The AI Fundamentalists

Join Christoph Molnar and Timo Freiesleben, co-authors of 'Supervised Machine Learning for Science,' as they dive deep into practical machine learning applications in research. They discuss the significance of tailoring evaluation metrics to enhance model performance and the pivotal role of domain knowledge in data collection. The duo also highlights strategies for measuring causality and improving robustness against distribution shifts. Finally, they tackle the challenges of reproducibility in science versus machine learning, offering insightful solutions.

Christoph Molnar, an expert in supervised machine learning, and Timo Freiesleben, a postdoctoral researcher in AI ethics, explore the intersection of machine learning and science. They discuss the skepticism scientists have towards predictive models and highlight the balance between accuracy and interpretability. The duo addresses the diverse levels of machine learning adoption across various scientific fields and the importance of domain knowledge. They also touch on how ML can enable scientists to test hypotheses and potentially discover new scientific laws.

Unlock the secrets to AI's modeling paradigms. We emphasize the importance of modeling practices, how they interact, and how they should be considered in relation to each other before you act. Using the right tool for the right job is key. We hope you enjoy these examples of where the greatest AI and machine learning techniques exist in your routine today.More AI agent disruptors (0:56)Proxy from London start-up Convergence AIAnother hit to OpenAI, this product is available for free, unlike OpenAI’s Operator. AI Paris Summit - What's next for regulation? (4:40)[Vice President] Vance tells Europeans that heavy regulation can kill AIUS federal administration withdrawing from the previous trend of sweeping big tech regulation on modeling systems.The EU is pushing to reduce bureaucracy but not regulatory pressureModeling paradigms explained (10:33)As companies look for an edge in high-stakes computations, we’ve seen best-in-class rediscovering expert system-based techniques that, with modern computing power, are breathing new light into them. Paradigm 1: Agents (11:23)Paradigm 2: Generative (14:26)Paradigm 3: Mathematical optimization (regression) (18:33)Paradigm 4: Predictive (classification) (23:19)Paradigm 5: Control theory (24:37)The right modeling paradigm for the job? (28:05)What did you think? Let us know.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.

Agentic AI is the latest foray into big-bet promises for businesses and society at large. While promising autonomy and efficiency, AI agents raise fundamental questions about their accuracy, governance, and the potential pitfalls of over-reliance on automation. Does this story sound vaguely familiar? Hold that thought. This discussion about the over-under of certain promises is for you.Show NotesThe economics of LLMs and DeepSeek R1 (00:00:03)Reviewing recent developments in AI technologies and their implications Discussing the impact of DeepSeek’s R1 model on the AI landscape, NVIDIA The origins of agentic AI (00:07:12)Status quo of AI models to date: Is big tech backing away from promise of generative AI?Agentic AI designed to perceive, reason, act, and learnGovernance and agentic AI (00:13:12)Examining the tension between cost efficiency and performance risks [LangChain State of AI Agents Report]Highlighting governance concerns related to AI agents Issues with agentic AI implementation (00:21:01)Considering the limitations of AI agents and their adoption in the workplace Analyzing real-world experiments with AI agent technologies, like Devin What's next for complex and agentic AI systems (00:29:27)Offering insights on the cautious integration of these systems in business practicesEncouraging a thoughtful approach to leveraging AI capabilities for measurable outcomesWhat did you think? Let us know.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.

What if privacy could be as dynamic and socially aware as the communities it aims to protect? Sebastian Benthall, a senior research fellow from NYU’s Information Law Institute, shows us how privacy is complex. He uses Helen Nissenbaum’s work with contextual integrity and concepts in differential privacy to explain the complexity of privacy. Our talk explains how privacy is not just about protecting data but also about following social rules in different situations, from healthcare to education. These rules can change privacy regulations in big ways.Show notesIntro: Sebastian Benthall (0:03)Research: Designing Fiduciary Artificial Intelligence (Benthall, Shekman)Integrating Differential Privacy and Contextual Integrity (Benthall, Cummings)Exploring differential privacy and contextual integrity (1:05)Discussion about the origins of each subjectHow are differential privacy and contextual integrity used to enforce each other?Accepted context or legitimate context? (9:33)Does context develop from what society accepts over time?Approaches to determine situational context and legitimacyNext steps in contextual integrity (13:35)Is privacy as we know it ending?Areas where integrated differential privacy and contextual integrity can help (Cummings)Interpretations of differential privacy (14:30)Not a silver bulletNew questions posed from NIST about its applicationPrivacy determined by social norms (20:25)Game theory and its potential for understanding social normsAgents and governance: what will ultimately decide privacy? (25:27)Voluntary disclosures and the biases it can present towards groups that are least concerned with privacyAvoiding self-fulfilling prophecy from data and contextWhat did you think? Let us know.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.

