Join us on The Before AGI Podcast as we explore Probabilistic AI – a profound shift from AI systems that give single, overconfident answers to "humble AI" that can quantify its own uncertainty. Discover how this approach leads to safer, more reliable, and ultimately more useful AI across high-stakes fields.
In this episode, you'll gain insights into:
💡 The Need for Uncertainty: Why deterministic AI can be dangerous and how probabilistic models provide a solution.
🤔 Aleatoric vs. Epistemic: Understanding the two key types of uncertainty AI must handle.
⚙️ Core Methods: An intuitive explanation of Bayesian Methods and Gaussian Processes.
🧠 Probabilistic Deep Learning: How these principles are being integrated into powerful deep learning models (BNNs, VAEs, EDL).
🌍 Proven Real-World Impact: Compelling examples from healthcare (42% fewer readmissions), autonomous driving (23% fewer simulated accidents), finance, science, and combating LLM hallucinations.
🚧 Challenges & Future: Addressing hurdles like scalability and interpretability, and looking ahead to synergies with Causal AI and Trustworthy XAI.
This deep dive reveals why the most intelligent AI might not be the one that pretends to know everything, but the one that's honest about its limitations.
Follow Before AGI Podcast for more essential explorations into core AI concepts!
TOOLS MENTIONED:
Bayesian Methods
Gaussian Processes (GPs)
Bayesian Neural Networks (BNNs)
Variational Autoencoders (VAEs)
Evidential Deep Learning (EDL)
CONTACT INFORMATION:
🌐 Website: ianochiengai.substack.com
📺 YouTube: Ian Ochieng AI
🐦 Twitter: @IanOchiengAI
📸 Instagram: @IanOchiengAI
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