ML, Data, Geo

O’Reilly’s Jenn Webb chats with Andreessen Horowitz general partner Balaji Srinivasan about the bigger picture of bitcoin, liquid markets, and the future of regulation.

More than a currency, bitcoin is an enabling technology

O'Reilly's Jenn Webb chats with Ethereum founder Vitalik Buterin about the disruptive potential of the bitcoin and blockchain technologies, some of the problems he's trying to solve with Ethereum, and how the use cases of money are going to change over the next 10 to 20 years. Also, Three Questions with Mac Slocum and Elizabeth Stark.

Better Currency Through Programming

O'Reilly's Jenn Webb chats with Filament co-founder and CEO Eric Jennings about how a decentralized Internet would work, what we need to do to get there, and why it will become necessary as the Internet of Things begins to scale. Eric also talks about why his company recently shifted its focus from the maker community to industrial manufacturing.

A Vision of a Decentralized IoT Stack

O'Reilly's Jenn Webb chats with Cory Doctorow about pitfalls in the Internet of Things and his work with the Electronic Frontier Foundation (EFF), including work to reform the Digital Millennium Copyright Act (DMCA) — the source of many of those IoT pitfalls. Doctorow also talks about why we should treat human beings as things that are good at sensing as opposed to things that need to be sensed.

Harmonizing the Four Factors That Regulate Our Society

O’Reilly’s Mac Slocum chats with Bradley Voytek about using data-driven approaches in his neuroscience work, the brain scanner project, and applying cognitive neuroscience to the zombie brain.

Data-Driven Neuroscience

O'Reilly's Jenn Webb chats with Simon King, design director at IDEO, about bringing design to his farming roots, his new book "Understanding Industrial Design," and the synergies between industrial design and interaction design.

Designing at the Intersection of Disciplines

Mike Kuniavsky, a principal scientist in the Innovation Services Group at PARC, on designing for the Internet of Things ecosystem and why the most interesting thing about the IoT isn’t the “things” but the sensors. He also talks about his deep-seated love for appliances and furniture, and how intelligence will affect those industries.

Mike Kuniavsky on the Tectonic Shift of the IoT

This week, O'Reilly's Mary Treseler chats with designer, creative coder, and artist Scott Murray about coding and computation in design, his book "Interactive Data Visualization for the Web" and his new book coming out soon "Creative Coding and Data Visualization with p5.js."

Designing With Code and Computation

In this episode, I chat with Marc Warner, CEO of ASI, a data science and business analytics consultancy and training organization in London. We talk about artificial intelligence, speculating about the future and looking at current real-world business applications of AI. We also talk about a survey Warner recently conducted with data science companies in London, where he uncovered a data scientist skills cap.

Marc Warner on AI's Fundamental Shift: Supplemental Thinking

This week, I chat with Othman Laraki, co-founder of Color Genomics. We chat about challenges and opportunities in genetic testing, the future of precision medicine, and the hurdles medicine and health care are currently facing (and how we can overcome them).

Othman Laraki on Achieving the Long-Tail Distribution of Genetic Insights

In this Radar Podcast episode, I chat with Haakon Faste, a design educator and innovation consultant. We talk about his interesting career path, including his perceptual robotics work, his teaching approaches, and his mission with the Ralf A. Faste Foundation. We also talk about navigating our way to a "post-human" world and the importance of designing to make the world a more human-centered place.

Haakon Faste on Designing for a Post-Human World

In this episode of the Radar Podcast, I chat with John Bassett III, chairman of the board of the Vaughan-Bassett Furniture Company. We talk about globalization and the effect it's had on the furniture industry, the international trade battle he waged (which was written about by Beth Macy in her book Factory Man), Bassett's book Making it in America, and what entrepreneurs need to know to succeed in business today.

John Bassett III on the Global Economy, the Power of People, and How to Make It in America

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

In this episode of the Data Show, I spoke with Anima Anandkumar, a leading machine learning researcher, and currently a principal research scientist at Amazon. I took the opportunity to get an update on the latest developments on the use of tensors in machine learning. Most of our conversation centered around MXNet—an open source, efficient, scalable deep learning framework. I’ve been a fan of MXNet dating back to when it was a research project out of CMU and UW, and I wanted to hear Anandkumar’s perspective on its recent progress as a framework for enterprises and practicing data scientists.

Deep Learning That's Easy to Implement and Easy to Scale

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 Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled "Hands-on Machine Learning with Scikit-Learn and TensorFlow." Géron’s book is aimed at software engineers who want to learn machine learning and start deploying machine learning models in real-world products.

Becoming a Machine Learning Engineer

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.

ML, Data, Geo

Avi Veidman