Machine Learning on Encrypted Data

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Worth Knowing with Bonnie Habyan

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#11: From OJ Simpson Case to Best Selling Author - Marcia Clark Shares Latest Real Crime Book Release and How Resilience Is Key to Success and Reinvention 34:35

לפני 10 ימים34:35

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34:35

Marcia Clark, best known as the lead prosecutor in the O.J. Simpson trial, has become a trailblazer for women in law and beyond. Her journey from courtroom to bestselling author reflects her resilience and determination to redefine herself amidst intense public scrutiny. On this episode of Worth Knowing, Clark dives into her latest book, *Trial by Ambush*, which examines the 1953 Barbara Graham case—a story that highlights gender bias, media sensationalism, and the notion that all cases are subject to societal, cultural, and political winds. Clark shares how her experiences during the Simpson trial shaped her perspective on societal pressures and the role of women in high-stakes professions. Her reflections on how media, forensic science, and legal practices have evolved over decades offer valuable insights into the intersection of law and culture. This conversation is a compelling exploration of true crime, personal growth, and how Clark’s groundbreaking career continues to inspire a new generation of women to challenge norms and forge their own paths. Marcia Clark is a bestselling author and a criminal lawyer who began her career in law as a criminal defense attorney and went on to become a prosecutor in the L.A. District Attorney's Office in 1981. She spent ten years in the Special Trials Unit, where she handled a number of high-profile cases, including the prosecution of stalker/murderer Robert Bardo, whose conviction for the murder of actress Rebecca Schaeffer resulted in legislation that offered victims better protection from stalkers as well as increased punishment for the offenders. She was lead prosecutor for the O.J. Simpson murder trial. In May of 1997 her book on the Simpson case, "Without a Doubt," was published and reached #1 on the New York Times, Wall St. Journal, Washington Post, Los Angeles Times, and Publishers Weekly bestsellers lists. In February 2016, Clark re-released the book with a new foreword. Resources Sign up for the Worth Knowing LinkedIn Newsletter to stay up to date: https://www.linkedin.com/newsletters/worth-knowing-7236433935503618048/ Follow Bonnie on LinkedIn: https://www.linkedin.com/in/bonnie-habyan/ Go to the Worth Knowing website: https://www.worthknowing.show Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company…

לפני 7 שנים 41:25

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In this episode of the Data Show, I spoke with Alon Kaufman, CEO and co-founder of Duality Technologies, a startup building tools that will allow companies to apply analytics and machine learning to encrypted data. In a recent talk, I described the importance of data, various methods for estimating the value of data, and emerging tools for incentivizing data sharing across organizations. As I noted, the main motivation for improving data liquidity is the growing importance of machine learning. We’re all familiar with the importance of data security and privacy, but probably not as many people are aware of the emerging set of tools at the intersection of machine learning and security. Kaufman and his stellar roster of co-founders are doing some of the most interesting work in this area.

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Machine Learning on Encrypted Data

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443 פרקים

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Semua episod

1
Machine Learning for Operational Analytics and Business Intelligence 51:41

לפני 6 years51:41

51:41

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.…

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Machine Learning and Analytics for Time Series Data 40:33

לפני 6 years40:33

40:33

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.…

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Understanding Deep Neural Networks 39:34

לפני 6 years39:34

39:34

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.…

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Becoming a Machine Learning Practitioner 33:24

לפני 6 years33:24

33:24

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.…

1
Labeling, Transforming, and Structuring Training Data Sets for Machine Learning 40:54

לפני 6 years40:54

40:54

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.…

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Taming Chaos: Preparing for Your Next Incident 29:45

לפני 6 years29:45

29:45

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.…

1
Make Data Science More Useful 35:06

לפני 6 years35:06

35:06

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?…

1
Acquiring and Sharing High-Quality Data 39:23

לפני 6 years39:23

39:23

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.”…

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Tools for Machine Learning Development 39:27

