Yan Wang

I'm excited to share two papers from the Retrieval team at Walmart that were presented at CIKM last week. They're both about improving the relevance of Embedding-Based Retrieval. 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴-𝗯𝗮𝘀𝗲𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹. https://lnkd.in/gNQe7QhA The first is about how to intelligently choose a query-dependent cutoff on the cosine score to remove irrelevant items from the retrieved set. Since cosine scores aren't comparable across queries, we introduce a "Cosine Adapter" to map the scores to interpretable scores. 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗼𝗳 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴-𝗯𝗮𝘀𝗲𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝘁 𝗪𝗮𝗹𝗺𝗮𝗿𝘁. https://lnkd.in/gdqkc2hR The second is about model training techniques we learned to improve the relevance of embedding models. In particular, we describe how to use a teacher model to provide relevance labels for training Juexin (Joy) Lin Nicholas Rossi, PhD Feng LIU Zhen Yang Sachin Yadav Praveen Reddy Suram Satya Chembolu Prijith Chandran Hrushikesh Mohapatra Alessandro Magnani Ciya Liao

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Our new technical blog post details the inner workings of Nuro's LAMBDA model, a real-time reasoning engine for autonomous driving! https://lnkd.in/eQxQfAUN We're proud to demonstrate the advanced capabilities of LAMBDA and to showcase its capacity to reason intelligently about the wide variety of challenging scenarios that our Nuro vehicles encounter and need to handle autonomously. LAMBDA has shown that it can explain complex interactions and describe safe and responsible behavior in response. What we've shared in this post is one of the many ways that we're building and utilizing foundational models for autonomous driving. The generality and sophistication of the LAMBDA model has led us to explore applying this technology to benefit Nuro's autonomy on-board in addition to improving developer efficiency and Nuro's safety case off-board. We look forward to sharing more of these advancements in the near future! Many thanks to all the contributors to this project. If you want to join us on our mission to advance the state of the art in autonomous robotics please apply here! https://lnkd.in/e9km-dV2 #nuro #ai #deeplearning #autonomousvehicles #robotics

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I've mentioned this to #investors outside of sports and entertainment. The adoption curve for technology in sports is a hockey stick. Years of pilots in fast paced, high stress environments, but if the application is scoped out properly with the right forces behind it can lead to BIG wins in terms of adoption. I don't know much about AiFi Inc. but this looks to be an awesome example on the adoption for checkout free applications. Well done. For #roboticsINsports check out Hop Robotics

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Excited to be back in the Bay Area this week to represent Caper and Instacart at the Embedded Vision Summit! Ever curious how to deploy and optimize AI models running on edge devices? I’ll break down some tips and techniques in my talk, "Real-Time Retail Product Classification on Android Devices inside the Caper AI Cart” If you're into AI, machine learning, or just want to learn about the Caper Cart or how we're improving our user's experience by leveraging AI, please feel free to come say hi!

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It’s been a pleasure to talk alongside such great researchers from Google DeepMind and top Taiwanese universities at Google AI Taiwan Academic Forum. (Details at https://lnkd.in/gnbcWZyf) My topic — “Challenges of Applied ML” — covered 4 observations: - We ought to build product, not merely expose LLMs. - Making them useful and trustworthy requires handling input and output properly. (Read more at https://lnkd.in/gcNDC54R) - Making those model useful requires high-quality data for tuning (and evaluation). (More at https://lnkd.in/gyVYQaaD) - There’s many practical deployment considerations, one of the main ones being cost.

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"To be great, your product needs to be more than just a thin wrapper around somebody else’s API. But mistakes in the opposite direction can be even more costly." From (https://lnkd.in/dYZicknw) Eugene Yan et al. Serving LLMs on your own is extremely costly from the beginning: The selection of the model, the creation of a dataset for benchmarking, preparing the tools around the model, another set of tests (i.e. load testing, sensitivity, safety...), inference-related optimization/engineering... A good strategy would be trying hard anything that's possible without LLMs! And only if you fail consistently (and you're ready to allocate budget upfront), go down the self-LLM journey

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