Jun 12, 2023
Matthew Renze is a data science consultant, author, and public speaker. He is the founder of Renze Consulting, an AI consulting company that has trained over 500,000 software developers and IT professionals. His clients range from small tech start-ups to Fortune 500 companies. He is also the President of Serenze Global, a 501(c)(3) non-profit organization dedicated to improving access to technology education for under-represented individuals by empowering the next generation of tech community leaders. Matthew is currently working on his Master’s degree in Artificial Intelligence with a Data Science specialization at Johns Hopkins University. He currently has double degrees in Computer Science and Philosophy with a minor in Economics from Iowa State University. He is a Microsoft MVP in AI, an ASPinsider, and an author for Pluralsight, Udemy, and Skillshare. His interests include AI, ML, data science, mindfulness, technology education, and tech community leadership.
Topics of Discussion:
[1:41] How Matthew got into software development and eventually AI, rebranding himself as a data scientist and then AI consultant.
[5:40] Matthew is getting his Master’s Degree in Artificial Intelligence.
[6:04] How can we demystify AI and all the buzzwords we use?
[9:13] Are there any current products that meet the definition of strong general AI?
[11:03] What does weak general AI mean?
[13:51] For .NET developers, what can they actually do today, with this latest generation of generative AI?
[17:02] What are some examples in AI right now that Matthew has come across that clearly violate any standard of ethical boundary?
[19:00] A few of the issues with AI currently or ways that AI systems are being abused:
Algorithmic bias and discrimination
Lack of trust in AI
Recommendation engines (rabbit holes)
Lack of basic AI literacy
[22:00] Is it even possible for these models not to be biased?
[22:35] We have to make sure that we’ve got balanced data sets in order to get the models to train properly.
[25:41] How do we regulate ethics?
[27:55] The distinction between using supervised learning, and then self-supervised learning, or reinforcement learning.
[39:20] How we can prevent deep fake videos.
[42:01] It’s important to get these tools in the hands of the right people, provide education, and move forward mindfully.
[47:02] Curating your own algorithm and handling information overload.
Mentioned in this Episode:
Clear Measure, Inc. (Sponsor)
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Architect Tips — Video podcast!
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