Accenture is a global professional services company with leading capabilities in digital, cloud and security. They are known for delivering unmatched experience and specialized skills across more than 40 industries, through their Strategy and Consulting, Interactive, Technology and Operations services--all powered by the world's largest network of Advanced Technology and Intelligent Operations centers. Their 506,000 people deliver on the promise of technology and human ingenuity every day, and serve clients in more than 120 countries. Accenture embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Laetitia Cailleteau is the Managing Director, UKI Emerging Technology and Global Lead for Conversational AI for Accenture.

Every week, we talk about important data and analytics topics with data science leaders from around the world on Facebook Live. You can subscribe to the DataTalk podcast on iTunes, Google Play, Stitcher, SoundCloud and Spotify. This data science video and podcast series is part of Experian's effort to help people understand how data-powered decisions can help organizations develop innovative solutions and drive more business. To keep up with upcoming events, join our Data Science Community on Facebook or check out the archive of recent data science videos. To suggest future data science topics or guests, please contact Mike Delgado. In this week's #DataTalk, we talked with Dr. Anima Anandkumar, Principal Scientist at Amazon AI and Bren Professor at Caltech, about what data scientists need to know about deep learning and how to scale deep learning frameworks. Today we're excited to talk about deep learning with Dr. Anima Anandkumar. Anima serves as the principal scientist at Amazon Web Services. Anima earned her BTech in electrical engineering from the Indian Institute of Technology. She also earned her Ph.D. in electrical engineering from Cornell University, and then after that she served as a postdoctoral researcher at MIT. She's the recipient of dozens of awards.

You reach out to the elite. You try and learn from the best of the best. The data science experts who have scaled the hackathon ladder and tasted success first hand. In short, you learn from the Grandmasters themselves. We are thrilled to present the new "Kaggle Grandmaster Series" where we interview top Kagglers from around the globe to bring their thoughts, insights, and experience in front of the Analytics Vidhya community.

The runaway train of machine (and deep) learning has, in my opinion, reached an alarming level — alarming in the most negative sense. It is one thing to be excited about a useful technology (say, one…

Craig Smith is a former New York Occasions correspondent and host of the podcast, Eye on AI. The continuing Covid-19 pandemic, which has disrupted classroom instruction all over the world, presents an historic alternative to democratize schooling with artificial intelligence. Since Socrates taught Plato and Plato taught Aristotle -- or Confucius taught Yan Hui -- man has identified that one of the best schooling is delivered one-to-one by an skilled educator. The result's the imperfect classroom-based instruction that we stay with in the present day. But, new types of AI, based mostly on deep neural networks, can now uncover patterns about how college students carry out and assist lecturers optimize their methods accordingly.

Arti Zeighami's interest in artificial intelligence started when he read science fiction as a teen. Yet as head of advanced analytics and AI for global retailer H&M Group, his leadership style focuses on reality: first building a business case and a proof of concept, and then undergoing an agile process of iteration and scaling, failure and success, measurement and improvement. Your reviews are essential to the success of Me, Myself, and AI. For a limited time, we're offering a free download of MIT SMR's best articles on artificial intelligence to listeners who review the show. Send a screenshot of your review to smrfeedback@mit.edu to receive the download. Arti Zeighami is a senior executive and a business leader at H&M Group. As chief data and analytics officer, he is responsible for all AI, analytics, and data capabilities across all of the company's brands. He has coined the term amplified intelligence -- where humans and machines work together -- and in this episode shares stories and practical tips on how teams can get started and scale successfully. Read more about our show and follow along with the series.

This paper presents a focused analysis of human studies in explainable artificial intelligence (XAI) entailing qualitative investigation. We draw on the social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous. We contextualize the presentation of the XAI contributions included in our analysis according to the components of rigor described in the qualitative research literature: 1) underlying theories or frameworks, 2) methodological approaches, 3) data collection methods, and 4) data analysis processes. The results of our analysis support calls from others in the XAI community advocating for collaboration with experts from social disciplines to bolster rigor and effectiveness in human studies.

The term AI Winter, first appeared in 1984 having been discussed at the American Association of Artificial Intelligence. This discussion then saw a rise in pessimism and a reduction of funding. Many minds that had survived the first'winter', prior to it being cited as such, suggested that an increase in enthusiasm for AI, perhaps without the technical capabilities to match, has seen a sharp rise and later collapse. Having hit extreme lows in the early 1990s, the enthusiasm for AI began to rise again, and as they say, the rest is now history with it now so ubiquitous in society. That said, we have seen this year that anything is certainly possible, therefore, we thought we would ask our community of AI expert friends what they thought on the topic, asking - 'Do you think we will see another AI Winter?

Managing Marketing is a weekly podcast hosted by TrinityP3. Each one is a conversation with a marketing thought-leader, professional, practitioner and experts on the issues and topics of interest to marketers and business leaders everywhere. In this special series, TrinityP3's Anton Buchner discusses the rise of Artificial Intelligence and the impact it is having on marketing. Alastair Herbert is the founder of the research consultancy Linguabrand. He shares his wisdom having developed a deep-listening robot (Bob), that analyses visual and verbal language. Alastair introduces you to how Bob listens and analyses the psychology of language that humans potentially miss in data analysis and research groups. Bob can uncover insights to help brands shift the conversation away from sounding generic, to position themselves more persuasively. Follow Managing Marketing on Soundcloud, TuneIn, Stitcher, Spotify and Apple Podcast. Welcome to Managing Marketing, a weekly podcast where we sit down and talk with thought leaders and experts on the issues and opportunities in the marketing and business world. And it's quite warm here, so windows are open, so if you hear barking dogs, police cars, or squawking birds, you all know the reason why. It's nothing to do with COVID, it's actually just to do with enjoying summer. Now I'm really excited to have a chat with you today. As in most communications, I think most people realise that the vast majority of it is actually subconscious. And hopefully, by the end of this session, your listeners will have a much better understanding of how communications work. I'm sure they'll be excited. Before we jump in, I met you relatively recently through a colleague, Jeremy Taylor-Riley. He's now a business colleague of yours, I believe. Well, we actually go back to school days together. And what was great is that we – I think this was back when dinosaurs ruled the earth.

Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federated learning in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. We propose a novel coded computing framework, CodedFedL, that injects structured coding redundancy into federated learning for mitigating stragglers and speeding up the training procedure. CodedFedL enables coded computing for non-linear federated learning by efficiently exploiting distributed kernel embedding via random Fourier features that transforms the training task into computationally favourable distributed linear regression. Furthermore, clients generate local parity datasets by coding over their local datasets, while the server combines them to obtain the global parity dataset. Gradient from the global parity dataset compensates for straggling gradients during training, and thereby speeds up convergence. For minimizing the epoch deadline time at the MEC server, we provide a tractable approach for finding the amount of coding redundancy and the number of local data points that a client processes during training, by exploiting the statistical properties of compute as well as communication delays. We also characterize the leakage in data privacy when clients share their local parity datasets with the server. We analyze the convergence rate and iteration complexity of CodedFedL under simplifying assumptions, by treating CodedFedL as a stochastic gradient descent algorithm. Furthermore, we conduct numerical experiments using practical network parameters and benchmark datasets, where CodedFedL speeds up the overall training time by up to $15\times$ in comparison to the benchmark schemes.