Collaborating Authors

Results Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud: 9780135404676: Deitel, Paul, Deitel, Harvey: Books


This book has my strongest recommendation both as an introduction to Python as well as Data Science. A great introduction to IBM Watson and the services it provides!"

The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

MLSys 2021: Bridging the divide between machine learning and systems


Machine learning MLSys 2021: Bridging the divide between machine learning and systems Amazon distinguished scientist and conference general chair Alex Smola on what makes MLSys unique -- both thematically and culturally. Email Alex Smola, Amazon vice president and distinguished scientist The Conference on Machine Learning and Systems ( MLSys), which starts next week, is only four years old, but Amazon scientists already have a rich history of involvement with it. Amazon Scholar Michael I. Jordan is on the steering committee; vice president and distinguished scientist Inderjit Dhillon is on the board and was general chair last year; and vice president and distinguished scientist Alex Smola, who is also on the steering committee, is this year's general chair. As the deep-learning revolution spread, MLSys was founded to bridge two communities that had much to offer each other but that were often working independently: machine learning researchers and system developers. Registration for the conference is still open, with the very low fees of $25 for students and $100 for academics and professionals. "If you look at the big machine learning conferences, they mostly focus on, 'Okay, here's a cool algorithm, and here are the amazing things that it can do. And by the way, it now recognizes cats even better than before,'" Smola says. "They're conferences where people mostly show an increase in capability. At the same time, there are systems conferences, and they mostly care about file systems, databases, high availability, fault tolerance, and all of that. "Now, why do you need something in-between? Well, because quite often in machine learning, approximate is good enough. You don't necessarily need such good guarantees from your systems.

Future of AI & 5G Part 4: Driving Cleaner Economic Growth & Jobs


Governments, investors and business leaders need to adopt practical solutions that can be deployed across the world at scale. The arrival of 5G along with wider adoption of AI technology into the physical world will make it possible to substantially enhance the opportunities to scale cleaner energy generation technologies, enable efficiency gains in manufacturing, our homes, retail stores, offices and transportation that will enable substantial reductions in pollution. Policies that incentivise the accelerated development and deployment of Industry 4.0 solutions will require politicians and regulators to better understand the opportunities that 5G alongside AI will enable. The OECD published a paper "What works in Innovation Policy" and observed that "Policies ignoring or resisting the industrial transition have proven to be not just futile but result in an innovative disadvantage and weak economic performance." Entering the new year will allow us to develop and deploy solutions for the 2020s that make use of the next industrial revolution with 5G and AI to enable dramatic efficiency gains across all sectors of the economy and to enhance renewable energy generation. The emergence of India, China and others as industrial economic powers is occurring at a time when we now know the damage that such pollution causes and hence there is a need to work together, collaboratively to solve a global problem. Embracing technological change and enhancing its capabilities to deliver better living standards alongside sustainable development is the best option for those who really want to make an impact on climate change at scale in the 2020s and beyond. I wish to thank Henry Derwent, former advisor to Prime Minister Margaret Thatcher and former CEO of IETA for his efforts to promote technological innovation and scaled up financing with Green Bonds.

Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020) Artificial Intelligence

The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning Machine Learning

The prevalence of e-commerce has made detailed customers' personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over $T$ time periods with an \emph{unknown} demand function of posted price and personalized information. At each time $t$, the retailer observes an arriving customer's personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third party agent might infer the personalized information and purchase decisions from price changes from the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer's information and purchasing decisions. To this end, we first introduce a notion of \emph{anticipating} $(\varepsilon, \delta)$-differential privacy that is tailored to dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for $d$-dimensional personalized information, our algorithm achieves the expected regret at the order of $\tilde{O}(\varepsilon^{-1} \sqrt{d^3 T})$, when the customers' information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to $\tilde{O}(\sqrt{d^2T} + \varepsilon^{-2} d^2)$ Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud (9780135404676): Deitel, Paul J., Deitel, Harvey: Books


This book has my strongest recommendation both as an introduction to Python as well as Data Science. A great introduction to IBM Watson and the services it provides!"

Overcoming the 'Retail Apocalypse'


The retail industry is undergoing a sea change so massive that many industry insiders have termed it the "retail apocalypse." In a sign of this industry upheaval, in 2018 major retailers closed 5,524 stores in the U.S. and 1,432 stores in the U.K., according to figures compiled by the Coresight Research, a firm that studies the retail industry.1 In some good news for the industry, Coresight predicts that 2019 "will not be the year of retail apocalypse or even decline. Instead, it will be a year of reinvention -- for the retail sector as a whole and for physical stores in particular."2 This predicted reinvention of the industry stems in part from the use of sophisticated technology, specifically artificial intelligence.

Machine learning job: Director of Machine Learning at Walmart (San Bruno, California, United States)


Director of Machine Learning at Walmart San Bruno, California, United States (Posted Jun 9 2019) About the company The Walmart US eCommerce team is rapidly innovating to evolve and define the future state of shopping. As the world's largest retailer, we are on a mission to help people save money and live better. With the help of some of the brightest minds in merchandising, marketing, supply chain, talent and more, we are reimaging the intersection of digital and physical shopping to help achieve that mission. Job description As Director of Machine Learning Science, you will lead a highly innovative team to strategically leverage the vast amounts of data from the World's largest Omni-channel retailer to better serve the Customer. Your primary focus will be building advanced data mining techniques, spearheading statistical analysis aligned to key business goals, and architecting high quality prediction systems to integrate with our Walmart Labs products, using advance machine learning techniques.

Anomaly Detection for an E-commerce Pricing System Machine Learning

Online retailers execute a very large number of price updates when compared to brick-and-mortar stores. Even a few mis-priced items can have a significant business impact and result in a loss of customer trust. Early detection of anomalies in an automated real-time fashion is an important part of such a pricing system. In this paper, we describe unsupervised and supervised anomaly detection approaches we developed and deployed for a large-scale online pricing system at Walmart. Our system detects anomalies both in batch and real-time streaming settings, and the items flagged are reviewed and actioned based on priority and business impact. We found that having the right architecture design was critical to facilitate model performance at scale, and business impact and speed were important factors influencing model selection, parameter choice, and prioritization in a production environment for a large-scale system. We conducted analyses on the performance of various approaches on a test set using real-world retail data and fully deployed our approach into production. We found that our approach was able to detect the most important anomalies with high precision.