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https://github.com/AnanthaRajuC/Engineering-and-Business-Podcasts

Software Engineering and Business podcasts

Top 50: Technology

The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

In this episode, I talk with Meredith Patterson, a software engineer and leader of the Langsec Conspiracy. We discuss the origins of LangSec, rigidity versus robustness, and game theory as it applies to organizational approaches to security.

Meredith Patterson on Using Language to Build Trustworthy Systems

Dr Pete and Russ Nash offers up a monstrous round-up of AWS news and services updates, spanning from performance enhancements, price reductions and software dev updates. Tune in to get across the most significant updates from AWS. Visit awstechchat.com for more info.

Episode 5 - Stay up to date on the cloud topics that matter to you most in this latest round-up

https://www.semanticscholar.org/paper/FastQA-A-Simple-and-Efficient-Neural-Architecture-Weissenborn-Wiese/7c1576b96a1e246d77b30f7b80cec63be96fa698

03 - FastQA: A Simple and Efficient Neural Architecture for Question Answering

https://www.semanticscholar.org/paper/Design-Challenges-for-Entity-Linking-Ling-Singh/aa2a7ac7bfa9a0201d4faddd4e7bb26299a5e0be

06 - Design Challenges for Entity Linking

https://www.semanticscholar.org/paper/Learning-to-Generate-Reviews-and-Discovering-Senti-Radford-Jozefowicz/664ec878de4b7170712baae4a7821fc2602bba25

https://blog.openai.com/unsupervised-sentiment-neuron/

09 - Learning to Generate Reviews and Discovering Sentiment

https://www.semanticscholar.org/paper/Question-Answering-from-Unstructured-Text-by-Retri-Watanabe-Dhingra/89d06c2996f379c602e64b4243f026cd164400d7

13 - Question Answering from Unstructured Text by Retrieval and Comprehension

https://www.semanticscholar.org/paper/Generalizing-to-Unseen-Entities-and-Entity-Pairs-w-Verga-Neelakantan/7dd8b958632b07e41979337c71d847a3f39df456

18 - Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema

In this episode, I talk with Amanda Berlin, security architect at Hurricane Labs. We discuss how to assess and develop defensive security policies when you’re new to the task, how to approach core security fundamentals like asset management, and generally how you can successfully improve your organization’s defensive security with limited time and resources.

Amanda Berlin on Defensive Security Fundamentals

The What Bitcoin Did Podcast - Intro

In this episode of the Data Show, I spoke with Fabian Yamaguchi, chief scientist at ShiftLeft. His 2015 Ph.D. dissertation sketched out how the combination of static analysis, graph mining, and machine learning, can be used to develop tools to augment security analysts. In a recent post, I argued for machine learning tools to augment teams responsible for deploying and managing models in production (machine learning engineers). These are part of a general trend of using machine learning to develop and manage the software systems of tomorrow. Yamaguchi’s work is step one in this direction: using machine learning to reduce the number of security vulnerabilities in complex software products.

How Machine Learning Can Be Used to Write More Secure Computer Programs

In this episode of the Data Show, I spoke with Mark Hammond, founder and CEO of Bonsai, a startup at the forefront of developing AI systems in industrial settings. While many articles have been written about developments in computer vision, speech recognition, and autonomous vehicles, I’m particularly excited about near-term applications of AI to manufacturing, robotics, and industrial automation. In a recent post, I outlined practical applications of reinforcement learning (RL)—a type of machine learning now being used in AI systems. In particular, I described how companies like Bonsai are applying RL to manufacturing and industrial automation. As researchers explore new approaches for solving RL problems, I expect many of the first applications to be in industrial automation.

Machine Learning Needs Machine Teaching

In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs. There are many problems and use cases that lend themselves naturally to graphs, and recent advances in hardware and software building blocks have made large-scale analytics possible.

Starting with his work as a graduate student at UC Berkeley, Meyerovich has pioneered the combination of hardware and software acceleration to create truly interactive environments for visualizing large amounts of data. Graphistry has built a suite of tools that enables analysts to wade through large data sets and investigate business and security incidents. The company is currently focused on the security domain—where it turns out that graph representations of data are things security analysts are quite familiar with.

Graphs as the Front End for Machine Learning

with Martin Casado (@martin_casado), Michel Feaster (@michelfeaster) and Sonal Chokshi (@smc90)

The purpose of category creation, argue the guests in this episode of the podcast, isn't just about making a dent in the way companies work and changing what people do every day... it's about setting the price. And with that, comes creating the concept in people's heads, defining the value, and setting the rules of the game. But when you're going for a big change, you have to play by the current rules of the game, too.

