A study presented at the Radiological Society of North America (RSNA) 2021 Annual Meeting demonstrates the importance of using racially diverse datasets while training artificial intelligence (AI) systems to ensure fair outcomes. "As the rapid development of deep learning in medicine continues, there are concerns of potential bias when interpreting radiological images," the authors wrote. "As future medical AI systems are approved by regulators, it is crucial that model performance on different racial/ethnic groups is shared to ensure that safe and fair systems are being implemented." The findings were presented by Brandon Price, a medical student at Florida State University College of Medicine in Tallahassee. Many studies have shown that deep learning systems are subjective in their interpretation of data.

This week researchers from the University of Florida published findings from an archaeological project that sheds new light on what life was like in North America before Christopher Columbus arrived. Using drones to scan the coastline of northwestern Florida, researchers discovered evidence of a settlement dated between 900 to 1200 AD. They discovered evidence of a settlement that could have supported between 200 and 300 people, who they believe worked to create one beads and decorative ornaments from shells that played an important role in Mississippian culture at the time. The settlement was discovered on Raleigh Island, halfway between Tampa and Tallahassee on Florida's northwestern coast, just outside the Cedar Keys Wildlife Refuge. The drone that discovered the settlement was equipped with a LiDAR system, according to a report by ArsTechnica.

Students from Marjory Stoneman Douglas High School walk through the Florida state Capitol in Tallahassee. Schools are increasingly turning to artificial intelligence-backed solutions to stop tragic acts of student violence such as the shooting at the Marjory Stoneman Douglas High School in Parkland, Florida, a year ago. Bark Technologies, Gaggle.Net, and Securly Inc. are three companies that employ AI and machine learning to scan student emails, texts, documents, and in some cases, social media activity. They look for warning signs of cyber bullying, sexting, drug and alcohol use, depression, and to flag students who may pose a violent risk not only to themselves, but classmates. When potential problems are found, and depending on the severity, school administrators, parents -- and under the most extreme cases -- law enforcement officials, are alerted.

Paul Zikopoulos, vice president of Big Data & Cognitive Systems, IBM speaks during the annual Tallahassee Chamber Conference at the Omni Amelia Island Plantation on Saturday, Aug. 11, 2018. AMELIA ISLAND – Tech years are a lot like dog years -- they go very quickly, said one of the nation's top Big Data experts during the Tallahassee Chamber of Commerce Conference on Saturday. Companies can transform their delivery services and business models by the "one percent rule" over time and reap catalytic results, said Paul Zikopoulos, vice president of Big Data and Cognitive Systems for IBM. Zikopoulos is the author of 19 books and a leading authority on Big Data. According to Leading Authorities Inc., he was named one of the "50 Big Data Twitter Influencers" by SAP, Zikopoulos has served as a consultant for 60 Minutes and multiple universities and has been named an expert on big data by publications such as Big Data Republic, Technopedia, and Analytics Week.

The progress of technology in leaps and bounds has resulted in the generation of an enormous amount of digital data in the modern era. Against this backdrop, artificial intelligence (AI) has emerged as a useful mechanism to automatically organize and categorize data and to leverage useful patterns in the data to make intelligent predictions for the future from past observations. In this way, we can use AI to help older adults and others. The most common kind of AI algorithms is supervised machine learning, which involves learning from labeled data. However, while gathering a large amount of unlabeled data is cheap and easy, hand-labeling the data is an expensive process in terms of time, labor and human expertise.

That's the advice of scientists who claim our instinctive reactions act as a'red signal' that stops our brain from making mistakes. Our'gut feelings' are part of an elaborate protective system that prompts us to slow down and evaluate a situation, or avoid it completely, scientists said. If you don't make decisions by'going with your gut', you may want to start now. The research, from Florida State University in Tallahassee, advances our understanding of the gut-to-brain circuit - a poorly understood part of the body. 'The neuroscience of gut feelings has come a long way in my lifetime,' said study coauthor and Florida State neuroscientist Dr Linda Rinaman.

Dr. Mark Jack is an experienced Data Scientist and Associate Professor of Physics at Florida A&M University with several years of experience in computational modeling in particle physics, neuroscience, nanoscience and high-performance computing. He is a certified trainer in machine learning and statistical programming in R. He has spoken in several Data Science conferences which includes the Global Big Data Conferences in Tampa, Fl and Atlanta, GA. The creation of a corpus of documents from three text data files mostly relies on the use of the library'quanteda' in R. It allows to quickly tokenize the corpus of documents to remove text features such as punctuation, numbers, white space, lowercase words etc. The processing time for the complete text data is considerable.

I was meeting with the Tallahassee Chamber of Commerce's Communications Committee; there was some brainstorming about session ideas for the upcoming Chamber Conference. There were some thoughts thrown out, and quite a few comments were made. Then someone said, "how about automation and artificial intelligence." Suddenly, a surge of ideas and thoughts hit the room like a vicious uppercut from Mike Tyson circa 1999. All industries went into the mix: retail, auto, construction, medical and legal.

A recent study led by a Tallahassee-based Florida State University psychology researcher investigated whether artificial intelligence can assist in suicide prevention. The researchers, led by Jessica Ribeiro, PhD, identified the EHRs of 2 million Tennessee patients, more than 3,200 of whom had attempted suicide. The researchers used machine learning on these patients' medical histories to determine which combination of risk factors most accurately predicted future suicide attempts. The machine learning algorithm could predict suicide attempts with between 80 percent and 90 percent accuracy as far as two years into the future. The algorithm's accuracy increased based on closeness to the time of the suicide attempt; accuracy was as high as 92 percent when identifying general hospital patients at risk for a suicide attempt within one week.

In 2012, Google dropped off two self-driving cars near the steps of Florida's statehouse in Tallahassee. They were there at the behest of a freshly elected Florida state senator, Jeff Brandes. Lawmakers, some with families in tow, stepped into Google's…