Pub Date : 2019-07-01DOI: 10.1162/99608F92.440445CB
Radu V. Craiu
``The Data Science revolution—that sweet promise of groundbreaking truths revealed from massive information—is an elusive one. As I keep looking for Big Data, people keep telling me that they are everywhere around us. This does not help my self-esteem. And when I finally start to get the big picture, I realize that I am already out of it. Statisticians out, data scientists in. I understand that my skills are good, but also that part of my training is holding me back. I know statistics, but somehow I have too much theory in me and not enough ’just do it.’ All of a sudden, I am a Franken-data scientist.”
{"title":"The Hiring Gambit: In Search of the Twofer Data Scientist","authors":"Radu V. Craiu","doi":"10.1162/99608F92.440445CB","DOIUrl":"https://doi.org/10.1162/99608F92.440445CB","url":null,"abstract":"``The Data Science revolution—that sweet promise of groundbreaking truths revealed from massive information—is an elusive one. As I keep looking for Big Data, people keep telling me that they are everywhere around us. This does not help my self-esteem. And when I finally start to get the big picture, I realize that I am already out of it. Statisticians out, data scientists in. I understand that my skills are good, but also that part of my training is holding me back. I know statistics, but somehow I have too much theory in me and not enough ’just do it.’ All of a sudden, I am a Franken-data scientist.”","PeriodicalId":23712,"journal":{"name":"Volume 4 Issue 1","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76275325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1162/99608F92.BA20F892
Xiaomin Meng
The Data Science Major degree program combines computational and inferential reasoning to draw conclusions based on data about some aspect of the real world. Data scientists come from all walks of life, all areas of study, and all backgrounds. They share an appreciation for the practical use of mathematical and scientific thinking and the power of computing to understand and solve problems for business, research, and societal impact.
{"title":"Data Science: An Artificial Ecosystem","authors":"Xiaomin Meng","doi":"10.1162/99608F92.BA20F892","DOIUrl":"https://doi.org/10.1162/99608F92.BA20F892","url":null,"abstract":"The Data Science Major degree program combines computational and inferential reasoning to draw conclusions based on data about some aspect of the real world. Data scientists come from all walks of life, all areas of study, and all backgrounds. They share an appreciation for the practical use of mathematical and scientific thinking and the power of computing to understand and solve problems for business, research, and societal impact.","PeriodicalId":23712,"journal":{"name":"Volume 4 Issue 1","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88318661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1162/99608F92.F06C6E61
M. I. Jordan
We praise Jordan for bringing much needed clarity about the current status of Artificial Intelligence (AI)—what it currently is and what it is not—as well as explaining the current challenges lying ahead and outlining what is missing and remains to be done. Jordan makes several claims supported by a list of talking points that we hope will reach a wide audience; ideally, that audience will include academic, university, and governmental leaders, at a time where significant resources are being allocated to AI for research and education.
{"title":"Artificial Intelligence—The Revolution Hasn’t Happened Yet","authors":"M. I. Jordan","doi":"10.1162/99608F92.F06C6E61","DOIUrl":"https://doi.org/10.1162/99608F92.F06C6E61","url":null,"abstract":"We praise Jordan for bringing much needed clarity about the current status of Artificial Intelligence (AI)—what it currently is and what it is not—as well as explaining the current challenges lying ahead and outlining what is missing and remains to be done. Jordan makes several claims supported by a list of talking points that we hope will reach a wide audience; ideally, that audience will include academic, university, and governmental leaders, at a time where significant resources are being allocated to AI for research and education.","PeriodicalId":23712,"journal":{"name":"Volume 4 Issue 1","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89754984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-30DOI: 10.1162/99608f92.c698b3a7
David Donoho
{"title":"Comments on Michael Jordan’s Essay “The AI Revolution Hasn’t Happened Yet”","authors":"David Donoho","doi":"10.1162/99608f92.c698b3a7","DOIUrl":"https://doi.org/10.1162/99608f92.c698b3a7","url":null,"abstract":"","PeriodicalId":23712,"journal":{"name":"Volume 4 Issue 1","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76426770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-23DOI: 10.1162/99608F92.DDC4D18E
M. Matarić
{"title":"What Kinds of Intelligent Machines Really Make Life Better?","authors":"M. Matarić","doi":"10.1162/99608F92.DDC4D18E","DOIUrl":"https://doi.org/10.1162/99608F92.DDC4D18E","url":null,"abstract":"","PeriodicalId":23712,"journal":{"name":"Volume 4 Issue 1","volume":"99 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80271045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-23DOI: 10.1162/99608F92.644EF4A4
Nathan Sanders
The strategic role of data science teams in industry is fundamentally to help businesses to make smarter decisions. This includes decisions on minuscule scales, such as what fraction of a cent to bid on an ad placement displayed in a web browser, whose importance is only manifest when scaled by orders of magnitude through machine automation. But it also extends to singular, monumental decisions made by businesses, such as how to position a new entrant within a competitive market. In both regimes, the potential impact of data science is only realized when both humans and machine actors are learning from data and when data scientists communicate effectively to decision makers throughout the business. I examine this dynamic through the instructive lens of the duality between inference and prediction. I define these concepts, which have varied use across many fields, in practical terms for the industrial data scientist. Through a series of descriptions, illustrations, contrasting concepts, and examples from the entertainment industry (box office prediction and advertising attribution), I offer perspectives on how the concepts of inference and prediction manifest in the business setting. From a balanced perspective, prediction and inference are integral components of the process by which models are compared to data. However, through a textual analysis of research abstracts from the literature, I demonstrate that an imbalanced, prediction-oriented perspective prevails in industry and has likewise become increasingly dominant among quantitative academic disciplines. I argue that, despite these trends, data scientists in industry must not overlook the valuable, generalizable insights that can be extracted through statistical inference. I conclude by exploring the implications of this strategic choice for how data science teams are integrated in businesses.KeywordsIndustry, Entertainment, Communication, Inference, Bibliometrics
