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

Winter Sonata (1)

This was my Submission for The Westworld Scoring Competition.. its been a while but i thought id upload anyways. 
it was fun Scoring That scene

Westworld Scoring Competition Submission By Ashraf Elziftawi

GIOVANNI MARRADI - Silent Cry

Phil Dupuis's Album

Toward the Horizon

New Age Music - Pianoscapes Seaside Relaxing Piano Music, Music for Yoga, Meditation and Relaxation

I Wonder - Piano Music and Sea Music with Natural Sounds

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

THE CRITICS ARE COMING. People understand that there will be challenges and fear when they chase their dreams. But they’re not willing to be embarrassed. Are you willing to look like a fool? To go all-in and fail? It’s a guarantee when you go after the thing you want. You have to keep putting your head out there knowing that it could get chopped off. The more you face your fears, the more self-confidence you’ll have. You will fall. You need to have the grit to use it as fuel to get back up. On today’s episode of The School of Greatness, I talk about relentlessly pursuing your passion with one of the most successful football players in history: Tony Gonzalez. Tony Gonzalez is a Pro Football Hall of Famer, retired All-Pro NFL Tight end, and father of four. He is currently an analyst on Fox NFL’s pregame show. During his career, he only missed two games and lost only two fumbles on 1,327 touches. Tony is open about his struggles with depression and self-doubt that he might not be good enough. But by working through these emotions, he has come out a more authentic and heart-centered person. So get ready to learn how to get into the flow of life on Episode 850.

Some Questions I Ask:
How do you get connected to the heart when there is so much ego? (8:00)
When did you experience the most self-doubt? (16:00)
How do people stop caring what people think about them? (31:30)
Who was the most influential for you growing up? (43:00)

In This Episode You Will Learn:
How to get into the flow (6:00)
How to use criticism as fuel (20:00)
About Tony’s struggle to transition from football to commentary (22:00)
An exercise for opening your heart (32:00)
About the letters Tony wrote himself before games (35:00)
How to parent with your heart (52:00)

If you enjoyed this episode, check out the video, show notes and more at http://www.lewishowes.com/850 and follow at instagram.com/lewishowes

The School of Greatness

Tony Gonzalez: What it Takes to Become The Greatest of All Time

Piano Short Ballad

Sahaj Yog Osho Hindi Discourse सहज योग   ओशो प्रवचन हिंदी

Sahaj Yog

OSHO - Sahaj Yog 03

Carol of the bells

how great thou art - piano

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Acquiring and Sharing High-Quality Data

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