AI & data modeling

In this episode, we're diving into the world of AI and data modeling with a special guest, Jeff Chien, Vice President of Data and AI at CompQSoft.
Jeff shares insights into the three categories of AI, how to approach building AI solutions, and the benefits of combining AI with RPA for maximum impact. We'll also explore the concept of knowledge graphing, the challenges of implementing AI, and the future of AI, especially in relation to cybersecurity.
If your company is looking to leverage AI to solve problems, this episode is packed with practical advice and real-world examples. Plus, Jeff recommends a must-read business book and shares his thoughts on policies surrounding Generative AI.

Chapters:
00:00 - Introduction to Jeff Chien
03:10 - The Three Categories of AI
08:52 - Finding Your "How" and "Why" in AI
12:00 - Comparing Modern and Traditional AI Approaches
14:10 - Building AI Solutions for Businesses
17:20 - Understanding Knowledge Graphing
18:40 - About CompQSoft
20:45 - Building In-House AI Solutions
24:20 - Maximizing AI's Potential: Low-Hanging Fruits
25:30 - AI in Action: Real-World Use Cases
28:50 - Data Collection for Problem-Solving
32:47 - Challenges in Implementing AI
35:20 - AI’s Impact on Cybersecurity
37:48 - Combining RPA and AI: The Ultimate Power
40:40 - The Future of AI
42:45 - Policies Around Generative AI
44:40 - Jeff's Book Recommendation: How Is Your Ship Sailing?
45:50 - Where to Find Jeff

Jeff’s book recommendation:
"It’s Your Ship: Management Techniques from the Best Damn Ship in the Navy" by Captain D. Michael Abrashoff
https://shorturl.at/z41EP

Where to find Jeff:
Jeff’s email: [email protected]
Jeff’s LinkedIn profile:   / actionableinsights  

Key takeaways from this discussion:
Jeff Chien has a background in AI and has used it in various sectors, including federal government, healthcare, and insurance.
• AI can be categorized into three categories: generative AI, automation, and insights.
• Jeff Shen has utilized all three categories of AI in his work, combining generative AI, RPA, and prescriptive analytics to achieve results.
• Traditional approaches to data analysis and AI are often siloed and lack synergy, leading to inefficiency and slower progress.
• Skill set shortage is a growing problem, so AI can help navigate situations for company success.
• Leveraging AI capabilities from vendors or building an in-house solution are two options for companies.
• The future of AI will automate the coding and cleaning of data, allowing for better problem-solving and improved productivity.
• Generative AI should be used to generate insights and solve problems, rather than generating content or made-up answers.
• Companies should focus on empowering their employees to use AI to collaborate and solve problems effectively.
• Data integrity and data access are important in cybersecurity, and AI can play a significant role in addressing these challenges.

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