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The Applications of AI to Improve Safety Performance
USING ARTIFICIAL INTELLIGENCE IN REAL-WORLD APPLICATIONS TO MAKE OUR WORKPLACES SAFER
AI’s history is marked by periods of intense optimism, major setbacks, and steady progress. From its conceptual beginnings to its 20th Century resurgence with the development of expert systems, and then the rapid advancements in deep learning today, AI has evolved from a theoretical idea into a transformative technology with real-world applications to make our workplaces safer.
Join us in this session to explore the application of AI to:
- Retinal Image Analysis.
- Voice recognition to assess impairment due to fatigue, drugs and alcohol.
- Reduce impairment-induced safety incidents.
- Assessing employee burnout.
- Overall workplace safety and improved safety management systems.
The history of Artificial Intelligence (AI) is a long and quite remarkable journey. Some credit its roots in Charles Babbage’s Analytical Engine, a mechanical computer in the 1830s that laid the groundwork for computational theory. IBM’s Deep Blue was an application that famously defeated world chess champion Garry Kasparov, marking a significant achievement in AI. Nowadays we are all familiar OpenAI’s GPT-3 that has shown an impressive ability to understand and generate human-like text. In addition to all these other applications, AI has also made significant contributions to improving workplace safety across various industries.
March 27, 2025 | 12:00PM Noon
Free Lunch and Learn Webinar
AI has many applications in improving workplace safety including improved predictive analytics for risk prevention, AI-based training involving simulation, enhanced safety protocols including the use of computer vision and natural language processing to assess workplace hazards, and the use of AI analysis of retinal and vocal patterns to assess impairment, fatigue, and employee burnout. The applications are increasing daily and the rate of change in AI applications is best described as highly dynamic and accelerating due to continuous advancements in research, increased computational resources, data availability, and cross-industry adoption.