I gave a presentation at the Ray Summit on my work building Multimodal Foundation Models for Document Automation at Uber. It is always a great pleasure to publicly share what I have been building over the past year!
Read MoreOver the past four and a half years at Landing AI, I have had the incredible opportunity to work with Andrew Ng, Dillon Laird and other amazing people to build AI applications across various industries. Each project has brought its unique challenges, pushing me to dive deeper into the ever-evolving world of AI. As I look back at this enriching journey, I am grateful and humble to share the lessons that I've learned in the hope of inspire others in the field.
Read MoreIn this blog post, I will walk you through how to build a fast and simple image search tool. I developed an image search application that uses multimodal foundation models to search for highly accurate and relevant results. By following this blog post and our code base, you can easily build one yourself!
Read MoreLast week, at Landing AI, we publicly launched our flagship AI platform, LandingLens. This all-in-one platform empowers users to build a computer vision application from start to deployment. In this blog, I want to share the motivation behind building this AI platform as well as highlight a few key features that I truly enjoy!
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In this blog post, I cover one of the awarded papers in NeurIPS 2022. This paper presents LAION-5B, a dataset consisting of 5.9 billion image-text pairs, to further push the scale of open datasets for training and studying state-of-the-art language-vision models. With this large scale, it gives strong increases to zero-shot transfer and robustness.
Read MoreQuestion Answering models are often used to automate the response to human questions by leveraging a knowledge base. My team at Stanford aims to build a robust question answering system that works across datasets from multiple domains. We explore two transformer-based Sparsely-Gated Mixture-of-Experts architectures and conduct an extensive ablation study to reach the best performance.
Read MoreMetrics are critical in machine learning projects. They help a team to prioritize their resources and concentrate on a single, clear objective. I am always amazed to see that, once my team is aligned on a single metric to optimize, the speed and momentum we will be able to execute. In the end, we will usually be able to accomplish the goals that seem impossible in the beginning.
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Based on our past experience at Landing AI we have developed best practices for model training and evaluation. In this article, I share a few high-priority tasks during model training. We openly share our guiding principles to help machine learning engineers (MLEs) through model training and evaluation.
Read MoreAt Landing AI we observed how many projects took an unnecessarily long and painful process to complete. It was due to ambiguous defect definitions or poor labeling quality. In comparison, it will make the life of machine learning engineers much easier, and the whole project lifespan much shorter, by having a dataset with high quality labels. Therefore, it is very important to invest the time in the project’s early stage to clarify defect definitions and formalize labeling.
Read MoreThis paper reminds me of many time where our model in production perform strangely, so engineers have to spend hours investigate root causes and roll back or push for fixes. Lots of late night works as result of such mistakes. I agree with this paper that such data validation systems, if implemented correctly, can really help save significant amount of engineer hours by catching important errors proactively and diagnose model errors more efficiently.
Read MoreAt Landing AI, I have gone through several projects where we developed an end-to-end machine-learning system from “scratch'“. That means before we started on the project, there was no existing data collection procedure, so we had to start from zero and set up cameras.
Read MoreI am looking forward to a more radical redesign of gym space that well integrates with these virtual workout services and elevate the users’ experience around workout.
Read MoreThere are two types of Full Stack Machine Learning Engineering in my mind — one vertical and one horizontal
Read MoreMeta Learning is one of the promising lines of work that aim to solve the small data problems in machine learning field. Currently, many people working on AI are thinking day and night about how to scale AI systems and improve their profit margins. One main challenge to solve is how to quickly build an AI model that reaches human-level performance on classes with only a few samples.
Read MoreI did this tech talk at Landing AI this week and I’d like to share it out on my website. It’s about the core concepts in photography and a little on the recent trend of computational photography.
I removed a section that talks about the imaging solution design in one of our internal projects, due to IP restriction. I will later share a blog post on that topic.
Read MoreToday, I read the “memory overload” section in “Sapiens”, where I learned that the first few generations of the written languages were developed for the purpose of book-keeping. The written languages were developed for accounting and taxation of ancient empires.
Read MoreToday I got a chance to visit the Airbnb HQ through an Experience on the platform. Through the tour, I am deeply impressed by the company’s strong obsession with design and its dedication towards its missions.
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Business models that leverage location data from mobile devices are part of a growing, multi-billion dollar market. Where you are matters much more than you might think.
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In this report, we will look at the application of algorithmic dynamic pricing through case studies of ride-hailing startups Uber and Lyft and E-commerce giant Amazon. We will also discuss potential issues involved in personalized dynamic pricing.
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