How did we get from symbolic AI to deep learning models that help you write code (i.e., GitHub and OpenAI’s new Copilot)? That’s what Chris and Daniel discuss in this episode about the history and future of deep learning (with some help from an article recently published in ACM and written by the luminaries of deep learning).Join the discussionChangelog++ members save 3 minutes on this episode because they made the ads disappear. Join today!Sponsors:PSSC Labs – Solutions from PSSC Labs provide a cost effective, highly secure, and performance guarantee that organizations need to reach their AI and Machine Learning Goals. Learn more and and get a FREE consultation today at pssclabs.com/practicalaiSnowplow Analytics – The behavioral data management platform powering your data journey. Capture and process high-quality behavioral data from all your platforms and products and deliver that data to your cloud destination of choice. Get started and experience Snowplow data for yourself at snowplowanalytics.comFastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com. Featuring:Chris Benson – Website, GitHub, LinkedIn, XDaniel Whitenack – Website, GitHub, XShow Notes:ACM article: “Deep Learning for AI”GitHub CopilotBooks“Human-in-the-Loop Machine Learning” by Robert (Munro) Monarch (use podpracticalAI19 for 40% off)“A Thousand Brains” by Jeff HawkinsSomething missing or broken? PRs welcome!

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