try to understand digital ageism, and the risks and harms associated with AI biases."

As technology continues to revolutionise the way we live and work beyond the pandemic, here are some early-stage companies innovating in the IoT space. The World Economic Forum (WEF) Technology Pioneers of 2021 represent a collection of 100 early to growth-stage companies identified as trailblazers working with new technologies and innovations. This year's list includes start-ups shaking up data and cybersecurity and blazing a trail in blockchain and digital assets. Here, we take a look at the IoT and smart city start-ups on the list, covering innovators that are finding advanced tech solutions to a burgeoning list of complex challenges in an increasingly digitised post-pandemic world. Founded by Andrea Thomaz and Vivian Chu in 2017, Diligent Robotics is a female-led early-stage company that makes AI-powered robot assistants for healthcare workers.

Hong Kong (CNN Business)Before the pandemic, Ka Tim Chu, teacher and vice principal of Hong Kong's True Light College, looked at his students' faces to gauge how they were responding to classwork. Now, with most of his lessons online, technology is helping Chu to read the room. An AI-powered learning platform monitors his students' emotions as they study at home. The software, 4 Little Trees, was created by Hong Kong-based startup Find Solution AI. While the use of emotion recognition AI in schools and other settings has caused concern, founder Viola Lam says it can make the virtual classroom as good as -- or better than -- the real thing.

Inside a Schnucks grocery store in St. Louis, Missouri, the toilet paper and baking ingredients are mostly cleared out. A rolling robot turns a corner and heads down an aisle stocked with salsa and taco shells. It comes up against a masked customer wearing shorts and sneakers; he's pushing a shopping cart carrying bread. The robot looks something like a tower speaker on top of an autonomous home vacuum cleaner--tall and thin, with orb-like screen eyes halfway up that shift left and right. A red sign on its long head makes the introductions. Tally freezes, sensing the human, and the customer pauses, seeming unsure of what to do next. Should he maneuver around the robot? Or wait for it to move along on its own?

Data science is a new and maturing field, with a variety of job functions emerging, from data engineering and data analysis to machine and deep learning. A data scientist must combine scientific, creative and investigative thinking to extract meaning from a range of datasets, and to address the underlying challenge faced by the client. There is an ever-growing amount of data generated in all areas of life -- from retail, transport and finance, to healthcare and medical research. Increases in available computing power and recent advances in artificial intelligence have propelled data scientists -- the people who take the raw data, analyze it, and make it useful and usable -- into the spotlight. Data science has topped the list of 50 best jobs in North America since 2016, based on criteria such as earning potential, reported job satisfaction, and the number of job openings on Glassdoor.

Reducing the incidence of surgical site infections (SSIs) is one of the objectives of the French nosocomial infection control program. Manual monitoring of SSIs is carried out each year by the hospital hygiene team and surgeons at the University Hospital of Bordeaux. Our goal was to develop an automatic detection algorithm based on hospital information system data. Three years (2015, 2016 and 2017) of manual spine surgery monitoring have been used as a gold standard to extract features and train machine learning algorithms. The dataset contained 22 SSIs out of 2133 spine surgeries. Two different approaches were compared. The first used several data sources and achieved the best performance but is difficult to generalize to other institutions. The second was based on free text only with semiautomatic extraction of discriminant terms. The algorithms managed to identify all the SSIs with 20 and 26 false positives respectively on the dataset. Another evaluation is underway. These results are encouraging for the development of semi-automated surveillance methods.

Ahead of the event in May, we are taking a look at some the companies and people who are taking part in the massive unconference–an open space event with organization powered by participants. TechNode is organizing the Explore Expo, an exhibition area for young tech startups looking for exposure. The education industry is generally viewed as traditional, dogmatic, and oppressive in many Asian countries, especially in China. As China's edtech sector takes off and begins to attract a deluge of investment, tech companies are exploring more ways to spice up the learning experience. "The compulsory education system is rigid," Chu Liang, CTO of Ellabook, told TechNode, "but over the past decade, technology has been transforming many industries and sectors. Ellabook (咿啦看书) is an ebook reading platform, like Kindle, but for kids from 3 to 12 years-old. The app is animated and interactive, which encompasses a wide range of learning categories like reading skills, English, mathematics, and art.

My husband likes to tell a story from his college years: he was working in data entry, next to a woman with a copy of Anne Rice's erotic novel The Claiming of Sleeping Beauty sitting on her desk. When a supervisor walked by and noticed it, she snapped it up, saying, "This is a romance novel? Hey, let's look for one of the good parts!" Then she started flipping through the pages. Finding page after page of S&M scenarios and graphic sexual activity, she quickly put the book back down.