Visual RecognitionAn AI that is learning to identify pedestrians on a street is trained with 2 million short videos of street scenes from self-driving cars. Some of the videos contain no pedestrians at all while others have up to 25. A variety of learning algorithms are trained on the data with each having access to the correct answers. Each algorithm develops a variety of models to identify pedestrians in fast moving scenes. The algorithms are then tested against another set of data to evaluate accuracy and precision.
SortingA robot is learning to sort garbage using visual identification. It sits all day picking out recyclable items from garbage as it passes on a conveyor belt. It places items such as glass, plastic and metal into 12 bins. Each item is labeled with an identification number on a sticker. Once a day, human experts examine the bins and inform the robot which items were improperly sorted. The robot uses this feedback to improve.
Decision SupportAn AI is learning to estimate investing risk. It is fed a large number of trades that real investors made and asked to estimate a risk/reward ratio for each trade based on company fundamentals, price and other factors such as volume. The estimated risk/reward ratio is then compared to the historical results of the trade at a variety of time intervals such as a day or a year.
|Overview: Supervised Learning|
Machine learning based on training data that includes correct answers.