InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
About this Podcast
Intended for architects and senior developers.
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Victor Dibia on TensorFlow.js and Building Machine Learning Models with JavaScript
Victor Dibia is a research engineer with Cloudera’s Fast Forward Labs. On today’s podcast, Wes Reisz and Victor Dibia talk about the realities of building machine learning in the browser. The two discuss the capabilities, limitations, process, and realities around using TensorFlow.js.
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Bryan Cantrill on Rust and Why He Feels It’s The Biggest Change in Systems Development in His Career
Today on the podcast, Bryan Cantrill discusses with Wes Reisz a bit about the origins of DTrace and then spends the rest of the time discussing why he feels Rust is the “biggest development in systems development in his career.”
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Katharine Jarmul and Ethical Machine Learning
Today on The InfoQ Podcast, Wes talks with Katharine Jarmul about privacy and fairness in machine learning algorithms. Jarmul discusses what’s meant by Ethical Machine Learning and some things to consider when working towards achieving fairness. Jarmul is the co-founder at KIProtect, a machine learning security and privacy firm based in Germany, and is one of the three keynote speakers at QCon.ai.
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Grady Booch on Today’s Artificial Intelligence Reality and What it Means for Developers
On the podcast today, Wes Reisz and Grady Booch discuss what today’s reality is for AI. Booch answers questions like what does an AI mean to the practice of writing software, and how it seems to impact delivering software. In addition, Booch talks about AI surges (and winters) over the years, the importance of ethics in software, and host of other related questions.
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Emmanuel Ameisen, Head of AI at Insight, on Building a Semantic Search System for Images
On this week’s podcast, Wes Reisz talks to Emmanuel Ameisen, head of AI for Insight Data Science, about building a semantic search system for images using convolution neural networks and word embeddings, how you can build on the work done by companies like Google, and then explores where the gaps are and where you need to train your own models.