What if the secret to successful AI governance lies in understanding the evolution of model documentation? In this episode, our hosts challenge the common belief that model cards marked the start of documentation in AI. We explore model documentation practices, from their crucial beginnings in fields like finance to their adaptation in Silicon Valley. Our discussion also highlights the important role of early modelers and statisticians in advocating for a complete approach that includes the entire model development lifecycle.Show NotesModel documentation origins and best practices (1:03)Documenting a model is a comprehensive process that requires giving users and auditors clear understanding: Why was the model built? What data goes into a model? How is the model implemented? What does the model output? Model cards - pros and cons (7:33)Model cards for model reporting, Association for Computing MachineryEvolution from this research to Google's definition to todayHow the market perceives them vs. what they areWhy the analogy “nutrition labels for models” needs a closer lookSystem cards - pros and cons (12:03)To their credit, OpenAI system cards somewhat bridge the gap between proper model documentation and a model card.Contains complex descriptions of evaluation methodologies along with results; extra points for reporting red-teaming resultsRepresents 3rd-party opinions of the social and ethical implications of the release of the modelAutomating model documentation with generative AI (17:17)Finding the balance in automation in a great governance strategyGenerative AI can provide an assist in editing and personal workflowImproving documentation for AI governance (23:11)As model expert, engage from the beginning with writing the bulk of model documentation by hand.The exercise of documenting your models solidifies your understanding of the model's goals, values, and methods for the businessWhat did you think? Let us know.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.

Are businesses ready for large language models as a path to AI? In this episode, the hosts reflect on the past year of what has changed and what hasn’t changed in the world of LLMs. Join us as we debunk the latest myths and emphasize the importance of robust risk management in AI integration. The good news is that many decisions about adoption have forced businesses to discuss their future and impact in the face of emerging technology. You won't want to miss this discussion.Intro and news: The veto of California's AI Safety Bill (00:00:03)Can state-specific AI regulations really protect consumers, or do they risk stifling innovation? (Gov. Newsome's response)Veto highlights the critical need for risk-based regulations that don't rely solely on the size and cost of language models Arguments to be made for a cohesive national framework that ensures consistent AI regulation across the United StatesAre businesses ready to embrace large language models, or are they underestimating the challenges? (00:08:35) The myth that acquiring a foundational model is a quick fix for productivity woes The essential role of robust risk management strategies, especially in sensitive sectors handling personal dataReview of model cards, Open AI's system cards, and the importance of thorough testing, validation, and stricter regulations to prevent a false sense of securityTransparency alone is not enough; objective assessments are crucial for genuine progress in AI integrationFrom hallucinations in language models to ethical energy use, we tackle some of the most pressing problems in AI today (00:16:29)Reinforcement learning with annotators and the controversial use of other models for reviewJan LeCun's energy systems and retrieval-augmented generation (RAG) offer intriguing alternatives that could reshape modeling approachesThe ethics of advancing AI technologies, consider the parallels with past monumental achievements and the responsible allocation of resources (00:26:49)There is good news about developments and lessons learned from LLMs; but there is also a long way to go.Our original predictions in episode 2 for LLMs still reigns true: “Reasonable expectations of LLMs: Where truth matters and risk tolerance is low, LLMs will not be a good fit”With increased hype and awareness from LLMs came varying levels of interest in how all model types and their impacts are governed in a business.What did you think? Let us know.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.

Our special guest, astrophysicist Rachel Losacco, explains the intricacies of galaxies, modeling, and the computational methods that unveil their mysteries. She shares stories about how advanced computational resources enable scientists to decode galaxy interactions over millions of years with true-to-life accuracy. Sid and Andrew discuss transferable practices for building resilient modeling systems. Prologue: Why it's important to bring stats back [00:00:03]Announcement from the American Statistical Association (ASA): Data Science Statement Updated to Include “ and AI”  Today's guest: Rachel Losacco [00:02:10]Rachel is an astrophysicist who’s worked with major galaxy formation simulations for many years. She hails from Leiden (Lie-den) University and the University of Florida. As a Senior Data Scientist, she works on modeling road safety.  Defining complex systems through astrophysics [00:02:52]Discussion about origins and adoption of complex systemsDifficulties with complex systems: Nonlinearity, chaos and randomness, collective dynamics and hierarchy, and emergence.Complexities of nonlinear systems [00:08:20]Linear models (Least Squares, GLMs, SVMs) can be incredibly powerful but they cannot model all possible functions (e.g. a decision boundary of concentric circles)Non-linearity and how it exists in the natural worldChaos and randomness [00:11:30]Enter references to Jurassic Park and The Butterfly Effect“In universe simulations, a change to a single parameter can govern if entire galaxy clusters will ever form” - RachelCollective dynamics and hierarchy [00:15:45]Interactions between agents don’t occur globally and often is mediated through effects that only happen on specific sub-scalesAdaptation: components of systems breaking out of linear relationships between inputs and outputs to better serve the function of the greater system   Emergence and complexity [00:23:36]New properties arise from the system that cannot be explained by the base rules governing the systemExamples in astrophysics [00:24:34]These difficulties are parts of solving previously impossible problemsConsider this lecture from IIT Delhi on Complex Systems to get a sense of what is required to study and formalize a complex system and its collective dynamics (https://www.youtube.com/watch?v=yJ39ppgJlf0)Consciousness and reasoning from a new point of view [00:31:45]Non-linearity, hierarchy, feedback loops, and emergence may be ways to study consciousness. The brain is a complex system that a simple set of rules cannot fully define.See: Brain modeling from scratch of C. Elgans What did you think? Let us know.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.

