Summary In this code pattern, learn how to use AutoAI to automatically generate a Jupyter Notebook that contains Python code of a machine learning model. Then, explore, modify, and retrain the model pipeline using Python before deploying the model in IBM Watson® Machine Learning using Watson Machine Learning APIs. Description AutoAI is a graphical tool…
Summary In this code pattern, learn how to use IBM® Watson Knowledge Studio to train a custom machine learning model to drive a decision-making process of identifying dangerous situations. Description Want to develop an application or solution that can reduce the response time of first responders? This code pattern explains how to create a danger…
Note: This code pattern uses Watson Discovery V1 and will not work with Discovery V2. However, you can still use it to learn the Discovery features. Future plans include updating the code pattern to work with Discovery V2. Summary This code pattern explains how to create an application that you can use to search for…
Summary In this developer code pattern, we will analyze insurance claims data and determine whether there are any fraudulent claims filed by users. We do this by analyzing data patterns using IBM Db2 Graph. The query extracts claims from the database and analyzes them using the visualization library. Analysts from insurance companies can visually analyze…
The direct conversion of methane gas to liquid methanol at the site where it is extracted from the Earth holds enormous potential for addressing a number of significant environmental problems. Developing a catalyst for that conversion has been a critical focus for Associate Professor Heather Kulik and the lab she directs at MIT. As important…
imodels: A python package with cutting-edge techniques for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. Recent machine-learning advances have led to increasingly complex predictive models, often at the cost of interpretability. We often need interpretability, particularly in high-stakes applications such as medicine, biology, and political science (see here and here…
Posted by Chelsea Finn, Research Adviser and Eric Jang, Senior Research Scientist, Robotics at Google People can flexibly maneuver objects in their physical surroundings to accomplish various goals. One of the grand challenges in robotics is to successfully train robots to do the same, i.e., to develop a general-purpose robot capable of performing a multitude…
It’s one thing to know whether an individual customer is intrigued by a new mattress or considering a replacement for their sofa’s throw pillows; it’s another to know to how to move these people to go ahead and make a purchase. When deployed strategically, artificial intelligence (AI) can be a marketer’s trusted customer experience ally—transforming…
In fall 2020, MIT’s School of Engineering and Takeda Pharmaceuticals Company Limited launched the MIT-Takeda Program, a collaboration to support members of the MIT community working at the intersection of artificial intelligence and human health. Housed at the Abdul Latif Jameel Clinic for Machine Learning in Health, the collaboration aims to use artificial intelligence to…
While working toward her dissertation in computer science at MIT, Marzyeh Ghassemi wrote several papers on how machine-learning techniques from artificial intelligence could be applied to clinical data in order to predict patient outcomes. “It wasn’t until the end of my PhD work that one of my committee members asked: ‘Did you ever check to…