Utility Safety Podcast - Deep Dive - Repeating Safety Successes written by David McPeak, CUSP

Utility Safety Podcast by Incident Prevention Magazine

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Understanding the Elegant Math Behind Modern Machine Learning 1:14:43

4일 전1:14:43

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1:14:43

Artificial intelligence is evolving at an unprecedented pace—what does that mean for the future of technology, venture capital, business, and even our understanding of ourselves? Award-winning journalist and writer Anil Ananthaswamy joins us for our latest episode to discuss his latest book Why Machines Learn: The Elegant Math Behind Modern AI . Anil helps us explore the journey and many breakthroughs that have propelled machine learning from simple perceptrons to the sophisticated algorithms shaping today’s AI revolution, powering GPT and other models. The discussion aims to demystify some of the underlying math that powers modern machine learning to help everyone grasp this technology impacting our lives, even if your last math class was in high school. Anil walks us through the power of scaling laws, the shift from training to inference optimization, and the debate among AI’s pioneers about the road to AGI—should we be concerned, or are we still missing key pieces of the puzzle? The conversation also delves into AI’s philosophical implications—could understanding how machines learn help us better understand ourselves? And what challenges remain before AI systems can truly operate with agency? If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits. Links: Read Why Machines Learn, Anil’s latest book on the math behind AI https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Learn more about Anil Ananthaswamy’s work and writing https://anilananthaswamy.com/ Watch Anil Ananthaswamy’s TED Talk on AI and intelligence https://www.ted.com/speakers/anil_ananthaswamy Discover the MIT Knight Science Journalism Fellowship that shaped Anil’s AI research https://ksj.mit.edu/ Understand the Perceptron, the foundation of neural networks https://en.wikipedia.org/wiki/Perceptron Read about the Perceptron Convergence Theorem and its significance https://www.nature.com/articles/323533a0…

약 1년 전 16:43

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In this episode, we delve into “Win and Win Again: The Blueprint for Repeating Safety Successes,” an insightful article by David McPeak, CUSP, CIT, CHST, CSP, CSSM, featured in Incident Prevention magazine . We explore strategies to define, reinforce, and replicate safety successes in the workplace. By understanding the WIN framework—Work, Influence, Normalize—we discuss how organizations can foster environments where desired behaviors lead to consistent, positive safety outcomes.

Read the article: https://incident-prevention.com/blog/win-and-win-again-the-blueprint-for-repeating-safety-successes/

Key Takeaways:

1.Defining Success: Success in safety is characterized by desired behaviors leading to desired results, emphasizing the importance of correct actions that prevent incidents.

2.Understanding Behavior Categories: It’s crucial to distinguish between incidents, chance occurrences, compliance, and true success to effectively address and promote safe practices.

3.The WIN Framework: Implementing the Work, Influence, Normalize approach helps in reinforcing successful behaviors and integrating them into the organizational culture.

4.Reinforcement of Positive Behavior: Consistent positive reinforcement encourages the repetition of safe behaviors, reducing the likelihood of accidents and fostering a proactive safety culture.

5.Addressing Complacency: Actively managing and providing consequences for ‘chance’ behaviors—where undesired actions don’t immediately lead to negative outcomes—is essential to prevent normalization of deviance.

#SafetySuccess #WorkplaceSafety #SafetyCulture #BehavioralSafety #IncidentPrevention

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