Over the last few years, the world has witnessed a robust upsurge in technology developments, especially in Artificial Intelligence. There is no doubt that the technology has the potential to transform businesses the way it is done earlier. Now, countries are focusing more on leveraging this tech to become and lead the race for AI supremacy across the globe. In India, there is a huge scope for AI as the country has been a growing hub for business and ranks among the most lucrative investment destinations for technology transactions worldwide. In recent times, the country has focused its interest more on technology, realising that it is a vital component of economic development.

We consider a novel application of inverse reinforcement learning which involves modeling, learning and predicting the commenting behavior of YouTube viewers. Each group of users is modeled as a rationally inattentive Bayesian agent. Our methodology integrates three key components. First, to identify distinct commenting patterns, we use deep embedded clustering to estimate framing information (essential extrinsic features) that clusters users into distinct groups. Second, we present an inverse reinforcement learning algorithm that uses Bayesian revealed preferences to test for rationality: does there exist a utility function that rationalizes the given data, and if yes, can it be used to predict future behavior? Finally, we impose behavioral economics constraints stemming from rational inattention to characterize the attention span of groups of users.The test imposes a R{\'e}nyi mutual information cost constraint which impacts how the agent can select attention strategies to maximize their expected utility. After a careful analysis of a massive YouTube dataset, our surprising result is that in most YouTube user groups, the commenting behavior is consistent with optimizing a Bayesian utility with rationally inattentive constraints. The paper also highlights how the rational inattention model can accurately predict future commenting behavior. The massive YouTube dataset and analysis used in this paper are available on GitHub and completely reproducible.

Financial fraud detection represents the challenge of finding anomalies in networks of financial transactions. In general, the anomaly detection (AD) is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles. The fraudulent behaviour in money laundering may manifest itself through unusual patterns in financial transaction networks. In such networks, nodes represents customers and the edges are transactions: a directed edge between two nodes illustrates that there is a money transfer in the respective direction, where the weight on the edge is the transferred amount. In this paper we present a survey on the fundamental anomaly detection techniques and then present briefly the relevant literature in connection with fraud detection context.

Nature provides us with abundant examples of how large numbers of individuals can make decisions without the coordination of a central authority. Social insects, birds, fishes, and many other living collectives, rely on simple interaction mechanisms to do so. They individually gather information from the environment; small bits of a much larger picture that are then shared locally among the members of the collective and processed together to output a commonly agreed choice. Throughout evolution, Nature found solutions to collective decision-making problems that are intriguing to engineers for their robustness to malfunctioning or lost individuals, their flexibility in face of dynamic environments, and their ability to scale with large numbers of members. In the last decades, whereas biologists amassed large amounts of experimental evidence, engineers took inspiration from these and other examples to design distributed algorithms that, while maintaining the same properties of their natural counterparts, come with guarantees on their performance in the form of predictive mathematical models. In this paper, we review the fundamental processes that lead to a collective decision. We discuss examples of collective decisions in biological systems and show how similar processes can be engineered to design artificial ones. During this journey, we review a framework to design distributed decision-making algorithms that are modular, can be instantiated and extended in different ways, and are supported by a suit of predictive mathematical models.

Artificial Intelligence (AI) and Machine Learning (ML) are poised to help companies make dramatic shifts in performance, shareholder value and business development over the next two years. AI opens the door to analyze massive amounts of data and deliver critical insights that organizations across a wide variety of industries can use to improve processes, drive profitability, and increase their competitive advantage. To better understand how this game-changing technology is being applied to drive business success today and where it is headed in the near future, Protiviti and ESI ThoughtLab interviewed more than 300 executives around the globe, reaching across functions, company size and industry, including healthcare, technology, financial services and consumer products. Results from this important research are included in this report, including how businesses are investing in AI, where the benefits of AI/ML are being seen now and will be seen in the immediate future, challenges and perceptions regarding AI talent, and obstacles that executive leadership will face. The clear picture gleaned from the research is this: Companies who are leading the charge with advanced AI are finding that it is a real game changer, while companies who are still lagging behind will soon experience a competitive disadvantage.

