Machine Learning and the Rise of Data-Driven AI: What’s All the Fuzz About?
Dr. Eitel J. M. Lauría
Monday, 7:30 PM
In cyberspace. To obtain the URL for this video conference, you must register to attend through the Meetup.com announcement. Meetup.com/ACM-Poughkeepsie. Once you've done so, you'll be able to access the Zoom link on Meetup's page after 6:00 PM the night of this event.
This program is free and open to the public. Because our meeting is virtual, we will not hold our normal dinner beforehand at the Palace Diner.
For further information, go to Pok.ACM.org (QR code below):
About the Topic
Data-driven artificial intelligence refers to the use of data to train and improve pattern recognition models. These models are then used to make predictions or decisions without being explicitly programmed to do so. In data-driven AI, large amounts of data are fed into the model, and the model "learns" from this data by adjusting its parameters to optimize a specific performance metric. This process is known as training the model. Once the model is trained, it can be used to make predictions or decisions on new, unseen data. This new artificial intelligence has, for the most part, moved away from the symbolic and logical manipulation of knowledge that was the landmark of the 70s and 80s. Progress in the field of data-driven AI has been breathtaking in recent years, with fantastic improvement in image recognition, speech recognition, and natural language processing. This talk provides a survey of machine learning methods and techniques, considering both structured and non-structured (perceptual) data. It also lays out some insights on the advent of large language models and some of the challenges facing data-driven AI.
About the Speaker
Dr. Eitel J. M. Lauría is a Professor of Data Science & Information Systems and the Director of Graduate Programs at the School of Computer Science & Mathematics. His broad research interests in data science cover the fields of learning analytics, applied machine learning and predictive modeling, data quality and probabilistic expert systems (Bayesian networks). Prof. Lauría’s research has been published in a number of prestigious journals, including Decision Support Systems, the European Journal of Operational Research, the ACM Journal of Data and Information Quality, Expert Systems with Applications, and the Journal of Learning Analytics. He is co-author of a textbook on data and information quality published by MIT/IQ. Dr. Lauría is the Lead Data Scientist of the Learning Analytics initiative at Marist College. Marist College has been recognized by premier technology organizations for their work in the learning analytics space, including awards from Campus Technology and Computerworld. Dr. Lauría has extensive experience in the IT industry, having consulted with companies such as IBM, Microsoft, Exxon Mobil, Reuters, and GE. He holds an Electrical Engineering degree from University of Buenos Aires, an MBA from Universidad del Salvador, both in Argentina, and a Ph.D. in Information Science from University at Albany, SUNY. Dr. Lauría is the 2015 recipient of the Board of Trustees Distinguished Teaching award at Marist College.
To Print this Announcement
- Print this webpage.
- For a one-page detailed announcement, print this PDF.
- For a one-page brief announcement, print this PDF.