Donghyeon Won

Marina Wyss, Senior Applied Scientist at Twitch, joins 🏳️‍🌈🏳️‍🌈 Kyler Middleton and Ned Bellavance to discuss her unique path from political science to AI Engineering. Wyss clarifies the difference between AI Engineering and Machine Learning Engineering and offers practical advice for aspiring engineers who want to incorporate data science, AI, and machine learning into their work. She digs into the importance of models and model selection when building products for different use cases, and talks about the value that the social sciences can bring to product development. She also stresses the importance of self-directed projects over certifications and suggests what to look for in training programs. Listen here or wherever you get your podcasts - https://lnkd.in/dcK_jjP4

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Physical Intelligence turns terabytes (TBs) of robotics sensor and video data into high-value training datasets. Before Ray on Anyscale, scaling multimodal data pipelines across diverse hardware slowed progress and demanded deep infrastructure expertise, pulling developers away from differentiated research work. With Anyscale, Physical Intelligence enables more than a dozen of its developers to run experiments concurrently and deploy workloads seamlessly across a multi-cloud environment, accelerating iteration and keeping teams focused on their mission to build advanced AI for robotics. Read the full story: https://lnkd.in/g29uj4vi

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Davi Prates's takeaways from SF capture the shift from "writing code" to "product taste." That's the environment our engineers thrive, prioritizing the user experience over the syntax. Time to stop treating engineering like a high-speed typing contest and get back to building stuff.

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What kinds of algorithmic building blocks do transformer models use to do different tasks? Jack Merullo recently gave a guest lecture at Stanford on "computational motifs" - the basic algorithmic primitives that show up again and again when we peer inside different circuits and models - and how they can help us understand models more fully. If you're interested in learning more about this approach to studying neural networks, check out this lecture (and our previous one, on causal approaches) - and stay tuned for our final lecture and reading list. Thanks again to Surya Ganguli for having us in his class! Watch the full lecture on YouTube: https://lnkd.in/g8n44bPs

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Stanford Center for Research on Foundational Model's Marin project has released the first fully open model in JAX. It’s an 'open lab' sharing the entire research process - including code, data, and logs, to enable reproducibility and further innovation. Check out the project: https://goo.gle/44AMeLY

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