Welcome to the Impact of AI: Explored podcast. In this episode
James and Gerjon have a awesome guest: Dr. Nikita Golovko - Certified ISAQB CPSA-F Architect/Cybersecurity expert & PSSE (ISC2 CC) (https://www.linkedin.com/in/dr-nikita-golovko/)
In this conversation, we discuss the challenges and nuances of implementing AI in industrial settings. We look at the importance of bridging the gap between AI technology and practical application on the factory floor, highlighting the need for translators who can communicate between data science and operational teams. The discussion also covers the transition from lab-based AI to real-world applications, the significance of team structure in successful AI projects, and the responsibilities of engineers in ensuring AI systems are safe, transparent, and sustainable. We share insights on generative AI, technical debt, and the future of AI in industrial automation, advocating for a collaborative approach that includes human oversight in decision-making processes.
Takeaways
* Finding the right talent to bridge AI and operational gaps is crucial.
* AI solutions often fail when transitioning from lab to real-world environments.
* Technical debt can be identified by signals like fear of touching code.
* Generative AI is not yet suitable for industrial applications due to unpredictability.
* AI should enhance human decision-making, not replace it.
* Team structure significantly impacts the success of AI projects.
* Sustainability in AI means retraining models with new teams over time.
* Transparency and explainability are essential for AI systems.
* AI can assist in coding but should not replace foundational knowledge.
* Documenting decisions is key to managing technical debt.
Chapters
00:00 AI agents in industrial environments
01:33 introduction
02:18 Listener question: Bridging the Gap in Industrial AI Hiring
06:47 Challenges of Deploying AI in Real-World Environments
09:38 Technical Debt and Its Early Warning Signs
12:18 Generative AI vs. Classical Machine Learning in Industry
14:51 The Role of Predictability in AI Models
17:29 Local vs. Centralized AI Models in Production
20:14 Architectural Challenges in AI Implementation
22:55 Sustainability in AI System Design
25:52 The Future of AI in Industrial Applications
26:54 The Role of AI in Industrial Automation
32:00 AI as an Advisor: The Human Element
36:39 AI-Assisted Coding: Benefits and Risks
40:08 Responsibility and Ethics in AI Development