AI and related advancements are progressively playing the role of a disruptor in business and society. The application of AI is also increasing in the healthcare domain. These advances can possibly change numerous parts of patient care, just as regulatory procedures inside supplier, patient experience, and pathology labs. There are as of now various researches recommending that AI can proceed just as or better than people at key human services, for example, diagnosing the ailment. Today, algorithms are beating radiologists at spotting harmful tumors.

Japan's first robot bartender has begun serving up drinks in a Tokyo pub in a test that could usher in a wave of automation in restaurants and shops struggling to hire staff in an aging society. The repurposed industrial robot serves drinks in its own corner of a pub run by restaurant chain Yoronotaki. A face on an attached tablet computer smiles as it chats about the weather while preparing orders. The robot, made by QBIT Robotics, can pour a beer in 40 seconds and mix a cocktail in a minute. It uses four cameras to monitor customers to analyze their expressions with artificial intelligence software.

In the consumer realm, technologies based on artificial intelligence (AI) are slowly changing the way we manage everyday tasks. Take the driving app Waze, for example. Waze uses crowdsourced data, social networking conversations, and cognitive learning to help shave time off daily commutes by providing the most efficient route based on current conditions and individual driving preferences. Or consider products like Nest. Gone are the days of paying to heat or cool your house while no one's home.

Often, when modeling human decision-making behaviors in th e context of human-robot teaming, the emotion aspect of human is ignored. Nevertheless, the influence of em otion, in some cases, is not only undeniable but beneficial. This work studies the humanlike characteristics brought b y regret emotion in one-human-multi-robot teaming for the application of domain search. In such application, the task management load is outsourced to the robots to reduce the human's workload, freeing the human to do more important work. The regret decision model is first used by each robot for deciding whether to request human service, th en is extended for optimally queuing the requests from multiple robots. For the movement of the robots in the domain search, we designed a path planning algorithm based on dynamic programming for each robot. The simulation shows that the humanlike characteristics, namely, risk-seeking and risk-aversion, indeed bring some appealing eff ects for balancing the workload and performance in the human-multi-robot team.

Augmented Intelligence is the result of decades of thought leadership in human services, whose leaders have worked closely with human services professionals across the country at county, state and federal levels. We are an outgrowth of Stewards of Change (SOC), a human services think tank and consultancy founded in 2005 after working with the Bloomberg administration in New York City on foster parent recruitment.

China's first web-only bank hopes artificial intelligence can improve customer service through the use of virtual robots powered by technologies such as facial recognition, speech recognition and natural language processing. AI is "there only to improve human services," Yang Qiang, an AI consultant at Tencent's WeBank, told CNBC's Arjun Kharpal at the East Tech West conference in the Nansha district of Guangzhou, China. "Automated service is not an enemy to human services," he said. "They should work side by side." In addition, advances in technology create possibilities for greater efficiencies in traditional bank roles such as processing loan applications, risk analysis and offering personalized service, he said.

The deputy director of a large county human services agency, she's been wrestling all week with staff turnover and media coverage about long wait times for services. Heading home on Friday evening, she worries that she might spend the rest of her career playing defense at work. After a Saturday morning of chauffeuring her kids to soccer games and music lessons, Natalie collapses on the couch. She relaxes to music from one of her favorite radio stations, wondering how Pandora always manages to serve up exactly the songs that fit her mood. After she's had a chance to unwind, Siri gives her the week's top headlines, reminds her that her niece's graduation is coming up, recommends a gift for the niece, and, when Natalie confirms the choice, places an order. Later, Natalie's fitness band reminds her that it's time to head to the gym for a session with her trainer. On the way to the gym, Waze alerts her to an accident ahead and automatically routes her around it.

We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan enables early detection of emerging patterns in spatio-temporal data, accounting for both the non-iid nature of the data and the fact that detecting subtle patterns requires integration of information across multiple spatial areas and multiple time steps. We apply this approach to 17 years of county-aggregated data for monthly opioid overdose deaths in the New York City metropolitan area, showing clear advantages in the utility of discovered patterns as compared to typical anomaly detection approaches. To detect and characterize emerging overdose patterns that differentially affect a subpopulation of the data, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved), we apply the Multidimensional Tensor Scan to 8 years of case-level overdose data from Allegheny County, PA. We discover previously unidentified overdose patterns which reveal unusual demographic clusters, show impacts of drug legislation, and demonstrate potential for early detection and targeted intervention. These approaches to early detection of overdose patterns can inform prevention and response efforts, as well as understanding the effects of policy changes.