Artificial Intelligence And Big Data: Good For Innovation?

#artificialintelligence

China's 19-year-old Go player Ke Jie reacts during the second match against Google's artificial intelligence programme AlphaGo in Wuzhen, eastern China's Zhejiang province on May 25, 2017. Artificial intelligence is firmly embedded throughout the economy. Financial services firms use it to provide investment advice to customers, automakers are using it in vehicle autopilot systems, technology companies are using it to create virtual assistants like Alexa and Siri, and retailers are using artificial intelligence (AI) together with customers' prior sales histories, to predict potential purchases in the future, to name but a few examples. The potential of AI to boost economic growth has been discussed in numerous forums, including by Accenture, the Council on Foreign Relations, the McKinsey Global Institute, the World Economic Forum, and President Obama's Council of Economic Advisers, among others. The most dramatic advances in AI are coming from a data-intensive technique known as machine learning.


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AI Magazine

The algorithm successfully detects and tracks 11 objects (5 teammates, 5 opponents, and 1 ball) at 30 frames a second. The algorithm determines the position and orientation for the robots. In addition, a Kalman-Bucy filter (Kalman and Bucy 1961) is used as a predictor of the ball's trajectory. This prediction is an integral factor in our robots' control and strategic decisions. Before developing strategic behaviors, the robots need a general control mechanism.


Artificial Intelligence And Big Data: Good For Innovation?

#artificialintelligence

China's 19-year-old Go player Ke Jie reacts during the second match against Google's artificial intelligence programme AlphaGo in Wuzhen, eastern China's Zhejiang province on May 25, 2017. Chinese netizens fumed on May 25 over a government ban on live coverage of Google algorithm AlphaGo's battle with the world's top Go player, as the programme clinched their three-match series in the ancient board game. Artificial intelligence is firmly embedded throughout the economy. Financial services firms use it to provide investment advice to customers, automakers are using it in vehicle autopilot systems, technology companies are using it to create virtual assistants like Alexa and Siri, and retailers are using artificial intelligence (AI) together with customers' prior sales histories, to predict potential purchases in the future, to name but a few examples. The potential of AI to boost economic growth has been discussed in numerous forums, including by Accenture, the Council on Foreign Relations, the McKinsey Global Institute, the World Economic Forum, and President Obama's Council of Economic Advisers, among others.


Building of a Heterogeneous Segway Soccer Team Towards a Peer-To-Peer Human Robot Team

AAAI Conferences

Robotic soccer is an adversarial multi-agent research domain, in which issues of perception, multi-agent coordination and team strategy are explored. One area of interest investigates heterogeneous teams of humans and robots, where the teammates must coordinate not as master and slave, but as equal participants. We research this peer-to-peer question within the domain of Segway soccer, where teams of humans riding Segway HTs and robotic Segway RMPs coordinate together in competition against other human-robot teams. Beyond the task of physically enabling these robots to play soccer, a key issue in the development of such a heterogeneous team is determining the balance between human and robot player. The first ever Segway soccer competition occurred at the 2005 RoboCup US Open, where demonstrations were held between Carnegie Mellon University (CMU) and the Neurosciences Institute (NSI).


Skill Acquisition and Use for a Dynamically-Balancing Soccer Robot

AAAI Conferences

Dynamically-balancing robots have recently been made available by Segway LLC, in the form of the Segway RMP (Robot Mobility Platform). We have addressed the challenge of using these RMP robots to play soccer, building up upon our extensive previous work in this multi-robot research domain. In this paper, we make three contributions. First, we present a new domain, called Segway Soccer, for investigating the coordination of dynamically formed, mixed human-robot teams within the realm of a team task that requires realtime decision making and response. Segway Soccer is a game of soccer between two teams consisting of both Segway riding humans and Segway RMPs. We believe Segway Soccer is the first game involving both humans and robots in cooperative roles and with similar capabilities.