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 the Eye on AI podcast, Avthar Sewrathan, Lead Technical Product Marketing Manager at Timescale joins Craig Smith to explore how Postgres is transforming AI development with cutting-edge tools and open-source innovation.

With its robust, extensible framework, Postgres has become the go-to database for AI applications, from semantic search to retrieval-augmented generation (RAG). Avthar takes us through Timescale's journey from its IoT origins to disrupting the way developers handle vector search, embedding management, and high-performance AI workloads—all within Postgres.

We dive into Timescale's tools like PGVector, PGVector Scale, and PGAI Vectorizer, uncovering how they aid developers to build AI-powered systems without the complexity of managing multiple databases. Avthar explains how Postgres seamlessly handles structured and unstructured data, making it the perfect foundation for next-gen AI applications.

Learn how Postgres supports AI-driven use cases across industries like IoT, finance, and crypto, and why its open-source ecosystem is key to fostering collaboration and innovation.

Tune in to discover how Postgres is redefining AI databases, why Timescale’s tools are a game-changer for developers, and what the future holds for AI innovation in the database space.

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(00:00) Introduction to Avthar and Timescale

(02:35) The origins of Timescale and TimescaleDB

(05:06) What makes Postgres unique and reliable

(07:17) Open-source philosophy at Timescale

(12:04) Timescale's early focus on IoT and time series data

(16:17) Applications in finance, crypto, and IoT

(19:03) Postgres in AI: From RAG to semantic search

(22:00) Overcoming scalability challenges with PGVector Scale

(24:33) PGAI Vectorizer: Managing embeddings seamlessly

(28:09) The PGAI suite: Tools for AI developers

(30:33) Vectorization explained: Foundations of AI search

(32:24) LLM integration within Postgres

(35:26) Natural language interfaces and database workflows

(38:11) Structured and unstructured data in Postgres

(41:17) Postgres for everything: Simplifying complexity

(44:52) Timescale’s accessibility for startups and enterprises

(47:46) The power of open source in AI

This episode is sponsored by Oracle.

Oracle Cloud Infrastructure, or OCI is a blazing fast and secure platform for your infrastructure, database, application development, plus all your AI and machine learning workloads. OCI costs 50% less for compute and 80% less for networking. So you’re saving a pile of money. Thousands of businesses have already upgraded to OCI, including MGM Resorts, Specialized Bikes, and Fireworks AI.

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In this episode of the Eye on AI podcast, Jeff Boudier, Head of Product and Growth at Hugging Face, joins Craig Smith to uncover how the platform is empowering AI builders and driving the open-source AI revolution.

With a mission to democratize AI, Jeff walks us through Hugging Face's journey from a chatbot for teens to the leading platform hosting over 1 million public AI models, datasets, and applications. We explore how Hugging Face is bridging the gap between enterprises and open-source innovation, enabling developers to build cutting-edge AI solutions with transparency and collaboration.

Jeff dives deep into Hugging Face’s tools and features, from hosting private and public models to fostering a thriving ecosystem of AI builders. He shares insights on the transformative impact of technologies like Transformers, transfer learning, and no-code solutions that make AI accessible to more creators than ever before.

We also discuss Hugging Face’s latest innovation, ‘Hugs,’ designed to help enterprises seamlessly integrate open-source AI within their infrastructure while retaining full control over their data and models.

Tune in to discover how Hugging Face is shaping the future of AI development, why open-source models are catching up with proprietary ones, and what trends are driving innovation across AI disciplines.

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(00:00) Introduction to Jeff Boudier

(02:16) How Hugging Face Empowers AI Builders

(05:26) Transition from Chatbot to Leading AI Platform

(07:07) Hosting AI Models: Public and Private Options

(10:13) What Does Hosting Models on Hugging Face Mean?

(14:22) Hugging Face vs. GitHub: Key Differences

(19:09) Navigating 1 Million Models on the Hugging Face Hub

(22:33) Leaderboards and Filtering AI Models

(25:26) Building Applications with Hugging Face Models

(28:03) AI Innovation: From Code to Model-Driven Development

(30:45) Frameworks for Agentic Systems and Hugging Chat

(35:20) Open Source vs. Proprietary AI: The Future

(40:41) Introducing ‘Hugs’: Open AI for Enterprises

(44:59) The Role of No-Code in AI Development

(47:26) Hugging Face’s Vision

This episode is sponsored by Legal Zoom.

