Episode 4. The AI Fundamentalists welcome Christoph Molnar to discuss the characteristics of a modeling mindset in a rapidly innovating world. He is the author of multiple data science books including Modeling Mindsets, Interpretable Machine Learning, and his latest book Introduction to Conformal Prediction with Python. We hope you enjoy this enlightening discussion from a model builder's point of view.

To keep in touch with Christoph's work, subscribe to his newsletter Mindful Modeler - "Better machine learning by thinking like a statistician. About model interpretation, paying attention to data, and always staying critical."

Summary

• Introduction. 0:03
  • Introduction to the AI fundamentalists podcast.
  • Welcome, Christopher Molnar
• What is machine learning? How do you look at it? 1:03
  • AI systems and machine learning systems.
  • Separating machine learning from classical statistical modeling.
• What’s the best machine learning approach? 3:41
  • Confusion in the space between statistical learning and machine learning.
  • The importance of modeling mindsets.
  • Different approaches to using interpretability in machine learning.
  • Holistic AI in systems engineering.
• Modeling is the most fun part but also the beginning. 8:19
  • Modeling is the most fun part of machine learning.
  • How to get lost in modeling.
• How can we use the techniques in interpretable ML to create a system that we can explain to stakeholders that are non-technical? 10:36
  • How to interpret at the non-technical level.
  • Reproducibility is a big part of explainability.
• Conformal prediction vs. interpretability tools. 12:51
  • Explanability to a data scientist vs. a regulator.
  • Interoperability is not a panacea.
  • Conformal prediction with Python.
  • Roadblocks to conformal prediction being used in the industry.
• What’s the best technique for a job in data science? 17:20
  • The bandwagon effect of Netflix and machine learning.
  • The mindset difference between data science and other professions.
• Machine learning is always catching up with the best practices in the industry. 19:21
  • The machine learning industry is catching up with best practices.
  • Synthetic data to fill in gaps.
  • The barrier to entry in machine learning.
  • How to learn from new models.
• How to train your mindset before you start modeling. 23:52
  • The importance of simplifying two different mindsets.
  • Introduction to conformal prediction with Python.

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Episode 3.  Get ready because we're bringing stats back! An AI model can only learn from the data it has seen. And business problems can’t be solved without the right data. The Fundamentalists break down the basics of data from collection to regulation to bias to quality in AI. 

• Introduction to this episode
  • Why data matters.
• How do big tech's LLM models stack up to the proposed EU AI Act?
  • How major models such as Open AI and Bard stack up against current regulations.
  • Stanford HAI - Do Foundation Model Providers Comply with the Draft EU AI Act?
  • Risk management documentation and risk management.
• The EU is adding teeth outside of the banking and financial sectors now.
  • Time - Exclusive: OpenAI Lobbied the E.U. to Water Down AI Regulation
• Bringing stats back: Why does data matter in all this madness?
  • How AI is taking us away from human intelligence.
  • Having quality data and bringing stats back!
  • The importance of having representative data, sampling data
• What are your business objectives? Don’t just throw data into it.
  • Understanding the use case of the data.
  • GDPR and EU AI regulations.
  • AI field caught off guard by new regulations.
  • Expectations for regulatory data.
• What is data governance? How do you validate data?
  • Data management, data governance, and data quality.
  • Structured data collection for financial companies.
• What else should we learn about our data collection and data processes?
  • Example: US Census data collection and data processes.
  • The importance of representativeness and being representative of the community in the census.
  • Step one, the fine curation of data, the intentional and knowledgeable creation of data that meets the specific business need.
  • Step two, fairness through awareness.
• The importance of data curation and data selection in data quality.
  • What data quality looks like at a high level.
  • Rights to be forgotten.
• The importance of data provenance and data governance in data science.
  • Synthetic data and privacy.
• Data governance seems to be 40 % of the path to AI model governance. What else needs to be in place?
  • What companies are missing with machine learning.
  • The impact that data will have on the future of AI.
  • The future of general AI in the future.