לפני 6 years39:27

39:27

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).…

1
Enabling End-to-End Machine Learning Pipelines in Real-World Applications 42:56

לפני 6 years42:56

42:56

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.…

1
How to Get Started With Site Reliability Engineering (SRE) 38:32

לפני 6 years38:32

38:32

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.…

1
Bringing Scalable Real-time Analytics to the Enterprise 37:14

לפני 6 years37:14

37:14

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.…

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Applications of Data Science and Machine Learning in Financial Services 42:35

לפני 6 years42:35

42:35

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.…

לפני 7 years41:25

41:25

O'Reilly Radar

1
How Social Science Research Can Inform the Design of AI Systems 45:33

לפני 7 years45:33

45:33

In this episode of the Data Show, I spoke with Jacob Ward, a Berggruen Fellow at Stanford University. Ward has an extensive background in journalism, mainly covering topics in science and technology, at National Geographic, Al Jazeera, Discovery Channel, BBC, Popular Science, and many other outlets. Most recently, he’s become interested in the interplay between research in psychology, decision-making, and AI systems. He’s in the process of writing a book on these topics, and was gracious enough to give an informal preview by way of this podcast conversation.…

O'Reilly Radar

1
Why It's Hard to Design Fair Machine Learning Models 34:26

לפני 7 years34:26

34:26

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.…

O'Reilly Radar

1
Kelsey Hightower and Chris Gaun on Serverless and Kubernetes 37:11

לפני 7 years37:11

37:11

This episode of the O’Reilly Podcast, features a conversation on serverless and Kubernetes, with Kelsey Hightower, developer advocate for Google Cloud Platform at Google (and co-author of "Kubernetes: Up and Running"), and Chris Gaun, Kubernetes product marketing manager at Mesosphere.

O'Reilly Radar

1
Using Machine Learning to Improve Dialog Flow in Conversational Applications 45:06

לפני 7 years45:06

45:06

In this episode of the Data Show, I spoke with Alan Nichol, co-founder and CTO of Rasa, a startup that builds open source tools to help developers and product teams build conversational applications. About 18 months ago, there was tremendous excitement and hype surrounding chatbots, and while things have quieted lately, companies and developers continue to refine and define tools for building conversational applications. We spoke about the current state of chatbots, specifically about the types of applications developers are building today and how he sees conversational applications evolving in the near future.…

O'Reilly Radar

1
Building Accessible Tools for Large-Scale Computation and Machine Learning 53:35

לפני 7 years53:35

53:35

In this episode of the Data Show, I spoke with Eric Jonas, a postdoc in the new Berkeley Center for Computational Imaging. Jonas is also affiliated with UC Berkeley’s RISE Lab. It was at a RISE Lab event that he first announced Pywren, a framework that lets data enthusiasts proficient with Python run existing code at massive scale on Amazon Web Services. Jonas and his collaborators are working on a related project, NumPyWren, a system for linear algebra built on a serverless architecture. Their hope is that by lowering the barrier to large-scale (scientific) computation, we will see many more experiments and research projects from communities that have been unable to easily marshal massive compute resources. We talked about Bayesian machine learning, scientific computation, reinforcement learning, and his stint as an entrepreneur in the enterprise software space.…

O'Reilly Radar

1
Developers Worldwide Will Collaborate on Solutions to Save Lives in Call For Code 15:49

לפני 7 years15:49

15:49

In this episode of the O’Reilly Media Podcast, Rachel Roumeliotis, VP of content strategy at O’Reilly, sat down with Daniel Krook, IBM developer advocate. They discussed how developers across industries can participate in the Call for Code initiative, the benefits of the program, support from its charitable partners—United Nations Human Rights and the American Red Cross—and the positive impacts IBM hopes to achieve by investing in Call for Code.…