Launch, run, and protect your business to make it official TODAY at https://www.legalzoom.com/ and use promo code Smith10 to get 10% off any LegalZoom business formation product excluding subscriptions and renewals.

In this episode of the Eye on AI podcast, we dive into the world of Artificial General Intelligence (AGI) with Ben Goertzel, CEO of SingularityNET and a leading pioneer in AGI development.

Ben shares his vision for building machines that go beyond task-specific capabilities to achieve true, human-like intelligence. He explores how AGI could reshape society, from revolutionizing industries to redefining creativity, learning, and autonomous decision-making.

Throughout the conversation, Ben discusses his unique approach to AGI, which combines decentralized AI systems and blockchain technology to create open, scalable, and ethically aligned AI networks. He explains how his work with SingularityNET aims to democratize AI, making AGI development transparent and accessible while mitigating risks associated with centralized control.

Ben also delves into the philosophical and ethical questions surrounding AGI, offering insights into consciousness, the role of empathy, and the potential for building machines that not only think but also align with humanity’s best values. He shares his thoughts on how decentralized AGI can avoid the narrow, profit-driven goals of traditional AI and instead evolve in ways that benefit society as a whole.

This episode offers a thought-provoking glimpse into the future of AGI, touching on the technical challenges, societal impact, and ethical considerations that come with creating truly intelligent machines.

Ben’s perspective will leave you questioning not only what AGI can achieve, but also how we can guide it toward a positive future.

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(00:00) Introduction to Ben Goertzel

(01:21) Overview of "The Consciousness Explosion"

(02:28) Ben’s Background in AI and AGI

(04:39) Exploring Consciousness and AI

(08:22) Panpsychism and Views on Consciousness

(10:32) The Path to the Singularity

(13:28) Critique of Modern AI Systems

(18:30) Perspectives on Human-Level AI and Creativity

(21:42) Ben’s AGI Paradigm and Approach

(25:39) OpenCog Hyperon and Knowledge Graphs

(31:12) Integrating Perception and Experience in AI

(34:02) Robotics in AGI Development

(35:06) Virtual Learning Environment for AGI

(39:01) Creativity in AI vs. Human Intelligence

(44:21) User Interaction with AGI Systems

(48:22) Funding AGI Research Through Cryptocurrency

(53:03) Final Thoughts on Compassionate AI

(55:21) How to Get "The Consciousness Explosion" Book

This episode of the Eye on AI podcast is sponsored by JLL.

JLL's AI solutions are transforming the real estate landscape, accelerating growth, streamlining operations and unlocking hidden value in properties and portfolios. From predictive analytics to intelligent automation, JLL is creating smarter buildings, more efficient workplaces and sustainable cities.

To learn more about JLL and AI, visit: jll.com/AI

In this episode of the *Eye on AI* podcast, we explore the world of AI at Google Cloud with Nenshad Bardoliwalla, Director of Product Management for Vertex AI.

Nenshad unpacks the three core layers of Vertex AI: the Model Garden, where users can access and evaluate a diverse range of models; the Model Builder, which supports model fine-tuning and prompt optimization; and the Agent Builder, designed to develop AI agents that can perform complex, goal-oriented tasks.

He shares insights into model evaluation strategies, the role of Google’s Tensor Processing Units (TPUs) in scaling AI infrastructure, and how enterprises can choose the right models based on performance, cost, and regulatory requirements.

Nenshad also delves into the challenges and opportunities of AI prompt optimization, highlighting Google’s approach to ensuring consistent outputs across different models. He discusses the ethical considerations in AI design, emphasizing the need for human oversight and clear guardrails to maintain safety.

Whether you’re in AI, tech, or curious about AI's potential impact, this episode is packed with insights on next-gen AI deployment.

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(00:00) Introduction to Nenshad Bardoliwalla & Vertex AI

(01:52) Overview of Vertex AI's Three Core Layers

(05:35) Nenshad's Journey to Google Cloud

(06:36) Choosing the Right AI Model

(08:00) Google’s AI Infrastructure & Tensor Processing Units (TPUs)

(10:15) Model Builder: Fine-Tuning & Prompt Optimization

(12:11) Agent Builder: Building AI Agents with Tools & Planning

(17:57) Model Evaluation & Prompt Management

(21:23) Generative AI for Business Analysts

(23:24) AI Model Modality & Use Case Selection

(25:23) Popularity Distribution of AI Models

(28:18) Prompt Optimization Tools

(34:20) Building AI Agents: Real-World Use Cases & Ethical Safeguards

(40:13) The Capabilities & Limitations of AI Agents

(45:48) TPU vs. GPU

(50:33) Future of AI at Google Cloud

This episode of Eye on AI is sponsored by Citrusx.

