Resiliency and scalability of contact centers are the key factors to Business Continuity Planning. Contact centers built on legacy systems are expensive to maintain and do not scale well to handle increasing customer anxiety. Contact center operations face dips in agent productivity due to legacy technology and skill gaps. To overcome these business challenges, organizations must implement a flexible solution and leverage automation to enhance operational scalability. Join our upcoming webinar with guest speaker, Analyst Kate Leggett of Forrester Research to learn how to leverage a full suite of AI technologies, including Natural Language Processing and machine and deep learning to create a smarter, and more scalable contact center.

This week, we are excited to announce the launch of the Radiology: AI podcast series. Through this podcast, we will be discussing the science and emerging applications of AI in radiology. The podcasts will consist of interviews with leading figures in AI in radiology, highlights of key published articles in the journal, and discussions with authors of cutting-edge manuscripts. A new episode will be released every 2 months in the first phase. This podcast will serve as a new means of communicating exciting advances in AI in radiology with our readers.

In this introductory lecture set of lectures I will give a very quick overview of the different kinds of machine learning paradigms and therefore I call this lectures machine learning.

We consider a system consisting of a single transmitter/receiver pair and $N$ channels over which they may communicate. Packets randomly arrive to the transmitter's queue and wait to be successfully sent to the receiver. The transmitter may attempt a frame transmission on one channel at a time, where each frame includes a packet if one is in the queue. For each channel, an attempted transmission is successful with an unknown probability. The transmitter's objective is to quickly identify the best channel to minimize the number of packets in the queue over $T$ time slots. To analyze system performance, we introduce queue length regret, which is the expected difference between the total queue length of a learning policy and a controller that knows the rates, a priori. One approach to designing a transmission policy would be to apply algorithms from the literature that solve the closely-related stochastic multi-armed bandit problem. These policies would focus on maximizing the number of successful frame transmissions over time. However, we show that these methods have $\Omega(\log{T})$ queue length regret. On the other hand, we show that there exists a set of queue-length based policies that can obtain order optimal $O(1)$ queue length regret. We use our theoretical analysis to devise heuristic methods that are shown to perform well in simulation.

You can watch this seminar here at 1PM EST (10AM PST) on May 15th. Abstract: To enable the next generation of smart robots and devices which can truly interact with their environments, Simultaneous Localisation and Mapping (SLAM) will progressively develop into a general real-time geometric and semantic Spatial AI' perception capability. I will give many examples from our work on gradually increasing visual SLAM capability over the years. However, much research must still be done to achieve true Spatial AI performance. A key issue is how estimation and machine learning components can be used and trained together as we continue to search for the best long-term scene representations to enable intelligent interaction.

For this edition of My Journey In Data Science column, Analytics India Magazine got in touch with a data scientist, influencer and blogger. Rahul Agrawal, Data Scientist at Walmart Labs, shared his exciting journey in data science, and also offered advice on best practices for aspirants to thrive in the ever-changing data science landscape. Rahul is a mechanical engineer from IIT Delhi, who started his job in a steel company in 2010, but quit the job since it was not interesting enough. Then he joined Fractal Analytics in 2011 as a business analyst. "Initially, I wrote a lot of SQL and made dashboards – most of the work revolved around reporting. And it was not a love-at-first-sight for me," says Rahul.

AI is seen as one of the first lines of defence in a pandemic. Given the current situation with COVID-19, hospitals and healthcare facilities are using AI to help screen and triage patients and identify those most likely to develop severe symptoms. The use of data and analytics is also helpful to track and contain diseases. As the Global Government Practice lead, Steve Bennett is helping governments around the world put their data to work for the citizens they serve. In his current role, he drives strategic industry positioning and messaging in global government markets.

According to research by IDC, global spending on cognitive and AI systems will reach $57.6 billion by 2021! Artificial Intelligence has surrounded us with the most innovative tech inventions, and almost every critical sector or industry relies on AI to accomplish a specific task that is difficult for humans to achieve. In this run, AI is also driving the market for education, and helping to automate the process to increase profitability for educators and students as well. Today, users are expecting more customized content and unbeatable browsing experience. It has forced web development companies to think out-of-the-box rather than getting glued to old and conventional methods.

In this tutorial, we will learn how to apply Computer Vision, Deep Learning, and OpenCV to identify potential child soldiers through automatic age detection and military fatigue recognition. Military service is something of personal importance to me, something I consider honorable and admirable. That's precisely the reason why this project, leveraging technology to identify child soldiers, is something I feel strongly about -- nobody should be forced to serve, and especially young children. You see, the military has always been a big part of my family growing up, even though I did not personally serve. Even outside my direct family, the military was still part of my life and community. I went to high school in a rural area of Maryland. If you didn't want to become a farmer or work in agriculture, that really only left two options -- go to college or join the military. If I'm recalling correctly, before I graduated from high school, at least 10 kids from my class enlisted, some of whom I knew personally and had classes with.

Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.