Gesture recognition and hand motion tracking are important tasks in advanced gesture based interaction systems. In this paper, we propose to apply a sliding windows filtering approach to sample the incoming streams of data from data gloves and a decision tree model to recognize the gestures in real time for a manual grafting operation of a vegetable seedling propagation facility. The sequence of these recognized gestures defines the tasks that are taking place, which helps to evaluate individuals' performances and to identify any bottlenecks in real time. In this work, two pairs of data gloves are utilized, which reports the location of the fingers, hands, and wrists wirelessly (i.e., via Bluetooth). To evaluate the performance of the proposed framework, a preliminary experiment was conducted in multiple lab settings of tomato grafting operations, where multiple subjects wear the data gloves while performing different tasks. Our results show an accuracy of 91% on average, in terms of gesture recognition in real time by employing our proposed framework.

Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, called CausalKinetiX, that identifies structure from discrete time, noisy observations, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying, invariant kinetic model, a key criterion for reproducible research. Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective. The identified variables and models allow for a concise description of the dynamics across multiple experimental settings and can be used for prediction in unseen experiments. We observe significant improvements compared to well established approaches focusing solely on predictive performance, especially for out-of-sample generalization.

A cutting-edge'social' robot designed to keep people company, is to hold bingo lessons for pensioners in a British care home as part of a study. Stevie the robot is so advanced he was recently named one of the best inventions of 2019 and featured on the cover of Time Magazine. There he will keep residents involved, entertained, and engaged - and rather bizarrely he will even be leading bingo sessions. Recently back from a visit to the States, Stevie has been placed into the care of experts from the University of Plymouth's Centre for Health Technology. Dr Conor McGinn, assistant professor at Trinity College Dublin, said: 'This pilot is the start of an exciting new relationship with the University of Plymouth.

Tinder claims to have made 30 billion matches to date, but many of those connections did not go beyond the digital world. A new analysis found that many users do not meet their potential mate in-person and the chances of finding someone interested in a long-term relationship are very slim. Researchers discovered that users need a very large number of matches in order to have just a few meetups - as only 50 percent of users met one match face-to-face. Tinder is a location-based mobile dating service app that presents users with pictures, name, age and other information of potential mates. Users swipe either left (not interested) or right (interested) on the screen, and provided both users swipe right they are matched and can begin messaging.

The Valley's affinity for robotics shows no signs of cooling. Technical enhancements through innovations like AI/ML, compute power and big data utilization continue to drive new performance milestones, efficiencies and use cases. Despite the old saying, "hardware is hard," investment in the robotics space continues to expand. Money is pouring in across robotics' billion-dollar sub verticals, including industrial and labor automation, drone delivery, machine vision and a wide range of others. According to data from Pitchbook and Crunchbase, 2018 saw new highs for the number of venture deals and total invested capital in the space, with roughly $5 billion in investment coming from nearly 400 deals.

Delivery robots suffered a setback this week when New York City made it clear they're not welcome there. On Monday, November 25, just a few days after a FedEx "SameDay Bot" autonomous robot was spotted trundling along a Manhattan street, lawyers for the New York City Department of Transportation sent a strongly worded cease-and-desist letter to the shipping giant, CNN reported. The letter warned FedEx that its last-mile delivery robot breached multiple traffic rules, adding that any further outings made by the machine could result in serious consequences for the firm. "You are hereby directed to immediately cease and desist operating your SameDay Bots on the streets and sidewalks in the City of New York," lawyers said in the letter. "Failure to do so may result in the seizure of the property, notices of violation and/or the commencement of legal action."

Peggy Smedley: For you, what are the most interesting trends that you see? You and I have talked in the past about the IoT (Internet of Things) and I know that you have a lot of vision, a lot of examples that you look at when you think about cloud and edge and we talk about manufacturing and all these things in vertical markets, but for listeners right now, based on investments you guys [Microsoft] are making, what do you see are the most interesting trends? Sam George: Well, I think if you zoom the telescope way back out and look at the very big picture, what we're seeing across all of these vertical markets, whether it's manufacturing or agriculture, smart cites, smart energy. If you take a look at what's happening with all of these, there's a set of disruptive technologies that are fundamentally transforming how those industries function. Cloud was a big catalyst for that and I'd say, very well established at this point. And then IoT, a couple of years ago, really started hitting the scene, building on top of cloud and giving these businesses unprecedented visibility if they were able to take advantage of it back in the early days. Virtually all aspects of their business are able to sense things in the physical world, in realtime, that they weren't able to before. And then while the IoT was happening, edge computing started happening too, which was a normal and natural optimization, where as I connect and start collecting data from these billions of devices that are sensing across all of these different industries that are sensing things that are happening, it's natural to start taking some of the computing that you were doing in the cloud and some of the services that you were taking advantage of and pushing those right out and distributing those right out to the devices themselves for a variety of reasons, whether that's latency concerns or security concerns or anything else. We see this wonderful trend of AI that is powering really new breakthrough capabilities across all of these industries. AI is a great example, where as it takes advantage of those proceeding waves, edge computing and the IoT and cloud. AI can now run in a distributed fashion as well.

Titled "Beginners' Artificial Intelligence and Python Programming", the book was written by the convener of Data Science Nigeria, Olubayo Adekanmbi, …

They promised us the robots wouldn't attack… In what seems like dialogue lifted straight from the pages of a post-apocalyptic science fiction novel, researchers from the University of Massachusetts Amherst and Stanford claim they've developed an algorithmic framework that guarantees AI won't misbehave. The framework uses'Seldonian' algorithms, named for the protagonist of Isaac Asimov's "Foundation" series, a continuation of the fictional universe where the author's "Laws of Robotics" first appeared. According to the team's research, the Seldonian architecture allows developers to define their own operating conditions in order to prevent systems from crossing certain thresholds while training or optimizing. In essence, this should allow developers to keep AI systems from harming or discriminating against humans. Deep learning systems power everything from facial recognition to stock market predictions.

In the race for artificial intelligence dominance, it is currently just a two horse race when looked at on a national level. As our chart shows, when looking at patent applications, investment, talent, research and companies in the sector, the United States and China are top of the charts when it comes to these key metrics. Between these two leaders, there are areas in which one or the other is far stronger, with China well ahead in terms of investment and financing - China accounted for 60 percent of global investment since 2013. The U.S. on the other hand is most dominant from the perspective of the number of companies operating in the field. This chart shows the countries most dominant in key areas of artificial intelligence in 2018.