Navigating the complexities of AI risk management? Citrusx has you covered. Their innovative platform helps you make better business decisions and achieve reliable AI outcomes while staying compliant with regulatory standards. Citrusx enables you to manage AI risks effortlessly, connecting all stakeholders and providing continuous validation. Their solution can detect and mitigate vulnerabilities, biases, and errors, ensuring the accuracy, robustness, and compliance of AI models

Visit https://email.citrusx.ai/eyeonai to book your free demo today!

In this episode of the Eye on AI podcast, we dive into process intelligence with Manuel Haug, Field CTO at Celonis.

Manuel shares how process mining is transforming business operations by connecting directly to digital systems to map workflows, optimize processes, and boost efficiency. He explains how Celonis builds digital twins of business processes, allowing companies to visualize and resolve inefficiencies with data-driven insights.

Manuel also explores the role of AI in process optimization, discussing the integration of generative AI and AI agents in Celonis. From automating repetitive tasks to enabling strategic decision-making, Manuel details how AI agents can enhance workflows, reduce costs, and deliver measurable productivity gains across industries.

Tune in to learn how AI agents are evolving, the potential of process intelligence graphs, and how Celonis is pioneering the future of autonomous enterprises.

Whether you're in business, tech, or curious about the impact of AI, this episode offers valuable insights into next-gen process optimization.

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(00:00) Introduction to Manuel Haug & Celonis

(01:07) Understanding Process Intelligence

(07:14) Integrating AI into Process Mining

(12:12) Generative AI’s Role in Process Optimization

(15:18) Challenges of Large-Scale Process Integration

(20:24) Role of AI Agents in Process Automation

(23:43) AI Maturity & Implementation in Enterprises

(27:23) Real-Life AI Agent Examples

(30:47) Building and Integrating AI Agents

(34:53) Future of AI Agents in Enterprises

(37:56) Introducing the Process Intelligence Graph

(39:20) Addressing Data Privacy Concerns

This episode is sponsored by RapidSOS. Close the safety gap and transform your emergency response with RapidSOS.

Visit https://rapidsos.com/eyeonai/ today to learn how AI-powered safety can protect your people and boost your bottom line.

In this episode of the Eye on AI podcast, we dive deep into the world of AI agents with Ece Kamar, VP of Research and Managing Director of AI Frontiers Lab at Microsoft.

Ece shares her unique insights on the future of AI, discussing how AI agents are reshaping the way we interact with technology and perform tasks.

Throughout the episode, Ece explains the groundbreaking potential of AI agents, describing how they act as autonomous entities that can perceive, learn, and carry out complex tasks in real time. She discusses the revolutionary shift from traditional AI models to agentic workflows, highlighting how multi-agent systems like Microsoft's AutoGen are creating scalable solutions for industries and everyday life. Ece also shares her thoughts on building responsible AI, touching on the ethical challenges and safety concerns that come with the rise of autonomous agents.

We explore how multi-agent systems can scale to millions of agents, and how they are transforming enterprises by automating complex workflows, personalizing customer experiences, and pushing the boundaries of AI development. Ece’s perspective on the future of AI in scientific discovery, as well as her work in responsible AI, offers a thought-provoking glimpse into what lies ahead.

Don’t forget to like, subscribe, and hit the notification bell to stay updated on the latest in AI, automation, and ethical tech!

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(00:00) Preview and Introduction

(03:11) What Are AI Agents?

(04:43) Building Responsible AI at Microsoft

(10:55) The Rise of Agentic Workflows

(12:30) Multi-Agent Systems and AutoGen

(18:04) Scaling Multi-Agent Systems

(20:22) The Creation and Evolution of AutoGen

(23:07) Real-World Applications of AutoGen

(25:52) Large-Scale Simulations with AI Agents

(27:36) The Role of AI Agents in Scientific Discovery

(31:20) AI Agents and Complex Reasoning

(36:49) Challenges in Defining Agent Boundaries

(39:12) The Risk of Agents Interacting with Each Other

(43:59) Building Trustworthy and Safe AI Agents

(48:44) Learning from Human Factors in Automation

(50:50) Why Speed and Coordination Matter in AI Development

(55:08) The Future of AI Agents in Enterprises

(57:47) Low-Code/No-Code Development for AI Agents

This episode is sponsored by Oracle. AI is revolutionizing industries, but needs power without breaking the bank. Enter Oracle Cloud Infrastructure (OCI): the one-stop platform for all your AI needs, with 4-8x the bandwidth of other clouds. Train AI models faster and at half the cost. Be ahead like Uber and Cohere.