And to make things even more complicated, theories about how "IT is dead" -- or the conviction that companies and departments beyond IT will become empowered through software -- are still very much in transition. Somehow we don't talk about that enough. That means startups need to do everything in two phases: for the now, and for the later and towards two constituencies: both direct lines of businesses and IT. So what does that mean for startups trying to navigate a complex enterprise, including internal debates around build vs. buy? How do you move beyond a few internal champions only? And just how long can a company cash out on founder charisma? In fact, all of these things can give entrepreneurs very confusing, mixed signals about whether or not they have product-market fit yet. So what patterns reveal that it's working?

In this episode of the a16z Podcast, general partner Martin Casado -- who helped create the category of "software-defined networking" in the enterprise through Nicira and then VMware (and has also written about the mixed messages involved in going to market when no market exists) -- and Michel Feaster, CEO and co-founder of Usermind, and who previously (as VP of products at Apptio) also defined the category and discipline of "technology business management" -- share their insights, in conversation with Sonal Chokshi. It's a long game, but if you can tease apart the signals, and nail some key moves early... you can win.

a16z Podcast: Creating a Category, from Pricing to Positioning

In this episode of the Data Show, I spoke with Ameet Talwalkar, assistant professor of machine learning at CMU and co-founder of Determined AI. He was an early and key contributor to Spark MLlib and a member of AMPLab. Most recently, he helped conceive and organize the first edition of SysML, a new academic conference at the intersection of systems and machine learning (ML).

We discussed using and deploying deep learning at scale. This is an empirical era for machine learning, and, as I noted in an earlier article, as successful as deep learning has been, our level of understanding of why it works so well is still lacking. In practice, machine learning engineers need to explore and experiment using different architectures and hyperparameters before they settle on a model that works for their specific use case. Training a single model usually involves big (labeled) data and big models; as such, exploring the space of possible model architectures and parameters can take days, weeks, or even months. Talwalkar has spent the last few years grappling with this problem as an academic researcher and as an entrepreneur. In this episode, he describes some of his related work on hyperparameter tuning, systems, and more.

How to Train and Deploy Deep Learning at Scale

with Kristen Fortney (@kpfortney), Jeff Kaditz (@jeffkaditz), David Sinclair (@davidasinclair), and Hanne Tidnam (@omnivorousread)

Even without a mythical fountain of youth, scientific advances have already dramatically increased how long humans live. But those advances to date have also largely been due to lower mortality rates, less infectious disease, and better nutrition. So when will modern medicine increase not just our healthspan, but our lifespan -- slowing down and possibly even reversing aging? What tools will it take? And what else, beyond the biology and technology involved, would change -- in our healthcare system and society as a whole?

In this episode of the a16z Podcast -- recorded at a16z's November 2017 Summit -- Kristen Fortney, CEO and cofounder of BioAge Labs; Jeff Kaditz, CEO and cofounder of Q.Bio; David Sinclair, Co-Director of the Paul F. Glenn Center for the Biology of Aging at Harvard Medical School; and Michael Snyder, Professor and Department Chair of Genetics at Stanford University (as well as co-founder of and advisor to Q.Bio), in conversation with Hanne Tidnam, break down the science from the science fiction around the topics of longevity, health, and aging.

image credit: Garry Knight/ Flickr

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The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
 
This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: How to Live Longer and Better

Focusing only on the technical, "crunchy, wonky stuff" behind policies or products sometimes misses the humanity at the center of why we're doing the thing in the first place. Because systems -- whether algorithms and artificial intelligence, or capitalism and other such "operating systems" -- need to work for people, not the other way around. Or so observes economist and author Arthur Brooks, president of the American Enterprise Institute (AEI) -- a public policy think tank focused on free enterprise (and where he recently announced he will be passing on the baton after a decade of leadership).

So how does this philosophy of human dignity and human potential apply to automation and jobs, to education, to entrepreneurship? And not just in the "conventional" entrepreneurial sense of building companies and products -- but in changing one's life? The answer, argues Brooks in this quick, hallway-style episode of the a16z Podcast with Sonal Chokshi (recorded in one of our earlier Washington, D.C. roadshows) -- has to be rooted in the philosophy of human meaning. And that involves truly needing each other... so no one is left behind given technological progress and innovation.

image credit: Maria Eklind/ Flickr

a16z Podcast: On Morals and Meaning in Products, Business, and Life

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.”

Acquiring and Sharing High-Quality Data

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?

Make Data Science More Useful

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.

Labeling, Transforming, and Structuring Training Data Sets for Machine 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.

Becoming a Machine Learning Practitioner

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.

Understanding Deep Neural Networks

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

How to Train and Deploy Deep Learning at Scale

O'Reilly Radar