The field of learning has evolved drastically over the years. With the advent of e-learning and learning management systems, the process of learning has gone beyond the traditional model of classroom training. Now it is possible for instructors and teachers to reach a wider, international audience through online courses hosted on cloud based LMS platforms. Students can access these courses from any place in the world at any time, by simply logging into their account using their login credentials. Although e-learning is a complete and self-sustainable medium for imparting knowledge, it also works well in conjunction with traditional classroom training.

We live in a connected world and generate a vast amount of connected data. Social networks, financial transaction systems, biological networks, transportation systems and a telecommunication nexus are all examples. The paper citation network displayed in Figure 1 is another example of connected data. Representing connected data is possible using a graph data structure regularly used in Computer Science. In this article, we will provide an introduction to the assorted types of connected data, what they represent, and the challenges we can solve.

Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.

The latest market analysis report on the Deep Learning market performs industry diagnostic as a way to accumulate valuable data into the business environment of the Deep Learning market for the forecast period 2019 – 2026. The subject matter experts behind the research have collected vital statistics on the market share, size and growth as a way to help stakeholders, business owners and field marketing personnel identify the areas to reduce costs, improve sales, explore new opportunities and streamline their processes. Unbiased perspective on intangible aspects such as key challenges, threats, new entrants as well as strengths and weaknesses of the prominent vendors too are discussed in this market intelligence report. By offering expert assistance, it would be able to assist humans in extending their capabilities. Organizations are using deep learning networks to get valuable insights from huge amount of data.

Many kinds of data are naturally amenable to being treated as sequences. An example is text data, where a text may be seen as a sequence of words. Another example is clickstream data, where a data instance is a sequence of clicks made by a visitor to a website. This is also common for data originating in the domains of speech processing and computational biology. Using such data with statistical learning techniques can often prove to be cumbersome since most of them only allow fixed-length feature vectors as input. In casting the data to fixed-length feature vectors to suit these techniques, we lose the convenience, and possibly information, a good sequence-based representation can offer. The framework of rational kernels partly addresses this problem by providing an elegant representation for sequences, for algorithms that use kernel functions. In this report, we take a comprehensive look at this framework, its various extensions and applications. We start with an overview of the core ideas, where we look at the characterization of rational kernels, and then extend our discussion to extensions, applications and use at scale. Rational kernels represent a family of kernels, and thus, learning an appropriate rational kernel instead of picking one, suggests a convenient way to use them; we explore this idea in our concluding section. Rational kernels are not as popular as the many other learning techniques in use today; however, we hope that this summary effectively shows that not only is their theory well-developed, but also that various practical aspects have been carefully studied over time.

Industry has always been in the pursuit of becoming more economically efficient and the current focus has been to reduce human labour using modern technologies. Even with cutting edge technologies, which range from packaging robots to AI for fault detection, there is still some ambiguity on the aims of some new systems, namely, whether they are automated or autonomous. In this paper we indicate the distinctions between automated and autonomous system as well as review the current literature and identify the core challenges for creating learning mechanisms of autonomous agents. We discuss using different types of extended realities, such as digital twins, to train reinforcement learning agents to learn specific tasks through generalization. Once generalization is achieved, we discuss how these can be used to develop self-learning agents. We then introduce self-play scenarios and how they can be used to teach self-learning agents through a supportive environment which focuses on how the agents can adapt to different real-world environments.

Alphabet/Google is now the most innovative company in the world according to Boston Consulting Group (BCG), unseating Apple's 13-year dominance of their annual rankings. These and many other insights are from the Boston Consulting Group's 13th annual report defining the world's most innovative companies in 2019. The Most Innovative Companies 2019: The Rise of AI, Platforms, and Ecosystems is a fascinating glimpse into the rising importance of artificial intelligence (AI) and of platforms that support innovation. What makes this survey noteworthy is how it captures how AI's use is rapidly expanding and how enterprises are relying on platforms to scale their efforts in this area. BCG is providing an Interactive Guide that compares the 50 most innovative companies in the world, sortable by industry, company and year.

By Howard Solomon A new report highlights a growing fear among Canadians that's tied to the rapid advancement of artificial intelligence. An online study conducted by Environics Research Group revealed that 77 per cent of Canadians are concerned that AI is advancing too quickly to properly understand its potential risks. The survey of 1,200 Canadians was sponsored by TD Bank back in May, and also indicated a growing concern around biases in how the technology is developed. Additionally, sixty per cent of Canadians worry about a lack of diversity in the growing field of AI. The results don't shock Tomi Poutanen, chief AI officer for TD and co-founder of Layer 6, but he said they do signal a growing awareness of AI's transformative capabilities, and people are looking to banks to validate its adoption.

KLM Royal Dutch Airlines is the world's oldest international airline still operating under its original name. On its 100th anniversary, FTE spoke to Daan Debie, Director Engineering & Architecture, KLM Royal Dutch Airlines, who outlined how the airline has embraced innovation through its "pioneering and entrepreneurial spirit". Indeed, KLM's vigorous digital transformation strategy is largely due to recognising and leveraging the advantages of modern technology. Debie, who will speak in the Premium Conference at FTE-APEX Asia EXPO 2019 (12-13 November, Singapore), explains: "Digital transformation does not just mean replacing paper with apps. For us it means getting the right information to the right people at the right time to enable well-informed decision-making in an increasingly complex environment, supported by digital tooling. "Key to this is to be truly data-driven, working from a single-source-of-truth and applying cutting-edge technology and algorithms to make sense of the complex operations." KLM is currently investing heavily in building automated decision-making tools to improve operations. In June last year, the airline embarked on a unique partnership with Boston Consulting Group (BCG) which has the potential to "revolutionise global airline operations". The project is a result of a close collaboration between KLM Operations Decision Support and Operations frontline teams, BCG's consulting team, and members of BCG Gamma, an artificial intelligence and advanced analytics entity of data scientists, data engineers and software developers, who have developed a solution based on artificial intelligence, machine learning, and advanced optimisation that addresses all elements of the airline operations, while having a positive impact on customer experience and operating costs. With these tools, KLM and other airlines will be able to tackle the most complex decisions pertaining to fleet, crew, ground services and network, with a focus on breaking down the typical silos across these departments. Earlier this year, Brazilian low-cost carrier GOL became the first airline customer of the KLM-BCG joint venture which will help GOL deliver better on-time performance to its customers while maintaining low costs. As Director Engineering & Architecture for the Department of Operations Decision Support (ODS) at KLM, Debie is responsible for creating and maintaining a cohesive overall architecture and technological vision for the products and platforms developed at ODS, but also for other clients within the partnership between KLM and BCG. "I help teams within ODS and BCG/KLM teams at Partnership clients to build their products in accordance with the architectural vision," he explains. "Additionally, I'm responsible for ensuring that we maintain high engineering standards in our development efforts.

Take advantage of our discount rooms at the conference hotel. We are beginning to announce speakers for 2020. Want to join us as a speaker? Check out our proposals page. Jesse Anderson is a data engineer, creative engineer, and managing director of the Big Data Institute. He works with companies ranging from startups to Fortune 100 companies on Big Data. This includes training on cutting edge technologies like Apache Kafka, Apache Hadoop and Apache Spark. He has taught over 30,000 people the skills to become data engineers.