What we’re about
This is for professional data scientists and analysts in the Tampa Bay area interested in Analysis, Massive Parallel Computing, Natural Language Processing, Machine Learning, Data Mining, and the supporting technologies and practices. We pull together the best minds in the region to discover and discuss the trends in our technical, methodological, and professional ecosystems. We are dedicated to establishing the Tampa Bay region as a center of excellence for interdisciplinary information analytics.
This group is in part sponsored by the Microsoft Data Science User Group Sponsorship Program, Wolters Kluwer, and CGRII. Microsoft, Wolters Kluwer, and CGRII are not responsible for content on this website or activities that take place at user group meetings.
We also are supporters of the Suncoast Developers Guild and adhere to their code of conduct. In addition to this code of conduct, we also encourage code attribution.
In order to ensure proper credit is given to developers or development teams, an effort should be made to indicate attribution of programs, scripts, and code snippets. This should include, but is not limited to: name of person/group; location of source (URL, event name/location); date/time of information. This ensures that provenance is maintained and that all parties involved in the collaborative effort are noted for their contributions to the best of one’s knowledge.
Upcoming events (4+)
See all- The Intelligence Life Cycle and the Limitations of AINeeds location
We'll gather beginning at 6:30 in the Alessi Kitchen (La Cucina) for some food and non-alcoholic beverages and convene in the meeting room about 7.
“The Intelligence Life Cycle and the Limitations of AI”
1. Abstract
What is the Intelligence Life Cycle? How do we define Data, Information, and Knowledge? Are they the same? These terms have been used interchangeably, but while they are close, they are not the same.
The Intelligence Life Cycle (ILC) consists of four blocks: “Data, Information, Knowledge, and Wisdom.”
This presentation offers clear, precise, and simple definitions of these terms using an axiomatic model that allows us to show the scope and limitations of AI. The ILC model helps explain some of the pitfalls when addressing Big Data. A brief exposition of the major developments of AI and Data is also discussed.
How are advances in LLM and, in particular, in GPT going to impact IT professionals? What is the minimum knowledge of data processing and analysis that the knowledge workers of the 21st century need to remain competitive?
We are witnessing a shift from Know-How to the What and Why. In the era of automation and AI, we need more WISDOM than KNOWLEDGE.
Objective 1 - Axiomatic hypothesis to demonstrate the Scopes and Limitations of AI
Objective 2 - Eliminate the confusion regarding the use of the terms Data, Information, and Knowledge, which, while similar, are not the same.
Objective 3 - Explain and define the different blocks of the ILC: Data, Information, Knowledge, Wisdom, and the role of AI in the ILC.
Objective 4 - Using the ILC model, present my theory of The Intelligence Life Cycle.2. BIO
http://www.linkedin.com/in/FrankQuintanaDataArchitect
Doctor Quintana has been involved in optimization and mathematical modeling since the 90s. He is a former IT University professor at the Technological University of America in South Florida where he taught Databases, Data Mining, and Knowledge Discovery. He worked as a research engineer at Memorial University of Newfoundland. He also worked as a consultant in the role of a technical team leader and software architect for EDS. He was one of the software architects who built and designed "The Benefit System for the Veteran Affairs of Canada."
