One of the most memorable moments from this interview is when our guest mentioned that Larry Page hired him to solve intelligence; very few people can say this, and this says something about today’s guest, Dr. Nando de Freitas - a senior researcher at Google and professor at Oxford - as well as the gravity of his present work. Today, I speak with Nando about a topic well known through his research at Google, deep learning. de Freitas gives his perspective on the basics of deep learning, the applications in conversational interfaces and recognizing images and videos, and what the future of this technology might look like in the nearer future.

DeepMind's Nando de Freitas - Why Deep Learning is Like Building with Legos

Purple shirts, haircuts, and cats. How are these three all related? According to deep learning pioneer Andrew Ng, they all played a part in AI’s growing presence in our lives. Ng, formerly of Google and Baidu, and the founder of his new company, Deeplearning.ai, joined this week’s episode of the AI Podcast to share his thoughts on AI being the new electricity.

Deep Learning Pioneer Andrew Ng on AI as the New Electricity

Ep. 32: Deep Learning Pioneer Andrew Ng on AI as the New Electricity

In this episode of the SuperDataScience Podcast, I chat with Meetup organizer and enthusiast, Mary Loubele. You will know how to organize your first Meetup group, learn the ways on how to encourage people in Meetups to interact with each other, and discuss the current state of deep learning and NLP. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/157

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

SDS 157: The Amazing World of a Data Science Meetup

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.”

Simplifying Machine Learning Lifecycle Management

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.

How Social Science Research Can Inform the Design of AI Systems

In what ways can you make your ideas valuable?

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/202

DS

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