Carnegie Mellon University’s Manuela Veloso, the Herbert A. Simon University Professor describes Machine learning “as a fascinating field of Artificial Intelligence (AI) research and practice where scientists investigate how computer agents can improve their perception, cognition, and action with experience. Machine Learning is about machines improving from data, knowledge, experience, and interaction. Machine Learning utilizes a variety of techniques to intelligently handle large and complex amounts of information build upon foundations in many disciplines, including statistics, knowledge representation, planning and control, databases, causal inference, computer systems, machine vision, and natural language processing. AI agents with their core at Machine Learning aim at interacting with humans in a variety of ways, including providing estimates on phenomena, making recommendations for decisions, and being instructed and corrected. Machine Learning can impact many applications relying on all sorts of data, any data that is recorded in computers, such as health data, scientific data, financial data, location data, weather data, energy data, etc. As our society increasingly relies on digital data, Machine Learning is crucial for most of our current and future applications.”
The world is being reshaped by machine learning. Data collected through sensors and novel technologies at many scales is being leveraged to make decisions and infer relationships in every discipline and application. But it takes the right techniques and tools to do so effectively.
It is interesting that on this episode, we are joined by John Kitchin, a chemical engineering expert who is using machine learning to develop new tools to change the way that research is being conducted.
His work with machine learning focuses on creating tools such SCIMAX - - open source software that improves data sharing and efficiency in research and academia. The software uniquely integrates data processing and analysis into plain text. Dr. Kitchin is very interested in creating tools, augmenting research with data tools and teaching students about machine learning as an integrated part of the research process.