{"title":"A Balanced Perspective on Prediction and Inference for Data Science in Industry","authors":"Nathan Sanders","doi":"10.1162/99608F92.644EF4A4","DOIUrl":"https://doi.org/10.1162/99608F92.644EF4A4","url":null,"abstract":"The strategic role of data science teams in industry is fundamentally to help businesses to make smarter decisions. This includes decisions on minuscule scales, such as what fraction of a cent to bid on an ad placement displayed in a web browser, whose importance is only manifest when scaled by orders of magnitude through machine automation. But it also extends to singular, monumental decisions made by businesses, such as how to position a new entrant within a competitive market. In both regimes, the potential impact of data science is only realized when both humans and machine actors are learning from data and when data scientists communicate effectively to decision makers throughout the business. I examine this dynamic through the instructive lens of the duality between inference and prediction. I define these concepts, which have varied use across many fields, in practical terms for the industrial data scientist. Through a series of descriptions, illustrations, contrasting concepts, and examples from the entertainment industry (box office prediction and advertising attribution), I offer perspectives on how the concepts of inference and prediction manifest in the business setting. From a balanced perspective, prediction and inference are integral components of the process by which models are compared to data. However, through a textual analysis of research abstracts from the literature, I demonstrate that an imbalanced, prediction-oriented perspective prevails in industry and has likewise become increasingly dominant among quantitative academic disciplines. I argue that, despite these trends, data scientists in industry must not overlook the valuable, generalizable insights that can be extracted through statistical inference. I conclude by exploring the implications of this strategic choice for how data science teams are integrated in businesses.KeywordsIndustry, Entertainment, Communication, Inference, Bibliometrics","PeriodicalId":23712,"journal":{"name":"Volume 4 Issue 1","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83360822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-23DOI: 10.1162/99608F92.9A36BDB6
C. Borgman
The most elusive term in data science is ‘data.’ While often treated as objects to be computed upon, data is a theory-laden concept with a long history. Data exist within knowledge infrastructures that govern how they are created, managed, and interpreted. By comparing models of data life cycles, implicit assumptions about data become apparent. In linear models, data pass through stages from beginning to end of life, which suggest that data can be recreated as needed. Cyclical models, in which data flow in a virtuous circle of uses and reuses, are better suited for irreplaceable observational data that may retain value indefinitely. In astronomy, for example, observations from one generation of telescopes may become calibration and modeling data for the next generation, whether digital sky surveys or glass plates. The value and reusability of data can be enhanced through investments in knowledge infrastructures, especially digital curation and preservation. Determining what data to keep, why, how, and for how long, is the challenge of our day.Keywordsastronomy, curation, data, digital curation, life cycles, observations, preservation, reuse, science, stewardship
{"title":"The Lives and After Lives of Data","authors":"C. Borgman","doi":"10.1162/99608F92.9A36BDB6","DOIUrl":"https://doi.org/10.1162/99608F92.9A36BDB6","url":null,"abstract":"The most elusive term in data science is ‘data.’ While often treated as objects to be computed upon, data is a theory-laden concept with a long history. Data exist within knowledge infrastructures that govern how they are created, managed, and interpreted. By comparing models of data life cycles, implicit assumptions about data become apparent. In linear models, data pass through stages from beginning to end of life, which suggest that data can be recreated as needed. Cyclical models, in which data flow in a virtuous circle of uses and reuses, are better suited for irreplaceable observational data that may retain value indefinitely. In astronomy, for example, observations from one generation of telescopes may become calibration and modeling data for the next generation, whether digital sky surveys or glass plates. The value and reusability of data can be enhanced through investments in knowledge infrastructures, especially digital curation and preservation. Determining what data to keep, why, how, and for how long, is the challenge of our day.Keywordsastronomy, curation, data, digital curation, life cycles, observations, preservation, reuse, science, stewardship","PeriodicalId":23712,"journal":{"name":"Volume 4 Issue 1","volume":"266 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75773247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-23DOI: 10.1162/99608F92.88BA42CB
A. Garber
{"title":"Data Science: What the Educated Citizen Needs to Know","authors":"A. Garber","doi":"10.1162/99608F92.88BA42CB","DOIUrl":"https://doi.org/10.1162/99608F92.88BA42CB","url":null,"abstract":"","PeriodicalId":23712,"journal":{"name":"Volume 4 Issue 1","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80668930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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