“If we march into that village and we start trying to persecute people for using poison, something that's very illegal, nobody's going to talk to us. We're not going to find out where the poison came from. We're not going to be able to shut anything down. We should take the approach that people are using poison because they're desperate, because they see no other alternative.” – Andrew Stein Andrew Stein is a wildlife ecologist who spent the past 25 years studying human carnivore conflict from African wild dogs and lions in Kenya and Botswana to leopards and hyenas in Namibia. His work has long focused on finding ways for people and predators to coexist. He is the founder of CLAWS , an organization based in Botswana that's working at the intersection of cutting-edge wildlife research and community driven conservation. Since its start in 2014 and official launch as an NGO in 2020, CLAWS has been pioneering science-based, tech-forward strategies to reduce conflict between people and carnivores. By collaborating closely with local communities, especially traditional cattle herders, CLAWS supports both species conservation and rural livelihoods—making coexistence not just possible, but sustainable.…
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Why It's Hard to Design Fair Machine Learning Models
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In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification, classification parity, and calibration) has limitations, and their paper is a must-read tour through recent research in designing fair algorithms. We talked about their key findings, and, most importantly, I pressed them to list a few best practices that analysts and industrial data scientists might want to consider.
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In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification, classification parity, and calibration) has limitations, and their paper is a must-read tour through recent research in designing fair algorithms. We talked about their key findings, and, most importantly, I pressed them to list a few best practices that analysts and industrial data scientists might want to consider.
…
continue reading
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In this episode of the Data Show, I speak with Peter Bailis, founder and CEO of Sisu, a startup that is using machine learning to improve operational analytics. Bailis is also an assistant professor of computer science at Stanford University, where he conducts research into data-intensive systems and where he is co-founder of the DAWN Lab.…
In this episode of the Data Show, I speak with Arun Kejariwal of Facebook and Ira Cohen of Anodot (full disclosure: I’m an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, where we discussed time series data, and, specifically, anomaly detection and forecasting. Both Kejariwal (at Machine Zone, Twitter, and Facebook) and Cohen (at HP and Anodot) have extensive experience building analytic and machine learning solutions at large scale, and both have worked extensively with time-series data. The growing interest in AI and machine learning has not been confined to computer vision, speech technologies, or text. In the enterprise, there is strong interest in using similar automation tools for temporal data and time series.…
In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.…
In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.…
In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services. Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.…
In this interview, Tim Craig and fellow Googler Gustavo Franco, a site reliability engineer (SRE), discuss the wide range of events that qualify as “incidents;” the need for a conscious, robust, and well-defined process for understanding them; the role of training; and how to get buy-in from management so you can spread incident response training throughout an organization.…
In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses. We had a great conversation spanning many topics, including: How data science can be more useful The importance of the human side of data The leadership talent shortage in data science Is data science a bubble?…
In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”…
In this week's episode of the Data Show, we're featuring an interview Data Show host Ben Lorica participated in for the Software Engineering Daily Podcast, where he was interviewed by Jeff Meyerson. Their conversation mainly centered around data engineering, data architecture and infrastructure, and machine learning (ML).…
In this episode of the Data Show, I spoke with Nick Pentreath, principal engineer at IBM. Pentreath was an early and avid user of Apache Spark, and he subsequently became a Spark committer and PMC member. Most recently his focus has been on machine learning, particularly deep learning, and he is part of a group within IBM focused on building open source tools that enable end-to-end machine learning pipelines.…
At Google’s 2019 Cloud Next conference, I sat down with Stephen Thorne, site reliability engineer on Google’s customer reliability engineering team and co-author of "The Site Reliability Workbook," to talk about how organizations, both large and small, can use SRE to reduce operational costs, improve reliability, and create productive cross-functional teams.…
In this episode of the Data Show, I spoke with Dhruba Borthakur (co-founder and CTO) and Shruti Bhat (SVP of Marketing) of Rockset, a startup focused on building solutions for interactive data science and live applications. Borthakur was the founding engineer of HDFS and creator of RocksDB, while Bhat is an experienced product and marketing executive focused on enterprise software and data products. Their new startup is focused on a few trends I’ve recently been thinking about, including the re-emergence of real-time analytics, and the hunger for simpler data architectures and tools. Borthakur exemplifies the need for companies to continually evaluate new technologies: while he was the founding engineer for HDFS, these days he mostly works with object stores like S3.…
In this episode of the Data Show, I spoke with Jike Chong, chief data scientist at Acorns, a startup focused on building tools for micro-investing. Chong has extensive experience using analytics and machine learning in financial services, and he has experience building data science teams in the U.S. and in China. We had a great conversation spanning many topics, including: Potential applications of data science in financial services. The current state of data science in financial services in both the U.S. and China. His experience recruiting, training, and managing data science teams in both the U.S. and China.…
In this episode of the Data Show, I spoke with Jeff Jonas, CEO, founder and chief scientist of Senzing, a startup focused on making real-time entity resolution technologies broadly accessible. He was previously a fellow and chief scientist of context computing at IBM. Entity resolution (ER) refers to techniques and tools for identifying and linking manifestations of the same entity/object/individual. Ironically, ER itself has many different names (e.g., record linkage, duplicate detection, object consolidation/reconciliation, etc.). ER is an essential first step in many domains, including marketing (cleaning up databases), law enforcement (background checks and counterterrorism), and financial services and investing. Knowing exactly who your customers are is an important task for security, fraud detection, marketing, and personalization. The proliferation of data sources and services has made ER very challenging in the internet age. In addition, many applications now increasingly require near real-time entity resolution. We had a great conversation spanning many topics including: Why ER is interesting and challenging How ER technologies have evolved over the years How Senzing is working to democratize ER by making real-time AI technologies accessible to developers Some early use cases for Senzing’s technologies Some items on their research agenda…
In this episode of the Data Show, I spoke with Neelesh Salian, software engineer at Stitch Fix, a company that combines machine learning and human expertise to personalize shopping. As companies integrate machine learning into their products and systems, there are important foundational technologies that come into play. This shouldn’t come as a shock, as current machine learning and AI technologies require large amounts of data—specifically, labeled data for training models. There are also many other considerations—including security, privacy, reliability/safety—that are encouraging companies to invest in a suite of data technologies. In conversations with data engineers, data scientists, and AI researchers, the need for solutions that can help track data lineage and provenance keeps popping up. There are several San Francisco Bay Area companies that have embarked on building data lineage systems—including Salian and his colleagues at Stitch Fix. I wanted to find out how they arrived at the decision to build such a system and what capabilities they are building into it.…
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