He and his team have played a key part in weaving ML into the very fabric of Workday's underlying platform, which is critical to delivering compelling experiences and outcomes without customers even needing to realize it is there. Earlier in his career, while at a number of Silicon Valley companies, he played a part in making the technology we rely on everyday--GPS, and wifi, for example--so ubiquitous that most of us take these revolutionary technologies for granted. Chakraborty also co-founded and served as chief operating officer at GridCraft, a company that developed simple-to-use data analytics tools that Workday acquired in 2015. Now, as senior vice president of tools and technology at Workday, Chakraborty is responsible for the infrastructure on which our applications are built. In particular, he's leading the charge to make sure that machine learning helps customers make faster, better decisions using all of Workday's products.

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.

He and his team have played a key part in weaving ML into the very fabric of Workday's underlying platform, which is critical to delivering compelling experiences and outcomes without customers even needing to realize it is there. Earlier in his career, while at a number of Silicon Valley companies, he played a part in making the technology we rely on everyday--GPS, and wifi, for example--so ubiquitous that most of us take these revolutionary technologies for granted. Chakraborty also co-founded and served as chief operating officer at GridCraft, a company that developed simple-to-use data analytics tools that Workday acquired in 2015. Now, as senior vice president of tools and technology at Workday, Chakraborty is responsible for the infrastructure on which our applications are built. In particular, he's leading the charge to make sure that machine learning helps customers make faster, better decisions using all of Workday's products.

In today's so-called smart era when everything is getting virtual, the implementation of Artificial Intelligence in e-Commerce is a remarkable movement towards progress. By adopting the AI technology, e-commerce businesses are creating a boom in the market. Artificial Intelligence is somewhat a challenge to the creative power of a human being. It aims to achieve something which at times becomes tedious for human employees. With the right coding, it learns faster and better than compared to humans. The absence of emotional issues and health-related restrictions allow it to think logically and perform better. It has the power to detect any fraudulent activity which might be overlooked by humans. For a strong understanding of the behavior of human users and to provide the customers with a satisfactory experience, the e-commerce companies are reportedly adopting the new AI technology. Artificial Intelligence in eCommerce has now emerged as an individual business solution and is dominating the market. Now the question lies in the part that how are the business companies improvising this unique and innovative technology? Many a time, the sales team fails to keep track of the leading products and services in the market and hence could not impress potential buyers who might have an interest in the item.

They say it is ushering in a new age, a Customer Experience 3.0. As a notorious heckler, I thought I would have my team research the market, as it stands, in the closing weeks of 2018. My question was: Who cares about promises; I am exhausted by all this thought leadership; I want specific examples; what are AI-driven solutions capable of achieving for customer experience, right now? So that is exactly what they did. Then we discussed how these solutions change or do not change the game.

Decentralized Online Learning (online learning in decentralized networks) attracts more and more attention, since it is believed that Decentralized Online Learning can help the data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers. Typically, the cooperation is achieved by letting the data providers exchange their models between neighbors, e.g., recommendation model. However, the best regret bound for a decentralized online learning algorithm is $\Ocal{n\sqrt{T}}$, where $n$ is the number of nodes (or users) and $T$ is the number of iterations. This is clearly insignificant since this bound can be achieved \emph{without} any communication in the networks. This reminds us to ask a fundamental question: \emph{Can people really get benefit from the decentralized online learning by exchanging information?} In this paper, we studied when and why the communication can help the decentralized online learning to reduce the regret. Specifically, each loss function is characterized by two components: the adversarial component and the stochastic component. Under this characterization, we show that decentralized online gradient (DOG) enjoys a regret bound $\Ocal{n\sqrt{T}G + \sqrt{nT}\sigma}$, where $G$ measures the magnitude of the adversarial component in the private data (or equivalently the local loss function) and $\sigma$ measures the randomness within the private data. This regret suggests that people can get benefits from the randomness in the private data by exchanging private information. Another important contribution of this paper is to consider the dynamic regret -- a more practical regret to track users' interest dynamics. Empirical studies are also conducted to validate our analysis.

We present a predictor-corrector framework, called PicCoLO, that can transform a first-order model-free reinforcement or imitation learning algorithm into a new hybrid method that leverages predictive models to accelerate policy learning. The new "PicCoLOed" algorithm optimizes a policy by recursively repeating two steps: In the Prediction Step, the learner uses a model to predict the unseen future gradient and then applies the predicted estimate to update the policy; in the Correction Step, the learner runs the updated policy in the environment, receives the true gradient, and then corrects the policy using the gradient error. Unlike previous algorithms, PicCoLO corrects for the mistakes of using imperfect predicted gradients and hence does not suffer from model bias. The development of PicCoLO is made possible by a novel reduction from predictable online learning to adversarial online learning, which provides a systematic way to modify existing first-order algorithms to achieve the optimal regret with respect to predictable information. We show, in both theory and simulation, that the convergence rate of several first-order model-free algorithms can be improved by PicCoLO.

In machine learning, the notion of multi-armed bandits refers to a class of online learning problems, in which an agent is supposed to simultaneously explore and exploit a given set of choice alternatives in the course of a sequential decision process. In the standard setting, the agent learns from stochastic feedback in the form of real-valued rewards. In many applications, however, numerical reward signals are not readily available -- instead, only weaker information is provided, in particular relative preferences in the form of qualitative comparisons between pairs of alternatives. This observation has motivated the study of variants of the multi-armed bandit problem, in which more general representations are used both for the type of feedback to learn from and the target of prediction. The aim of this paper is to provide a survey of the state of the art in this field, referred to as preference-based multi-armed bandits or dueling bandits. To this end, we provide an overview of problems that have been considered in the literature as well as methods for tackling them. Our taxonomy is mainly based on the assumptions made by these methods about the data-generating process and, related to this, the properties of the preference-based feedback.

The use of AI at work has grown tremendously. According to Deloitte's 2018 Global Human Capital Trends survey, 42 percent companies believe that AI will be widely deployed in their organizations within three to five years. In the world of HR technology, AI is reshaping employee experience, enabling the selection of the right candidates, enhancing employee productivity, easing and simplifying tedious HR processes. There are two kinds of application in the HR context: 1) Conversational analytics using tools like Chatbots and 2) Machine learning using pattern analysis on data. While there are a number of applications that AI-based technology helps support, there are other emerging areas that this technology can help with.

Artificial intelligence has been a trending topic for quite some time, which comes as no surprise as the use of it is on the rise in every industry. According to a report by Salesforce, 51% of marketing leaders state that they currently use AI in some scope, with 27% planning to start using it in the next two years. As it continues to grow, it will progressively impact how our society functions and transform the way we work as marketers. As with any transformative technology, AI has marketing professionals on their toes; leaving them to wonder where the fate of their jobs lies in the era of AI. So, should you anticipate a massive disruption of AI threatening the security of your job?