Distributed Computing for AI
Kenn So of Shasta Ventures and I recently introduced a class of AI startups (“pegacorns”) that have at least $100 million in annual revenue. We found that more AI pegacorn founders cited proficiency in distributed systems compared to ML and AI. In this new post, we examine metrics that measure interest in distributed systems and distributed computing with an eye towards their current and future implications for machine learning and AI.
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Data Exchange podcast
- Efficient Scaling of Language Models: Barret Zoph and Liam Fedus are Google Brain research scientists who have been working on tools and techniques for the efficient scaling of large language models. We also discuss Google’s new Pathways system, a new large scale orchestration layer for accelerators used to train large neural models.
- Machine Learning for Optimization: These problems are routine in many industries including logistics, manufacturing, retail, energy, and financial services. Such problems can often be complex and hard to solve in practice, and as such teams often have to solve simpler and perhaps less realistic optimization tasks. Ade Fajemisin and Donato Maragno of the University of Amsterdam provide an overview of how ML is being applied to optimization problems.
- Data Science at Stitch Fix: Olivia Liao describes how they blend data science and domain expertise, how they tune recommendations in light of logistics and supply chain constraints, and how they incorporate new developments in large language models, multimodal models and Responsible AI.
Data+AI Summit 2022
I helped develop the program for the upcoming Data+AI Summit in San Francisco, and it is excellent! I hope to see you in-person at the Moscone Center (or online for the virtual portion of the event). I will be giving away some FREE passes to the in-person conference in San Francisco. If you wish to participate in the raffle, please leave your email address on the form I have included in my conference overview:
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