it is playlist about machine learning and artificial intelligence. I'm putting here all podcast in english in related topic. If you know something that should be here please drop me a message with link and follow me on twitter: @hyzhak_en

artificial intelligence

data

ML, Data, Geo

deep learning

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

to listen

With the rise of mobile phones equipped with GPS, we're meeting many more data scientists at start-ups and large companies who specialize in spatio-temporal pattern recognition. Analyzing “moving dots” requires specialized tools and techniques. A few months ago, I sat down with Rajiv Maheswaran founder and CEO of Second Spectrum, a company that applies analytics to sports tracking data. Maheswaran talks about the tools and techniques required to analyze new kinds of sports data.

For more on our interview, visit O'Reilly Radar: http://oreil.ly/1t7EcOQ

The science of moving dots

In this episode, Ben Lorica chats with Jay Kreps, a highly regarded data engineer and former technical lead for Online Data Infrastructure at LinkedIn, and most recently CEO/co-founder of Confluent, about Apache Kafka, data integration, event data, and the Internet of Things.

Building Apache Kafka From Scratch

O'Reilly Ben Lorica chats with Ion Stoica, UC Berkeley Professor and Databricks CEO, about the rise of Apache Spark and Apache Mesos.

Apache Spark's Journey From Academia To Industry

O’Reilly’s big data guru Ben Lorica sits down with prominent data scientist DJ Patil to talk about his new ebook (written with Hilary Mason) and other topics in data science and big data.

A Brief Look At Data Science's Past And Future

What is intelligence? What is learning? Can we build computers and robots that learn? How much information does the brain store? How does mathematics help us answer these questions? Professor Zoubin Ghahramani takes us on a journey exploring these questions and leading us to the field of machine learning: the invisible algorithms underlying many of the tools we now use everyday.

Professor Ghahramani highlights some current areas of research at the frontiers of machine learning, including a project on developing an Automatic Statistician, and speculates on some of the future applications of computers that learn.

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, and the Cambridge Liaison Director of the Alan Turing Institute.


Intelligence and learning in brains and machines

In this episode of the O’Reilly Data Show, I spoke with Rajat Monga, who serves as a director of engineering at Google and manages the TensorFlow engineering team. We talked about how he ended up working on deep learning, the current state of TensorFlow, and the applications of deep learning to products at Google and other companies.

Building Intelligent Applications with Deep Learning and TensorFlow

Think of our inaugural episode of The AI Podcast as a guide for the perplexed.  Host Michael Copeland speaks with NVIDIA's Will Ramey about the history behind today's AI boom and the key concepts you need to know to get your head around a technology that's reshaping the world.

Ep. 1: Deep Learning 101 - Will Ramey, NVIDIA Senior Manager for GPU Computing

In this episode of the O’Reilly Bots Podcast, Pete Skomoroch and I talk with Richard Socher, chief scientist at Salesforce.  He was previously the founder and CEO of MetaMind, a deep learning startup that Salesforce acquired in 2016.  Socher also teaches the “Deep Learning for Natural Language Processing” course at Stanford University. Our conversation focuses on where deep learning and NLP are headed, and interesting current and near-future applications.

Richard Socher on the Future of Deep Learning

In this episode, I sit down with Brad Knox, founder and CEO of Emoters, a startup building a product called bots_alive—animal-like robots that have a strong illusion of life. We chat about the approach the company is taking, why robots or agents that pass themselves off as human without any transparency should be illegal, and some challenges and applications of reinforcement learning and interactive machine learning.

Brad Knox on Creating a Strong Illusion of Life

This week we're featuring a conversation from earlier this year—O'Reilly's Mary Treseler chats with Giles Colborne, managing director of cxpartners. They talk about the transformative effects of AI on design, designing for natural language interactions, and why designers need to nurture the ability to reinvent themselves.

Giles Colborne on AI's Move From Academic Curiosity to Mainstream Tech

Specialists describe deep learning as akin to a rocketship that needs a really big engine (a model) and a lot of fuel (the data) in order to go anywhere interesting. To get a better understanding of the issues involved in building compute systems for deep learning, I spoke with one of the foremost experts on this subject: Greg Diamos, senior researcher at Baidu. Diamos has long worked to combine advances in software and hardware to make computers run faster. In recent years, he has focused on scaling deep learning to help advance the state-of-the-art in areas like speech recognition.

How Big Compute is Fueling the Deep Learning Rocketship

On this week's episode of the Radar Podcast, O'Reilly's Mac Slocum chats with award-winning author Pagan Kennedy about the art and science of serendipity—how people find, invent, and see opportunities nobody else sees, and why serendipity is actually a skill rather than just dumb luck.

Pagan Kennedy on How People Find, Invent, and See Opportunities Nobody Else Sees

As data scientists add deep learning to their arsenals, they need tools that integrate with existing platforms and frameworks. This is particularly important for those who work in large enterprises. In this episode of the Data Show, I spoke with Adam Gibson, co-founder and CTO of Skymind, and co-creator of Deeplearning4J (DL4J). Gibson has spent the last few years developing the DL4J library and community, while simultaneously building deep learning solutions and products for large enterprises.

The Key to Building Deep Learning Solutions for Large Enterprises

2016 saw some significant AI developments. To talk about the AI progress of the last year, we turned to Richard Mallah and Ian Goodfellow. Richard is the director of AI projects at FLI, he’s the Senior Advisor to multiple AI companies, and he created the highest-rated enterprise text analytics platform. Ian is a research scientist at OpenAI, he’s the lead author of a deep learning textbook, and he’s the inventor of Generative Adversarial Networks. Listen to the podcast here or review the transcript here.

AI Breakthroughs With Ian Goodfellow And Richard Mallah

This week, I sit down with David Beyer, an investor with Amplify Partners. We talk about machine learning and artificial intelligence, the challenges he’s seeing in AI adoption, and what he thinks is missing from the AI conversation.

AI Adoption at the Atomic Level of Jobs and Work

In this episode of the Data Show, I spoke with Reza Zadeh, adjunct professor at Stanford University, co-organizer of ScaledML, and co-founder of Matroid, a startup focused on commercial applications of deep learning and computer vision. Zadeh also is the co-author of the forthcoming book TensorFlow for Deep Learning (now in early release). Our conversation took place on the eve of the recent ScaledML conference, and much of our conversation was focused on practical and real-world strategies for scaling machine learning. In particular, we spoke about the rise of deep learning, hardware/software interfaces for machine learning, and the many commercial applications of computer vision.

Scaling Machine Learning

In this episode of the SuperDataScience Podcast, I chat with the Deep Learning Expert Hadelin de Ponteves. You will be able to have a look at the 6 supervised and unsupervised deep learning models, learn the idea to the question: why deep learning instead of machine learning, and discover TensorFlow and PyTorch as the two major tools in Deep Learning.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/47

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 047: An Expert Overview of the Deep Learning Models, Supervised and Unsupervised

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.