Eye On A.I.

• #301 Hemant Banavar & Ryan Paulson: The AI Safety System Driving Toward Zero Harm

  How are AI and telematics changing safety for fleets in the real world, and what does it take to get from basic recordings to true accident prevention? In this episode of Eye on AI, host Craig Smith speaks with Hemant, Chief Product Officer at Motive, and Ryan, CIO at Fusion Site Services, to explore how AI powered cameras and telematics are transforming safety, productivity, and profitability across the physical economy, from trucking and construction to field services. We look at what makes safety AI trustworthy at scale, how to reduce false alerts that drivers ignore, and how to combine in cab coaching, human review, and rich telematics data to drive down risky behaviors. Learn how Fusion Site Services cut unsafe events by more than ninety percent while tripling in size, slashed insurance claims and premiums, and used real time insights to tackle idling, under utilized assets, and the hidden costs of unsafe operations. You will also hear how leading fleets run side by side vendor tests, design incentive programs that get drivers on board with cameras, and build a culture around zero preventable accidents. If you are responsible for safety, operations, or risk, this episode will show you how to evaluate AI and telematics platforms, which benchmarks to demand, and how to turn your data into safer roads and stronger unit economics. Stay Updated: Craig Smith on X: https://x.com/craigss  Eye on A.I. on X: https://x.com/EyeOn_AI 

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• #300 Fred Laluyaux: How Decision Intelligence & AI Agents Are Redefining Enterprise Operations

  How are Decision Intelligence and AI agents reshaping enterprise operations today? In this episode of Eye on AI, host Craig Smith sits down with Fred Laluyaux, CEO of Aera Technology, to unpack how organizations move from dashboards and ad hoc workflows to a system that senses, decides, and acts.  AI is not just about chatbots. At the heart of this transformation is decision intelligence: connecting data, analytics, AI, and automation to optimize decisions across the enterprise. Fred explains why this is becoming the operating backbone of the modern enterprise and how it accelerates the shift toward autonomous, self-driving businesses. We look at how to build a decision intelligence stack end to end, how AI agents collaborate with people, and how to stand up a control room that monitors decisions across supply chain, finance, and customer operations. Learn how leading companies model decisions, govern them safely, and measure impact with clear metrics that matter, including service level, cost to serve, cash flow, inventory turns, and time to resolution. Stay Updated: Craig Smith on X: https://x.com/craigss   Eye on A.I. on X: https://x.com/EyeOn_AI 

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• #299 Jacob Buckman: Why the Future of AI Won't Be Built on Transformers

  This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.    Visit https://agntcy.org/ and add your support.   Why do today's LLMs forget key details over long context, and what would it take to give them real memory that scales?   In this episode of Eye on AI, host Craig Smith explores Manifest AI's Power Retention architecture and how it rethinks memory, context, and learning for modern models. We look at why transformers struggle with long inputs, how state space and retention models keep context at linear cost, and how scaling state size unlocks reliable recall across lengthy conversations, code, and documents. We also cover practical paths to retrofit existing transformer models, how in context learning can replace frequent fine tuning, and what this means for teams building agents and RAG systems.   Learn how product leaders and researchers measure true long context quality, which pitfalls to avoid when extending context windows, and which metrics matter most for success, including recall consistency, answer fidelity, task completion, CSAT, and cost per resolution. You will also hear how to design per user memory, set governance that prevents regressions, evaluate LLM as judge with human review, and plan a secure rollout that improves retrieval, multi step workflows, and agent reliability across chat, email, and voice. Stay Updated: Craig Smith on X:https://x.com/craigss  Eye on A.I. on X: https://x.com/EyeOn_AI   

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• #298 Ryan Kolln: How Appen Trains the World's Most Powerful AI Models

  This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.  Visit https://agntcy.org/ and add your support. How do the world's most powerful AI models get trained and trusted at scale, and what does that really take from data to deployment? In this episode, Appen CEO Ryan Kolln joins Eye on AI to unpack how rigorous human evaluation, culturally aware data, and model-based judges come together to raise real-world performance. In this episode of Eye on AI, host Craig Smith speaks with Ryan Kolln, CEO of Appen, about building evaluation systems that go beyond static benchmarks to measure usefulness, safety, and reliability in production. They explore how human raters and AI evaluators work in tandem, why localization matters across regions and domains, and how quality controls keep feedback signals trustworthy for training and post-training. Ryan explains how evaluation feeds reinforcement strategies, where rubric-driven human judgments inform reward models, and how enterprises can stand up secure workflows for sensitive use cases. He also discusses emerging needs around sovereign models, domain-specific testing, and the shift from general chat to agentic workflows that operate inside real business systems. Learn how leading teams design human-in-the-loop evaluation, when to route judgments from models back to expert reviewers, how to capture cultural nuance without losing universal guardrails, and how to build an evaluation stack that scales from early prototypes to production AI. Stay Updated: Craig Smith on X: https://x.com/craigss  Eye on A.I. on X: https://x.com/EyeOn_AI 

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• #298 Sunita Sarawagi: How AI Can Learn the World and Still Follow Logic

  This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.  Visit https://agntcy.org/ and add your support. Why should AI that learns from the messy real world still obey strict logic, and what does it take to make that reliability hold up in production? In this episode of Eye on AI, host Craig Smith sits down with Sunita Sarawagi to unpack how large scale learning can be combined with explicit rules and constraints so models stay trustworthy. We cover when world ingestion fails without structure, how to encode domain logic alongside LLMs, and which hybrid or neurosymbolic approaches reduce hallucinations while preserving flexibility. You will hear how to design a reliability stack for real users, detect out of distribution inputs, and choose evaluation signals that reflect outcomes rather than accuracy alone. Learn how product teams layer formal logic on top of generative models, decide what to hard code versus learn from data, and enforce business policies across agents, tools, and knowledge graphs. You will also hear how to run safe experiments, track prompt and model changes, prevent regressions before they reach customers, and plan for compute and infrastructure at scale with metrics like completion rate, CSAT, retention, and cost per resolution.   ​​Stay Updated: Craig Smith on X: https://x.com/craigss  Eye on A.I. on X: https://x.com/EyeOn_AI

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