O'Reilly Radar

1
Simplifying Machine Learning Lifecycle Management 37:27

לפני 7 years37:27

37:27

In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today’s data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies. Risk and compliance considerations mean that the ability to reproduce machine learning workflows is essential to meet audits in certain application domains. And as data science and data engineering teams continue to expand, tools need to enable and facilitate collaboration. As someone who specializes in helping teams turn machine learning prototypes into production-ready services, I wanted to hear what Doddi has learned while working with organizations that aspire to “become machine learning companies.”…

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1
How Privacy-Preserving Techniques Can Lead to More Robust Machine Learning Models 36:48

לפני 7 years36:48

36:48

In this episode of the Data Show, I spoke with Chang Liu, applied research scientist at Georgian Partners. In a previous post, I highlighted early tools for privacy-preserving analytics, both for improving decision-making (business intelligence and analytics) and for enabling automation (machine learning). One of the tools I mentioned is an open source project for SQL-based analysis that adheres to state-of-the-art differential privacy (a formal guarantee that provides robust privacy assurances). Since business intelligence typically relies on SQL databases, this open source project is something many companies can already benefit from today. What about machine learning? While I didn’t have space to point this out in my previous post, differential privacy has been an area of interest to many machine learning researchers. Most practicing data scientists aren’t aware of the research results, and popular data science tools haven’t incorporated differential privacy in meaningful ways (if at all). But things will change over the next months. For example, Liu wants to make ideas from differential privacy accessible to industrial data scientists, and she is part of a team building tools to make this happen.…

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1
Specialized Hardware for Deep Learning Will Unleash Innovation 41:23

לפני 7 years41:23

41:23

In this episode of the Data Show, I spoke with Andrew Feldman, founder and CEO of Cerebras Systems, a startup in the blossoming area of specialized hardware for machine learning. Since the release of AlexNet in 2012, we have seen an explosion in activity in machine learning, particularly in deep learning. A lot of the work to date happened primarily on general purpose hardware (CPU, GPU). But now that we’re six years into the resurgence in interest in machine learning and AI, these new workloads have attracted technologists and entrepreneurs who are building specialized hardware for both model training and inference, in the data center or on edge devices. In fact, companies with enough volume have already begun building specialized processors for machine learning. But you have to either use specific cloud computing platforms or work at specific companies to have access to such hardware. A new wave of startups (including Cerebras) will make specialized hardware affordable and broadly available. Over the next 12-24 months architects and engineers will need to revisit their infrastructure and decide between general purpose or specialized hardware, and cloud or on-premise gear. ARTIFICIAL INTELLIGENCE CONFERENCE The Artificial Intelligence Conference in San Francisco, September 4-7, 2018 Early price ends July 20. In light of the training duration and cost they face using current (general purpose) hardware, some experiments might be hard to justify. Upcoming specialized hardware will enable data scientists to try out ideas that they previously would have hesitated to pursue. This will surely lead to more research papers and interesting products as data scientists are able to run many more experiments (on even bigger models) and iterate faster. As founder of one of the most anticipated hardware startups in the deep learning space, I wanted get Feldman’s views on the challenges and opportunities faced by engineers and entrepreneurs building hardware for machine learning workloads.…

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1
How to Choose and Deploy a Service Mesh 15:35

לפני 7 years15:35

15:35

In a recent episode of the O’Reilly Media Podcast, we spoke with George Miranda about the importance of service mesh technology in creating reliable distributed systems. As discussed in the new report The Service Mesh: Resilient Service-to-Service Communication for Cloud Applications, service mesh technology has emerged as a popular tool for companies looking to build cloud-native applications that are reliable and secure. During the podcast, we discussed the problems a service mesh infrastructure solves and the service mesh features you’ll find most valuable. We also talked about how to choose the right service mesh for your organization, the challenges involved in getting it deployed to production, and the best ways for getting started with a service mesh.…

O'Reilly Radar

1
Data Regulations and Privacy Discussions are Still in the Early Stages 33:23

לפני 7 years33:23

33:23

In this episode of the Data Show, I spoke with Aurélie Pols of Mind Your Privacy, one of my go-to resources when it comes to data privacy and data ethics. This interview took place at Strata Data London, a couple of days before the EU General Data Protection Regulation (GDPR) took effect. I wanted her perspective on this landmark regulation, as well as her take on trends in data privacy and growing interest in ethics among data professionals.…

