Creating a smart diagnosis system
There are many cases in which experts are required to carry out diagnosis, i.e., given a list of observed symptoms, find the problem that is causing these symptoms. Examples include medical diagnosis, where a healthcare professional needs to discern the ailment causing a set of symptoms, and white goods repair, where a technician must decide on the repair procedure for a dishwasher. In many cases, making such diagnosis requires significant expertise, acquired over years of experience. Therefore, any system which can help professionals make such diagnosis decisions would be of great benefit.
Applying analytical models to such tasks seems natural. However, applying such techniques face a unique challenge in that they must be able to incorporate unstructured information.
In this talk, I will describe a decision support technology created by IBM that helps diagnose problems. I will describe how combining a variety of advanced AI and analytical techniques can result in a formal probabilistic model that incorporates both the structured and unstructured information sources into a decision support system, or, in other words how we can teach Watson to repair electronic appliances such as washing machines.
Dr. Segev Wasserkrug is a Senior Technical Staff Member at the IBM Haifa Research Lab, and the technical leader of the Cognitive IoT work in the IoT and Mobile Platforms Area in the lab. Segev has over fourteen years practical experience in leading, developing and applying advanced optimization and analytical techniques to customer problems in a varietyof domains, including workforce, logistics, scheduling, and water and wastewater operations. Over the past two years, Segev has worked on enhancing IoT and Mobile driven solutions with cognitive capabilities, focusing on automated reasoning, semantic knowledge representation, and natural human/machine interaction.
Segev has a strong background in a variety of areas including probabilistic reasoning, machine learning, optimization, simulation, stochastic modeling, and computer science. Segev received his Ph.D. in information systems engineering, and his M.Sc. and B.A.in computer science from the Technion - Israel Institute of Technology. Segev has numerous academic publications and patents.