Jianhao Ma's IMA Blog

 A3: Applications of Machine Learning models Jianhao Ma Response to PoseNet's Data & Model Biography To be more specific the biography provided is for MobileNet. However, I did learn something about PoseNet because it's the sub model of MobileNet.  One important thing I noticed is that the model is optimized for devices with limited performance, ones that can be easily embedded and require less power. Therefore, it can be applied to outdoor activities in daily life scenarios. Moreover, PoseNet does a really good job in inspecting and classifying people's poses and gestures. All of the followings inspired me to make my project this week. Have A Safe Ride My intention is to make a demo of an application that detects motorcycle hand signals.  My plan for the application is to embed them in auto-driving and safety technologies on automobiles. It enables cars to recognize the hand signals of bikers in front of them and responses safely based on that. I built my project on ...

2A: Excavating AI, Teachable Machine Project Jianhao Ma Moral Machines The reading "Excavating AI" brings up an idea that the tasks of training AI should not just be making them smarter, but to apply political consideration and moral standard as well, especially when related to people. I would not care about such idea years ago, because I thought that technical breakthroughs is the one and foremost priority in emerging technology industries, why bother? However, when the application of machine learning started entering our lives, I changed my mind. Everyday, there are many information processed/filtered by machine learning tools being fed to us, textual or non textual. They could be the advertisement you watch on social media, the pop-up news on your phone. In other words, many of the lower products of machine learning are mediums. Therefore, they have the inherent nature of being political biased and ideologically influential. The fantasy that "technologies should be un...

Week2: ImageNet and MobileNet  Jianhao Ma ImageNet My initial impression with ImageNet is about how slow it is. But I thought that makes sense regarding it is a non-profitable research project with a huge dataset. I tried to find how big the dataset is and figured out that the statistics hasn't been updated for 10 years. Later I realized that not only the search engine lacks consistency, the tree view is also taking forever.  As I browsed through ImageNet, I started to have a sense that it's one of those early Image Classification project that's been forbidden by time. Can't neglect that there are many privacy concerns, especially under "person" synsets. I think that they would have be more careful when selecting synsets of illegal occupations and minority groups if it's a recent project. But I wouldn't blame that on ImageNet because that's the common theme of fabricating any datasets with all resources on the Internet. It's too much works for ...