Things I Learned at Landing AI

in
Things I Learned at Landing AI

Over 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 More

Fast and Simple Image Search with Foundation Models

in
Fast and Simple Image Search with Foundation Models

In 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 More

The Making of LandingLens AI Platform: Motivation and My Favorite Features

in
The Making of LandingLens AI Platform: Motivation and My Favorite Features

Last 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!

Read More

Side Project Ideas with Large Pretrained Foundation Models

in
Side Project Ideas with Large Pretrained Foundation Models

I have been brainstorming with friends possible side project ideas to try with Foundation Models. In this blog, I’ll share some of the interesting and practical side project ideas that utilize pre-trained foundation models. Whether you're a seasoned developer or a beginner, hope this will inspire you to unleash your creativity and build something amazing.

Read More

做新年计划时，我想你看向未来十年

in
做新年计划时，我想你看向未来十年

临近年末，人们纷纷在社交网络上开始回顾过去一年、开始新一年的计划，这是很好的习惯。吴恩达老师在他新一期newsletter里则提供了一种新的思路：与其以2023年这一年为单位去计划，不如站在更长远的视角，将2023年视作一个开始，做更长期的人生规划。

Read More

Build an Automated Cross-Domain Question Answering System

in
Build an Automated Cross-Domain Question Answering System

Question 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 More

The Importance of Metrics in Machine Learning and How to Use Them

in
The Importance of Metrics in Machine Learning and How to Use Them

Metrics 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.

Read More

2022年的第一个旅程

in
2022年的第一个旅程

我最终还是成功地在2022年第一天踏上了去新加坡的航班，意外之余旅行依然按照原定的计划前进着。希望2022年，我和读着这篇文章的朋友们，都可以按着自己想要走的道路前行，完成新年定下的目标，走向一个更广阔的世界。

Read More

Data Labeling of Images for Supervised Learning

in
Data Labeling of Images for Supervised Learning

At 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 More

Data Validation for Machine Learning - Paper Reading Note

in
Data Validation for Machine Learning - Paper Reading Note

This 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 More

Designing Image Acquisition for Machine Vision

in
Designing Image Acquisition for Machine Vision

At 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 More