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In this episode of the Eye on AI podcast, we sit down with Mark Surman, President of Mozilla, to explore the future of open-source AI and how Mozilla is leading the charge for privacy, transparency, and ethical technology.

Mark shares Mozilla’s vision for AI, detailing the company’s innovative approach to building trustworthy AI and the launch of Mozilla AI. He explains how Mozilla is working to make AI open, accessible, and secure for everyone—just as it did for the web with Firefox. We also dive into the growing importance of federated learning and AI governance, and how Mozilla Ventures is supporting groundbreaking companies like Flower AI.

Throughout the conversation, Mark discusses the critical need for open-source AI alternatives to proprietary models like OpenAI and Meta’s LLaMA. He outlines the challenges with closed systems and highlights Mozilla’s work in giving users the freedom to choose AI models directly in Firefox.

Mark provides a fascinating look into the future of AI and how open-source technologies can create trillions in economic value while maintaining privacy and inclusivity. He also sheds light on the global race for AI innovation, touching on developments from China and the impact of public AI funding.

Don’t forget to like, subscribe, and hit the notification bell to stay up to date with the latest trends in AI, open-source tech, and machine learning!

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(00:00) Introduction to Mark Surman and Mozilla’s Mission

(02:01) The Evolution of Mozilla: From Firefox to AI

(04:40) Open-Source Movement and Mozilla’s Legacy

(06:58) The Role of Open-Source in AI

(11:06) Advancing Federated Learning and AI Governance

(14:10) Integrating AI Models into Firefox

(16:28) Open vs Closed Models

(22:09) Partnering with Non-Profit AI Labs for Open-Source AI

(25:08) How Meta’s Strategy Compares to OpenAI and Others

(27:58) Global Competition in AI Innovation

(31:17) The Cost of Training AI Models

(33:36) Public AI Funding and the Role of Government

(37:40) The Geopolitics of AI and Open Source

(41:35) Mozilla’s Vision for the Future of AI and Responsible Tech

This episode is sponsored by Speechmatics. Check it out at www.speechmatics.com/realtime

Today, we're joined by Dr. Thomas G. Dietterich, a pioneer in machine learning who recently was honored with the Award for Research Excellence from the International Joint Conference on Artificial Intelligence, one of the top awards for AI researchers.

Dietterich traces the field's progression from early rule-based systems to modern machine learning paradigms and delves into his work on novel category detection and open set problems. He also discusses the evolution of ensemble methods in the context of large language models (LLMs), highlighting the shift from combining many cheap models to more selective approaches with expensive models.

He advocates for a foundation model approach to capture the variability of the world.

Join us for a deep dive into the future of AI, where Thomas explains why the development of novel materials and drugs may have the most transformative impact on our economy. Plus, hear about his latest work on multi-instance learning, weak supervision, and the role of reinforcement learning in real-world applications like wildfire management.

Don’t forget to like, subscribe, and hit the notification bell to stay updated on the latest trends and insights in AI and machine learning!

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(00:00) Introduction to Thomas Dietterich's Machine Learning Journey

(02:34) The Early Days of Machine Learning and AI Systems

(04:29) Tackling the Multiple Instance Problem in Drug Design

(05:41) AI in Sustainability

(07:17) The Challenge of Novelty Detection in AI Systems

(08:00) Addressing the Open Set Problem in Cybersecurity and Computer Vision

(09:11) The Evolution of Deep Learning in Computer Vision

(11:21) How Deep Learning Handles Novel Representations

(12:01) Foundation Models and Self-Supervised Learning

(14:11) Vision Transformers vs. Convolutional Neural Networks

(16:05) The Role of Multi-Instance Learning in Weakly Labeled Data

(18:36) Ensemble Learning and Deep Networks in Machine Learning

(20:33) The Future of AI: Large Language Models and Their Applications

(23:51) Symbolic Regression and AI’s Role in Scientific Discovery

(34:44) AI in Wildfire Management: Using Reinforcement Learning

(39:32) AI-Driven Problem Formulation and Optimization in Industry

(41:30) The Future of AI Reasoning Systems and Problem Solving

(45:03) The Limits of Large Language Models in Scientific Research

(50:12) Closing Thoughts: Open Challenges and Opportunities in AI