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1
Managing Risk in Machine Learning Models 32:38

לפני 7 years32:38

32:38

In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer at Immuta, and Steven Touw, co-founder and CTO of Immuta. Burt recently co-authored an upcoming white paper on managing risk in machine learning models, and I wanted to sit down with them to discuss some of the proposals they put forward to organizations that are deploying machine learning. Some high-profile examples of models gone awry have raised awareness among companies for the need for better risk management tools and processes. There is now a growing interest in ethics among data scientists, specifically in tools for monitoring bias in machine learning models. In a previous post, I listed some of the key considerations organization should keep in mind as they move models to production, but the upcoming report co-authored by Burt goes far beyond and recommends lines of defense, including a description of key roles that are needed.…

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1
The Real Value of Data Requires a Holistic View of the End- to-End Data Pipeline 31:09

לפני 7 years31:09

31:09

In this episode of the Data Show, I spoke with Ashok Srivastava, senior vice president and chief data officer at Intuit. He has a strong science and engineering background, combined with years of applying machine learning and data science in industry. Prior to joining Intuit, he led the teams responsible for data and artificial intelligence products at Verizon. I wanted his perspective on a range of issues, including the role of the chief data officer, ethics in machine learning, and the emergence of AI technologies for enterprise products and applications.…

O'Reilly Radar

1
From Chaos Engineering to Bug Bounties—Why Companies Need to Experiment with New Technology 20:42

לפני 7 years20:42

20:42

In this episode of the O’Reilly Podcast, I talk with Tammy Butow, a site reliability engineer at Gremlin, and Annie Lau, a software engineering manager at Trulia, about creating a culture of learning, how experimentation is important to business, and their careers in tech.

O'Reilly Radar

1
The Evolution of Data Science, Data Engineering, and AI 30:18

לפני 7 years30:18

30:18

This episode of the Data Show marks our 100th episode. This podcast stemmed out of video interviews conducted at O’Reilly’s 2014 Foo Camp. We had a collection of friends who were key members of the data science and big data communities on hand and we decided to record short conversations with them. We originally conceived of using those initial conversations to be the basis of a regular series of video interviews. The logistics of studio interviews proved too complicated, but those Foo Camp conversations got us thinking about starting a podcast, and the Data Show was born. To mark this milestone, my colleague Paco Nathan, co-chair of Jupytercon, turned the tables on me and asked me questions about previous Data Show episodes. In particular, we examined the evolution of key topics covered in this podcast: data science and machine learning, data engineering and architecture, AI, and the impact of each of these areas on businesses and companies. I’m proud of how this show has reached so many people across the world, and I’m looking forward to sharing more conversations in the future.…

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1
Cory Doctorow on the Fight for a Configurable and Free Internet 21:53

לפני 7 years21:53

21:53

In this episode of the O’Reilly Podcast, I talk with Cory Doctorow, who is a science fiction author, editor of Boing Boing, the former European director of the Electronic Frontier Foundation (EFF), and currently a special advisor for the EFF. Doctorow will be a keynote speaker at the O’Reilly Fluent Conference, July 11-14, 2018, in San Jose.…

O'Reilly Radar

1
Brian Holt on Learning Javascript and Understanding How to Scale a Website 25:21

לפני 7 years25:21

25:21

In this episode of the O'Reilly Podcast, Fluent Conference Speaker Series chair and author Kyle Simpson sat down with Brian Holt, a senior cloud developer at Microsoft. Holt will be teaching a training course, A Complete Introduction to React" and hosting a session "10 KB or bust: The delicate power of webpack and Babel" at the O'Reilly Fluent Conference in June. Simpson and Holt discuss the winding road to finding your way in the software industry, new changes to React, and optimizing the end user experience.…

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