We bandy about the term "artificial intelligence," evoking ideas of creative machines anticipating our every whim, though the reality is more banal: "For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations." This is from Michael I. Jordan, one of the foremost authorities on AI and machine learning, who wants us to get real about AI. "People are getting confused about the meaning of AI in discussions of technology trends--that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans. We don't have that, but people are talking as if we do," he noted in the IEEE Spectrum article. Instead, he wrote in an article for Harvard Data Science Review, we should be talking about ML and its possibilities to augment, not replace, human cognition. Jordan calls this "Intelligence Augmentation," and uses examples like search engines to showcase the possibilities for assisting humans with creative thought.

Confucius once said, "Fish forget they live in water; people forget they live in the Tao" (Lin, 2007). Analogously, it may be easy for data scientists to forget they live in a world defined and permeated by mathematics. The two pieces, "Ten Research Challenge Areas in Data Science" by Jeannette M. Wing and "Challenges and Opportunities in Statistics and Data Science: Ten Research Areas" by Xuming He and Xihong Lin, provide an impressively complete list of data science challenges from luminaries in the field of data science. They have done an extraordinary job, so this response offers a complementary viewpoint from a mathematical perspective and evangelizes advanced mathematics as a key tool for meeting the challenges they have laid out. Notably, we pick up the themes of scientific understanding of machine learning and deep learning, computational considerations such as cloud computing and scalability, balancing computational and statistical considerations, and inference with limited data.

It is thought-provoking to read the pair of articles on 10 challenges in data science by Xuming He and Xihong Lin from a statistics perspective and Jeannette Wing from a computer science perspective. Unsurprisingly, there is a good overlap of important topics including multimodal and heterogenous data, data privacy, fairness and interpretability, and causal inference or reasoning. This overlap reflects and confirms the foundational and shared roles of statistics and computer science in data science, which is the merging of statistical and computing thinking in the context of solving domain problems. The challenges in both articles are presented as separate, not integrated, topics, and mostly decoupled from domain problems, possibly because of the mandate of "10 challenges." In my mind, the most exciting 10 challenges in data science are to solve 10 pressing real-world data problems with positive impacts. For example, how is data science going to help control covid-19 spread while allowing a healthy economy?

To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue. Since data science is broad, with methods drawing from computer science, statistics, and other disciplines, and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning science, technology, and society. We preface our enumeration with meta-questions about whether data science is a discipline. We then describe each of the 10 challenge areas. The goal of this article is to start a discussion on what could constitute a basis for a research agenda in data science, while recognizing that the field of data science is still evolving. Although data science builds on knowledge from computer science, engineering, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: fundamental scientific questions and pressing problems of societal importance.

Data science has made key contributions in the battle against COVID-19, from tracking cases and deaths to understanding how populations move during travel restrictions to vaccine design. The Harvard Data Science Initiative is working to support faculty members, students, and fellows in designing and applying the tools of statistics and computer science and creating a community to foster the flow of ideas. The year-old Harvard Data Science Review published a special issue online this summer dedicated to COVID-19 that will be updated with the latest findings, with a goal of fostering innovation and keeping the conversation going about how data science can help meet the COVID-19 challenge. The Gazette spoke with Francesca Dominici, Clarence James Gamble Professor of Biostatistics, Population and Data Science at the Harvard T.H. Chan School of Public Health and co-director of the initiative, and Xiao-Li Meng, the review's editor in chief and the Whipple V.N. Jones Professor of Statistics in the Faculty of Arts and Sciences, about how data science can be used to meet today's challenges, and in turn, challenges facing the field. GAZETTE: How is data science important to our understanding of and response to COVID-19? DOMINICI: Data science is on the front page of The New York Times probably every single day.

Over the past decade, many organizations have come to recognize that their future success will depend on data and AI (artificial intelligence) capabilities. Expectations are high and companies are heavily investing in the area. However, our experience advising organizations in diverse industries suggests that many have also become disillusioned in their journey to create companywide, data-driven business transformation. This article discusses some of the common pitfalls in the implementation of data and AI strategies and gives recommendations for business leaders on how to successfully include data and AI in their business processes. These recommendations address the core enablers for data and AI capabilities, from setting the ambition level to hiring the right talent and defining the AI organization and operating model. Many companies are currently investing in data and artificial intelligence (AI). Since the terminology varies, the activities may be called AI, advanced analytics, data science, or machine learning, but the goals are the same: to increase revenues and efficiency in current business and to develop new data-enabled offerings. In addition, many companies see an increasing responsibility to contribute their AI expertise toward humanitarian and social matters. It is well understood that to stay competitive in the digital economy, the company's internal processes and products need to be smart--and smartness comes from data and AI. Over the past 4 years, our company DAIN Studios has been involved in more than 40 Data and AI initiatives in different companies and industries in Finland, Germany, Austria, Switzerland, and the Netherlands. Our clients are typically large, publicly listed companies.

Sensors increasingly permeate our lives and generate a plethora of data, which has transformed the way we live in cities. Planners have been using data-science to improve our understanding of urban issues. While other domains have highlighted concerns with big data collection, aggregation, and analytical methods to understand different phenomena, urban planning has an additional aspiration: not only to understand, but to transform society through planning. Thus, on top of critically approaching data collection and analytical methods, for the emergent field of urban science to become a distinctively unique body of knowledge, it must examine the ontological and epistemological boundaries of the big data paradigm and how it affects urban decision-making processes and their short- and long-term consequences in cities. Data-driven approaches have transformed the way we analyze, design and make policy decisions in cities. This has been true during the COVID-19 pandemic, where countries have used self-reported information and tracing apps to map infected people. South Korea Corona Map, for example provides the addresses of all infected residents, and Singapore COVID19 maps each case and their social networks, to help other people identify if they had contact with an infected person, took the same flight or used the same urban facilities to be aware of their risk of contagion.

What great data analysts do--and why every organization needs them.

There is increasing use of algorithms in the health care and criminal justice systems, and corresponding increased concern with their ethical use. But perhaps a more basic issue is whether we should believe what we hear about them and what the algorithm tells us. It is illuminating to distinguish between the trustworthiness of claims made about an algorithm, and those made by an algorithm, which reveals the potential contribution of statistical science to both evaluation and'intelligent transparency.' In particular, a four-phase evaluation structure is proposed, parallel to that adopted for pharmaceuticals. When on holiday in Portugal last year, we came to rely on'Mrs.

Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists, and venture capitalists alike. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying use of the phrase. However, this is not the classical case of the public not understanding the scientists--here the scientists are often as befuddled as the public. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us, enthralling us and frightening us in equal measure. There is a different narrative that one can tell about the current era.