Author: uasysadm

The UAFS Artificial Intelligence Lab hosted a monthly meeting where faculty, members, and invited guests contemplated the future of intelligence augmentation and artificial intelligence. As the field of artificial intelligence continues to grow, numerous challenges remain unanswered, and the research lab seeks to explore some of these problems and formulate potential solutions.

Individuals in the meeting discussed the role of artificial intelligence in society from automation to assistive technology that facilitates human judgment and decision-making. Many members debated the role of artificial intelligence in mitigating potential threats in future pandemics. In addition, natural language processing, machine learning, and deep neural networks have demonstrated recent success in the medical field by improving operations and driving down costs.

Given the breadth of industries that have been affected by artificial intelligence, members considered what they feel have been the most impacted by AI and related technology. The challenge in this task is determining how to measure impact. While there have been many significant advances, most lab members cited successes that were beneficial toward operational effectiveness during the pandemic.

Several other major topics were discussed at the event, ranging from future use cases of intelligence augmentation to advanced applications of artificial intelligence. Members were asked to reflect on possible directions of AI research in the near future. Several members cited global supply chain concerns and provided hypotheses on how artificial intelligence could improve farming communities in the local region and state.

Fake news is a widespread problem that affects people daily. The widespread adoption of social media facilitates the dissemination of articles with the intention to deceive readers. The goal of our work is to automatically detect fake news articles using deep learning while improving upon existing approaches. 

Several advancements in image processing research have greatly improved our ability to construct models capable of image recognition and classification with low latency. We built a prototype autonomous vehicle that is capable of automatically detecting license plates on campus using IoT sensors and hardware.

Faculty members from the Department of Computer Science and Engineering were awarded a research grant to explore the rapidly growing area of fake news and disruptive content. The rise and popularity of social media services have largely contributed to the spread of content that can be factually incorrect for a number of reasons, including comedic work such as satire, or deceptive work where authors seek to intentionally mislead users. Researchers will explore new approaches for the algorithmic detection of fake news content through deep learning neural networks.