'This is a very timely, comprehensive and well written book in what is now one of the most dynamic and impactful areas of modern applied mathematics. Data science is rapidly taking center stage in our society. The subject cannot be ignored, either by domain scientists or by researchers in applied mathematics who intend to develop algorithms that the community will use. The book by Brunton and Kutz is an excellent text for a beginning graduate student, or even for a more advanced researcher interested in this field. The main theme seems to be applied optimization.

At QCon SF, Daniel Situnayake presented "Machine learning on mobile and edge devices with TensorFlow Lite". TensorFlow Lite is a production-ready, cross-platform framework for deploying ML on mobile devices and embedded systems, and was the main topic of the presentation. The key takeaways from it included understanding and getting started with TensorFlow Lite, and on-device machine learning on various devices – specifically microcontrollers and optimizing the performance of machine learning models. Situnayake, developer advocate for TensorFlow Lite at Google, began the presentation by explaining what machine learning is. Traditionally a developer feeds rules and data into an application and output answers, while with machine learning the developer or data scientists' feeds in the answers and data and the output are rules, which can be applied in the future.

The graph represents a network of 3,198 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 22 November 2019 at 06:21 UTC. The requested start date was Friday, 22 November 2019 at 01:01 UTC and the maximum number of tweets (going backward in time) was 5,000. The tweets in the network were tweeted over the 3-day, 18-hour, 23-minute period from Sunday, 17 November 2019 at 17:30 UTC to Thursday, 21 November 2019 at 11:54 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

In Swedish airports, new technologies are being tested and installed to drive innovation and improve the passengers' experience. Within this digitally-lead era, airports across the world are experimenting with different technologies to determine the best way to secure innovative growth and optimise processes. To gain a perspective of the digital approach within airports across Sweden, International Airport Review spoke to Karin Gylin, Head of Innovation at Swedavia AB, during Airport IT & Security 2019 regarding how new technological applications are benefiting passenger service. In December 2018, we implemented the chatbot. It's been working very well and has been well received by passengers.

Shelf, the leading AI-enabled knowledge management platform on the market, received the Business Intelligence Group "Best New Product of the Year" BIG Award in the startup category when the 2019 award winners were announced on November 6th 2019. The BIG Awards recognize the innovative products, people, and technologies leading their respective industries. Shelf was selected from a pool of 1000's of applicants, thanks to its unique ability to transform how employees access critical company knowledge. According to Maria Jimenez, chief nominations officer of the Business Intelligence Group, "We assessed numerous enterprise technology products in our search for the Best New Product and found Shelf capabilities to be truly unique. The product itself is intuitive and easy to use, and the ROI Shelf produces for its customers is undeniable. As a result, we felt as though Shelf was in a class of its own and deserving of this recognition."

Almost every business leader I know has begun to -- or is pondering the best way to -- incorporate artificial intelligence (AI) into their 2020 growth plan. Clearly, AI is sweeping the globe and "helping to drive the next great economic expansion," as Deloitte noted in its 2019 international survey of AI's early adopters. Nearly two-thirds of the executives surveyed for the report said AI technology is "very" or "critically" important to the success of their businesses, and most said that AI will help them move ahead of competitors who have been slower to adopt the technologies. Large companies are all over it, using AI to win customer loyalty by creating highly personalized experiences. Can startups and other small firms use AI and advanced analytics effectively without wasting precious time and money on technologies that even the experts are still working to operationalize?

"A year spent in artificial intelligence is enough to make one believe in God" It's futile to deny it, but Artificial Intelligence(AI) is no longer the buzzword of tomorrow, it's a striking reality of today, and the enterprise landscape of AI has never looked more promising than it does today! By 2022, the global business value created by AI will touch a whopping $3.9 trillion, and spending on AI systems is expected to reach $79.2 billion1. Forecasts estimate that AI technologies will pervade every software product2 next year, and AI software revenue is expected to grow to 118.6 billion by 20253. All these are tantamount to the fact that AI is no longer just a differentiator but a core part of business functions! In India, the enterprise AI market is heading towards much wider adoption. An industry expert associates the Indian Enterprise Market for AI to be estimated to be $100 million, growing at 200-250% CAGR.

Artificial intelligence, it seems, can figure out how to do just about anything. It can simulate the Universe, learn to solve a Rubik's Cube with just one hand, and even find ghosts hidden in our past. All these kinds of advancements are meant to be for our own good. In recent times, algorithmic systems that already affect people's lives have demonstrated alarming levels of bias in their operation, doing things like predicting criminality along racial lines and determining credit limits based on gender. Against this backdrop, how can scientists ensure that advanced thinking systems can be fair, or even safe?

Artificial intelligence has been used to determine how much of the play'Henry VIII' was written by William Shakespeare and how much was penned by John Fletcher. Fletcher replaced Shakespeare as the house playwright for acting troupe The King's Men in 1616 and, while literary experts have long known Henry VIII was a collaborative work, they didn't know how much of the work was written by Fletcher. To solve the puzzle, Czech artificial intelligence researcher, Petr Plecháč, decided to train a machine-learning algorithm on the works of Shakespeare, Fletcher and other contemporary writers. He then'let it loose' on a the text of Henry VIII to see if it could determine the true authorship of each scene. Mr Plecháč says the algorithm proves that Fletcher wrote several scenes - including much of the second act.

First Law -- A robot may not injure a human being or, through inaction, allow a human being to come to harm. 2. Second Law -- A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. Ever since Isaac Asimov penned down these fictional rules governing the behavior of intelligent robots -- in 1942 -- humanity has become fixated with the idea of making intelligent machines. After British mathematician Alan Turing devised the Turing Test as a benchmark for machines to be considered sufficiently smart, the term artificial intelligence was coined in 1956 at a summer conference in Dartmouth University, USA for the first time. Prominent scientists and researchers debated the best approaches to creating AI, favoring one that begins by teaching a computer the rules governing human behavior -- using reason and logic to process available information. There was plenty of hype and excitement about AI and several countries started funding research as well.