In previous articles, I've looked at a variety of video games that have proven useful test-beds for AI research, with the likes of Ms. Pac-Man, Super Mario Bros. and more recently StarCraft. But in this instance I want to look at a genre that is still relatively new whilst presenting exciting opportunities for AI research: Multiplayer Online Battle Arena's (MOBA). The MOBA genre is undoubtedly one of the most popular in gaming today, but what impact could this have upon AI research? I'm going to provide an overview of MOBA's as a genre, what aspects of their design can prove interesting to AI research and look at some projects that are now bearing fruit both in academia and in corporate research labs. Multiplayer Online Battle Arena's are an offshoot of Real-time Strategy (RTS) games, originating with the Aeon of Strife map for Blizzards StarCraft, followed by the'Defence of the Ancients' mod for WarCraft III: Reign of Chaos and its expansion The Frozen Throne.

Machine learning is an ever-evolving field, so it can be easy to feel like you're out of the loop on the latest developments changing the world this week. One of those emerging areas that have been getting a lot of buzz lately is GANs--or generative adversarial networks. So to keep you in the machine learning loop, we've put together a short crash course on GANs: With generative models, the aim is to model the distribution of a given dataset. For the generative models that we're talking about today, that dataset is usually a set of images, but it could also be other kinds of data, like audio samples or time-series data. There are two ways to go about getting a model of this distribution: implicitly or explicitly.

As if there weren't enough security challenges, a forecast for the coming year warns the brave new world of AI and machine learning brings with it a whole new set of threats as microservices, the Internet of Things and multi-cloud deployments expand attack surfaces for hackers constantly probing for weaknesses. Market analyst Gartner's list of key technology trends for 2020 includes a compelling list of emerging trends, ranging from "hyper-automation" to the "democratization of expertise." Still, these emerging capabilities come with a price: As AI and machine learning add another layer of technological complexity, "Security generalists cannot address the wide spectrum of risks efficiently in the current risk landscape," Gartner has warned. The connection of platforms, edge devices and users expecting instant access to data--structured and unstructured--has raised the stakes for security as AI and machine learning enter the enterprise mainstream. "Security and risk leaders should focus on three key areas -- protecting AI-powered systems, leveraging AI to enhance security defense and anticipating nefarious use of AI by attackers," the market analyst warned in its rankings of AI-centric emerging technologies.

With the industry 4.0 revolution around, Research N Reports presents a detailed analysis of Cognitive Computing market that offers latest insights for business professionals. Using BI tools such as Factiva and Hoover, the report offers a comprehensive analysis and is a mix of market intelligence studies and industry insights. Prepared by a panel of highly experienced market analysts and consultants, the report is spread across 137 pages offering chapter wise detailed market analysis that enables the clients with multiple data points and encourages them to have a 360 degree overview of the market performance. Clients can ask for sample of this report that gives a detailed overview of the market conditions, driving and restraining factors, segments, trends and opportunities. Covering the latest information about the market, the samples can give a basic understanding upon the report contents and its format.

In this presentation, Kathrin Melcher, who works as a data scientist at KNIME, will give an overview of KNIME Software, including the open-source tool KNIME Analytics Platform for creating data science applications and services and also the different deployment options you have when using KNIME Server. While the structure is often similar--data collection, data transformation, model training, deployment--each project required its own special trick, whether this was a change in perspective or a particular technique to deal with the special case and business questions. You'll learn about demand prediction in energy, anomaly detection in IoT, risk assessment in finance, the most common applications in customer intelligence, social media analysis, topic detection, sentiment analysis, fraud detection, bots, recommendation engines, and more. Join us to learn what's possible in data science. She holds a Master's Degree in Mathematics from the University of Konstanz, Germany.