Can your AI models survive a big disaster? While a recent major IT incident with CrowdStrike wasn't AI related, the magnitude and reaction reminded us that no system no matter how proven is immune to failure. AI modeling systems are no different. Neglecting the best practices of building models can lead to unrecoverable failures. Discover how the three-tiered framework of robustness, resiliency, and anti-fragility can guide your approach to creating AI infrastructures that not only perform reliably under stress but also fail gracefully when the unexpected happens.Show NotesTechnology, incidents, and why basics matter (00:00:03)While the recent Crowdstrike incident wasn't caused by AI, it's impact was a wakeup call for people and processes that support critical systemsAs AI is increasingly being used at both experimental and production levels, we can expect AI incidents are a matter of if, not when. What can you do to prepare?The "7P's": Are you capable of handling the unexpected? (00:09:05)The 7Ps is an adage, dating back to WWII, that aligns with our "do things the hard way" approach to AI governance and modeling systems.Let’s consider the levels of building a performant system: Robustness, Resiliency, and AntifragilityModel robustness (00:10:03)Robustness is a very important but often overlooked component of building modeling systems. We suspect that part of the problem is due to: The Kaggle-driven upbringing of data scientistsAssumed generalizability of modeling systems, when models are optimized to perform well on their training data but do not generalize enough to perform well on unseen data.Model resilience (00:16:10)Resiliency is the ability to absorb adverse stimuli without destruction and return to its pre-event state.In practice, robustness and resiliency, testing, and planning are often easy components to leave out. This is where risks and threats are exposed.See also, Episode 8. Model validation: Robustness and resilienceModels and antifragility (00:25:04)Unlike resiliency, which is the ability to absorb damaging inputs without breaking, antifragility is the ability of a system to improve from challenging stimuli. (i.e. the human body)A key question we need to ask ourselves if we are not actively building our AI systems to be antifragile, why are we using AI systems at all?What did you think? Let us know.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.

Join us as we chat with Patrick Hall, Principal Scientist at Hallresearch.ai and Assistant Professor at George Washington University. He shares his insights on the current state of AI, its limitations, and the potential risks associated with it. The conversation also touched on the importance of responsible AI, the role of the National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF) in adoption, and the implications of using generative AI in decision-making.Show notesGovernance, model explainability, and high-risk applications 00:00:03 Intro to PatrickHis latest book: Machine Learning for High-Risk Applications: Approaches to Responsible AI (2023)The benefits of NIST AI Risk Management Framework 00:04:01 Does not have a profit motive, which avoids the potential for conflicts of interest when providing guidance on responsible AI. Solicits, adjudicates, and incorporates feedback from the public and other stakeholders.NIST is not law, however it's recommendations set companies up for outcome-based reviews by regulators.Accountability challenges in "blame-free" cultures 00:10:24 Cites these cultures have the hardest time with the framework's recommendationsPractices like documentation and fair model reviews need accountability and objectivityIf everyone's responsible, no one's responsible.The value of explainable models vs black-box models 00:15:00 Concerns about replacing explainable models with LLMs for LLM's sake Why generative AI is bad for decision-making AI and its impact on students 00:21:49 Students are more indicative of where the hype and market is todayTeaching them how to work through the best model for the best job despite the hypeAI incidents and contextual failures 00:26:17 AI Incident Database AI, as it currently stands, is a memorizing and calculating technology. It lacks the ability to incorporate real-world context.McDonald's AI Drive-Thru debacle is a warning to us allGenerative AI and homogenization problems 00:34:30Recommended resources from Patrick:Ed Zitron “Better Offline” NIST ARIA AI Safety Is a Narrative ProblemWhat did you think? Let us know.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.