Eye On A.I.

• #237 Pedro Domingo’s on Bayesians and Analogical Learning in AI

  This episode is sponsored by Thuma. Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details. To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai   In this episode of the Eye on AI podcast, Pedro Domingos, renowned AI researcher and author of The Master Algorithm, joins Craig Smith to explore the evolution of machine learning, the resurgence of Bayesian AI, and the future of artificial intelligence. Pedro unpacks the ongoing battle between Bayesian and Frequentist approaches, explaining why probability is one of the most misunderstood concepts in AI. He delves into Bayesian networks, their role in AI decision-making, and how they powered Google’s ad system before deep learning. We also discuss how Bayesian learning is still outperforming humans in medical diagnosis, search & rescue, and predictive modeling, despite its computational challenges. The conversation shifts to deep learning’s limitations, with Pedro revealing how neural networks might be just a disguised form of nearest-neighbor learning. He challenges conventional wisdom on AGI, AI regulation, and the scalability of deep learning, offering insights into why Bayesian reasoning and analogical learning might be the future of AI. We also dive into analogical learning—a field championed by Douglas Hofstadter—exploring its impact on pattern recognition, case-based reasoning, and support vector machines (SVMs). Pedro highlights how AI has cycled through different paradigms, from symbolic AI in the '80s to SVMs in the 2000s, and why the next big breakthrough may not come from neural networks at all. From theoretical AI debates to real-world applications, this episode offers a deep dive into the science behind AI learning methods, their limitations, and what’s next for machine intelligence. Don’t forget to like, subscribe, and hit the notification bell for more expert discussions on AI, technology, and the future of innovation!    Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction (02:55) The Five Tribes of Machine Learning Explained   (06:34) Bayesian vs. Frequentist: The Probability Debate   (08:27) What is Bayes' Theorem & How AI Uses It   (12:46) The Power & Limitations of Bayesian Networks   (16:43) How Bayesian Inference Works in AI   (18:56) The Rise & Fall of Bayesian Machine Learning   (20:31) Bayesian AI in Medical Diagnosis & Search and Rescue   (25:07) How Google Used Bayesian Networks for Ads   (28:56) The Role of Uncertainty in AI Decision-Making   (30:34) Why Bayesian Learning is Computationally Hard   (34:18) Analogical Learning – The Overlooked AI Paradigm   (38:09) Support Vector Machines vs. Neural Networks   (41:29) How SVMs Once Dominated Machine Learning   (45:30) The Future of AI – Bayesian, Neural, or Hybrid?   (50:38) Where AI is Heading Next  

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• #236 Vall Herard: The Future of AI-Driven Compliance (Saifr.ai)

  This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more. NetSuite is offering a one-of-a-kind flexible financing program. Head to  https://netsuite.com/EYEONAI to know more.     In this episode of Eye on AI, Vall Herard, CEO of Saifr.ai, joins Craig Smith to explore how AI is transforming compliance in financial services.   Saifr.ai acts as a "grammar check" for regulatory compliance, ensuring AI-generated content meets SEC, FINRA, and global financial regulations. Vall explains how Saifr integrates into Microsoft Word, Outlook, and Adobe, reducing compliance risks in marketing, emails, and AI chatbots.   We also discuss Saifr.ai’s partnership with Microsoft, AI’s role in regulated industries, and how businesses can safely adopt generative AI without violating compliance laws. - How does AI reduce compliance friction? - Why is regulatory oversight a barrier to AI adoption? - What does AI safety really mean for financial services? Find out in this deep dive into AI, compliance, and the future of regulation. Like, subscribe, and hit the notification bell for more AI insights!   Strengthen your compliance controls with AI: https://saifr.ai/   Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction to Generative AI and Compliance   (02:47) Meet Vall Herard, CEO of Saifr.ai   (05:28) What Saifr.ai Does and Its Mission   (08:25) How Saifr.ai Ensures Regulatory Compliance   (12:13) Overcoming AI Adoption Barriers in Finance   (19:58) Saifr.ai’s Partnership with Microsoft   (24:11) How SaferAI Integrates with Microsoft Office   (29:33) AI in Podcast and Audio Compliance Review   (33:54) Saifr.ai’s Business Model and Pricing   (38:09) How Saifr.ai Works with Generative AI Chatbots   (42:36) Supporting Multiple Languages for Compliance   (50:08) Future Outlook

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• #235 Tyler Xuan Saltsman: How AI is Shaping the Future of Combat & Warfare

