AI
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!
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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 MoreThere are two types of Full Stack Machine Learning Engineering in my mind — one vertical and one horizontal
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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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Pinterest’s Visual Search
In this report, we use an Alibaba case study to show how visual search can help E-commerce customers find appropriate products in a massive catalog. We then examine how Pinterest uses the technology as an ad platform and to match brands and products to users.
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At the Stanford AI Salon
Today I went to Stanford to attend an AI Salon session hosted by the Stanford AI Lab. The topic of the salon today was "Deep Reinforcement Learning for Real World Systems". The speakers were Prof. Sergey Levine & Prof. Mykel Kochenderfer.
Read MoreThe story of Threads show us an example that the presence of human touches and deep thoughts on customers’ experience should not be ignored. We should not lazily use AI as a solution for everything. The success of commerce still rely on the deep understanding on customers.
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A big theme for Google recently is the on-device machine learning applications. Last week Google officially launched the Android Pie. Inside the new mobile system, Google integrates many AI applications and open up infrastructure for the external developers.
Read MoreAutoML 让机器可以自动化地为每个新的应用场景开发机器学习模型，这是谷歌“人工智能优先”战略的重要一步。
Read MoreAutoML and other similar services do have the potential to make AI more accessible to everyone, so that we can focus on much harder problems. We are far from automating the entire pipeline of Machine Learning development.
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