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Practical AI Practical AI #15

Artificial intelligence at NVIDIA

NVIDIA Chief Scientist Bill Dally joins Daniel Whitenack and Chris Benson for an in-depth conversation about 'everything AI' at NVIDIA. As the leader of NVIDIA Research, Bill schools us on GPUs, and then goes on to address everything from AI-enabled robots and self-driving vehicles, to new AI research innovations in algorithm development and model architectures. This episode is so packed with information, you may want to listen to it multiple times.

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link Icon spectrum.ieee.org

LinkedIn reports dramatically increasing shortage of data scientists across U.S.

What a difference a few years makes. In 2015, a LinkedIn snapshot of what it calls the skills gap—a mismatch between the skills workers have and the skills employers seek—showed a national surplus in the United States of people with data science skills; as of August 2018, LinkedIn data shows a dramatic shortage. It's a good time to be alive a Practical AI listener. 😉

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Kubernetes blog.openai.com

Scaling Kubernetes to 2,500 Nodes

Are you really pushing Kubernetes? No? OpenAI is... We’ve been running Kubernetes for deep learning research for over two years. While our largest-scale workloads manage bare cloud VMs directly, Kubernetes provides a fast iteration cycle, reasonable scalability, and a lack of boilerplate which makes it ideal for most of our experiments. We now operate several Kubernetes clusters (some in the cloud and some on physical hardware), the largest of which we’ve pushed to over 2,500 nodes. This cluster runs in Azure on a combination of D15v2 and NC24 VMs.

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Machine Learning varianceexplained.org

What's the difference between data science, machine learning, and AI?

We've needed this post for a very long time. Thank you David Robinson. When I introduce myself as a data scientist, I often get questions like “What’s the difference between that and machine learning?” or “Does that mean you work on artificial intelligence?” But that overlap, tho. The fields do have a great deal of overlap, and there’s enough hype around each of them that the choice can feel like a matter of marketing. But they’re not interchangeable. Most professionals in these fields have an intuitive understanding of how particular work could be classified as data science, machine learning, or artificial intelligence, even if it’s difficult to put into words. Here's the break down...

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Turning Design Mockups Into Code With Deep Learning

How do you you teach a neural network to code? One screenshot with matching HTML at a time. 😂 Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software. The field took off last year when Tony Beltramelli introduced the pix2code paper and Airbnb launched sketch2code. Currently, the largest barrier to automating front-end development is computing power.

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The Changelog The Changelog #273

Data Science at OSCON with Vida Williams and Michelle Casbon

We went back into the archives to conversations we had around data science at OSCON 2017. We talked with Vida Williams (Data Scientist) and Michelle Casbon (Director of Data Science at Qordoba) about the social impact of open data, personal data and transparency, privacy, the big data problem of public surveillance, electronic fingerprinting, the rift between data scientists and computer scientists, natural language processing, machine learning, and more.

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