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Good AI Needs Great Governance

Define, manage, and automate your AI model governance lifecycle from policy to proof.

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Truth-based AI: Large language models (LLMs) and knowledge graphs - The AI Fundamentalists, Episode 2

Show Notes

• What’s NOT new and what is new in the world of LLMs. 3:10
  •  Getting back to the basics of modeling best practices and rigor.
• What is AI and subsequently LLM regulation going to look like for tech organizations? 5:55
  • Recommendations for reading on the topic.
  • Andrew talks about regulation, monitoring, assurance, and alarm.
• What does it mean to regulate generative AI models? 7:51
  • Concerns with regulating generative AI models.
  • Concerns about the call for regulation from Open AI.
• What is data privacy going to look like in the future? 10:16
  • Regulation of AI models and data privacy.
  • The NIST AI Risk Management Framework.
  • Making sure it's being used as a productivity tool.
  • How it's different from existing processes.
• What’s different about these models vs old models? 15:07
  • Public perception of new machine learning models vs old models.
  • Hallucination in the field.
• Does the use of chatbots change the tendency toward hallucinations? 17:27
  • Bing still suffers from the same problem with their LLMs.
  • Multi-objective modeling and multi-language modeling.
• What does truth-based AI look like? 20:17
  • Public perception vs. modeling best practices
  • Knowledge graphs vs. generative AI: ideal use cases for each
• Algorithms have a really interesting potential application which is a plugin library model. 23:00
  • Algorithms have an interesting potential application.
  • The benefits of a plugin library model.
• What’s the future of large language models? 25:35
  • Practical uses for ML and knowledge base knowledge databases.
  • Predictions on ML and ML-based databases.
  • Finding a way to make LLM useful.
  • Next episodes of the podcast.

What did you think? Let us know.

Good AI Needs Great Governance

Define, manage, and automate your AI model governance lifecycle from policy to proof.

Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

• LinkedIn - Episode summaries, shares of cited articles, and more.
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The AI Fundamentalists - Ep1 

Summary

• Welcome to the first episode. 0:03
  • Welcome to the first episode of the AI Fundamentalists podcast.
  • Introducing the hosts.
• Introducing Sid and Andrew. 1:23
  • Introducing Andrew Clark, co-founder and CTO of Monitaur.
  • Introduction of the podcast topic.
• What is the proper rigorous process for using AI in manufacturing? 3:44
  • Large language models and AI.
  • Rigorous systems for manufacturing and innovation.
• Predictive maintenance as an example of manufacturing. 6:28
  • Predictive maintenance and predictive maintenance in manufacturing.
  • The Apollo program and the Apollo program.
• The key things you can see when you’re new to running. 8:31
  • The importance of taking a step back.
  • Getting past the plateau in software engineering.
• What’s the game changer in these generative models? 10:47
  • Can Chat-GPT become a lawyer, doctor, or teacher?
  • The inflection point with generative models.
• How can we put guardrails in place for these systems so they know when to not answer? 13:46
  • How to put guardrails in place for these systems.
  • The concept of multiple constraints.
• Generative AI isn’t new, it’s embedded in our daily lives. 16:20
  • Generative AI is not new, but not a new technology.
  • Examples of generative AI.
• The importance of data in machine learning. 19:01
  • The fundamental building blocks of machine learning.
  • AI is revolutionary, but it's been around for years.
•  What can AI learn from systems engineering? 20:59
  • Nasa Apollo program, systems engineering.
  • Systems engineering fundamentals world, rigor, testing and validating.
  • Understanding the why, data and holistic systems management.
  • The AI curmudgeons, the AI fundamentalists.

What did you think? Let us know.

Good AI Needs Great Governance

Define, manage, and automate your AI model governance lifecycle from policy to proof.

Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

• LinkedIn - Episode summaries, shares of cited articles, and more.
• YouTube - Was it something that we said? Good. Share your favorite quotes.
• Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.