  In this episode of the Eye on AI podcast, Tyler Xuan Saltsman, CEO of Edgerunner, joins Craig Smith to explore how AI is reshaping military strategy, logistics, and defense technology—pushing the boundaries of what’s possible in modern warfare.   Tyler shares the vision behind Edgerunner, a company at the cutting edge of generative AI for military applications. From logistics and mission planning to autonomous drones and battlefield intelligence, Edgerunner is building domain-specific AI that enhances decision-making, ensuring national security while keeping humans in control.   We dive into how AI-powered military agents work, including the LoRA (Low-Rank Adaptation) model, which fine-tunes AI to think and act like military specialists—whether in logistics, aircraft maintenance, or real-time combat scenarios. Tyler explains how retrieval-augmented generation (RAG) and small language models allow warfighters to access mission-critical intelligence without relying on the internet, bringing real-time AI support directly to the battlefield.   Tyler also discusses the future of drone warfare—how AI-driven, vision-enabled drones can neutralize threats autonomously, reducing reliance on human pilots while increasing battlefield efficiency. With autonomous swarms, AI-powered kamikaze drones, and real-time situational awareness, the landscape of modern warfare is evolving fast.   Beyond combat, we explore AI’s role in security, including advanced weapons detection systems that can safeguard military bases, schools, and public spaces. Tyler highlights the urgent need for transparency in AI, contrasting Edgerunner’s open and auditable AI models with the black-box approaches of major tech companies.   Discover how AI is transforming military operations, from logistics to combat strategy, and what this means for the future of defense technology.   Don’t forget to like, subscribe, and hit the notification bell for more deep dives into AI, defense, and cutting-edge technology!   Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI 00:00) Introduction – AI for the Warfighter (01:34) How AI is Transforming Military Logistics( 04:44) Running AI on the Edge – No Internet Required (06:49) AI-Powered Mission Planning & Risk Mitigation (14:32) The Future of AI in Drone Warfare (22:17) AI’s Role in Strategic Defense & Economic Warfare (26:34) The U.S.-China AI Race – Are We Falling Behind? (35:17) The Future of AI in Warfare

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• #234 Matt Price: How Crescendo is Disrupting Customer Service with Gen AI

  In this episode of the Eye on AI podcast, Matt Price, CEO of Crescendo, joins Craig Smith to discuss how generative AI is reshaping customer service and blending seamlessly with human expertise to create next-level customer experiences.   Matt shares the story behind Crescendo, a company at the forefront of revolutionizing customer service by integrating advanced AI technology with human-driven solutions. With a focus on outcome-based service delivery and quality assurance, Crescendo is setting a new standard for customer engagement.   We dive into Crescendo’s innovative approach, including its use of large language models (LLMs) combined with proprietary IP to deliver consistent, high-quality support across 56 languages. Matt explains how Crescendo’s AI tools are designed to handle routine tasks while enabling human agents to focus on complex, empathy-driven interactions—resulting in higher job satisfaction and better customer outcomes.   Matt highlights how Crescendo is redefining the BPO industry, combining AI and human capabilities to reduce costs while improving the quality of customer interactions. From enhancing agent retention to enabling scalable, multilingual support, Crescendo’s impact is transformative.   Discover how Matt and his team are designing a future where AI and humans work together to deliver exceptional customer experiences—reimagining what’s possible in the world of customer service.   Don’t forget to like, subscribe, and hit the notification bell for more insights into AI, technology, and innovation! Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction to Matt Price and Crescendo   (01:49) The rise of AI in customer service   (05:34) Using AI and human expertise for better customer experiences   (07:47) How Gen AI reduces costs and improves engagement   (09:37) Challenges in customer service design and innovation   (11:32) Moving from hidden chatbots to front-and-center customer interaction   (14:08) Training human agents to work seamlessly with AI   (17:02) Using AI to analyze and improve service interactions   (19:15) Outcome-based pricing vs traditional headcount models   (21:53) Improving contact center roles with AI integration   (25:08) The importance of curating accurate knowledge bases for AI   (28:05) Crescendo’s acquisition of PartnerHero and its impact   (30:39) Scaling customer service with AI-human collaboration   (32:06) Multilingual support: AI in 56 languages   (33:49) The vast market potential of AI-driven customer service   (36:28) How Crescendo is reshaping customer service with AI innovation   (42:42) Building customer profiles for personalized support  

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• #232 Sepp Hochreiter: How LSTMs Power Modern AI System’s

  In this special episode of the Eye on AI podcast, Sepp Hochreiter, the inventor of Long Short-Term Memory (LSTM) networks, joins Craig Smith to discuss the profound impact of LSTMs on artificial intelligence, from language models to real-time robotics. Sepp reflects on the early days of LSTM development, sharing insights into his collaboration with Jürgen Schmidhuber and the challenges they faced in gaining recognition for their groundbreaking work. He explains how LSTMs became the foundation for technologies used by giants like Amazon, Apple, and Google, and how they paved the way for modern advancements like transformers. Topics include: - The origin story of LSTMs and their unique architecture. - Why LSTMs were crucial for sequence data like speech and text. - The rise of transformers and how they compare to LSTMs. - Real-time robotics: using LSTMs to build energy-efficient, autonomous systems. The next big challenges for AI and robotics in the era of generative AI. Sepp also shares his optimistic vision for the future of AI, emphasizing the importance of efficient, scalable models and their potential to revolutionize industries from healthcare to autonomous vehicles. Don’t miss this deep dive into the history and future of AI, featuring one of its most influential pioneers. (00:00) Introduction: Meet Sepp Hochreiter (01:10) The Origins of LSTMs (02:26) Understanding the Vanishing Gradient Problem (05:12) Memory Cells and LSTM Architecture (06:35) Early Applications of LSTMs in Technology (09:38) How Transformers Differ from LSTMs (13:38) Exploring XLSTM for Industrial Applications (15:17) AI for Robotics and Real-Time Systems (18:55) Expanding LSTM Memory with Hopfield Networks (21:18) The Road to XLSTM Development (23:17) Industrial Use Cases of XLSTM (27:49) AI in Simulation: A New Frontier (32:26) The Future of LSTMs and Scalability (35:48) Inference Efficiency and Potential Applications (39:53) Continuous Learning and Adaptability in AI (42:59) Training Robots with XLSTM Technology (44:47) NXAI: Advancing AI in Industry

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