“In the case of CheXnet, the research team led by Stanford adjunct professor Andrew Ng, started by training the neural network with 112,120 chest X-ray images that were previously manually labeled with up to 14 different diseases. One of them was pneumonia. After training it for a month, the software beat previous computer-based methods to detect this type of infection. The Stanford Machine Learning Group team pitted its software against four Stanford radiologists, giving each of them 420 X-ray images. This graphic shows how the radiologists–represented by the orange Xs–did compared to the program–represented by the blue curve.”
Some of my favorite work with cities and companies is around the Internet of Things. There are many modern day challenges that are best solved with technology. However, the question that we face is how best to solve those challenges.
For example, some cities, are exploring adding sensors to parking spaces to help inform motorists about where parking spots are available. This type of endeavor will be expensive in so many different ways. Any city looking to do this, will likely need to architect some type of system that looks like the following:
On the other hand, Google, is leveraging its maps data too help predict where you are most likely to find parking. This is an impressive use of machine learning instead of on-the-ground sensors for smart parking.