This episode of Eye on AI is sponsored by Citrusx.

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In this episode of the Eye on AI podcast, Terry Sejnowski, a pioneer in neural networks and computational neuroscience, joins Craig Smith to discuss the future of AI, the evolution of ChatGPT, and the challenges of understanding intelligence.

Terry, a key figure in the deep learning revolution, shares insights into how neural networks laid the foundation for modern AI, including ChatGPT’s groundbreaking generative capabilities. From its ability to mimic human-like creativity to its limitations in true understanding, we explore what makes ChatGPT remarkable and what it still lacks compared to human cognition.

We also dive into fascinating topics like the debate over AI sentience, the concept of "hallucinations" in AI models, and how language models like ChatGPT act as mirrors reflecting user input rather than possessing intrinsic intelligence. Terry explains how understanding language and meaning in AI remains one of the field’s greatest challenges.

Additionally, Terry shares his perspective on nature-inspired AI and what it will take to develop systems that go beyond prediction to exhibit true autonomy and decision-making.

Learn why AI models like ChatGPT are revolutionary yet incomplete, how generative AI might redefine creativity, and what the future holds for AI as we continue to push its boundaries.

Don’t miss this deep dive into the fascinating world of AI with Terry Sejnowski. Like, subscribe, and hit the notification bell for more cutting-edge AI insights!

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Craig Smith Twitter: https://twitter.com/craigss

Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

(00:00) Introduction to Terry Sejnowski and His Work

(03:02) The Origins of Modern AI and Neural Networks

(05:29) The Deep Learning Revolution and ImageNet

(07:11) Understanding ChatGPT and Generative AI

(12:34) Exploring AI Creativity

(16:03) Lessons from Gaming AI: AlphaGo and Backgammon

(18:37) Early Insights into AI’s Affinity for Language

(24:48) Syntax vs. Semantics: The Purpose of Language

(30:00) How Written Language Transformed AI Training

(35:10) Can AI Become Sentient?

(41:37) AI Agents and the Next Frontier in Automation

(45:43) Nature-Inspired AI: Lessons from Biology

(50:02) Digital vs. Biological Computation: Key Differences

(54:29) Will AI Replace Jobs?

(57:07) The Future of 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 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.

Don’t forget to like, subscribe, and hit the notification bell for more AI insights!

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Craig Smith Twitter: https://twitter.com/craigss

Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

(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.

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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.

Don’t forget to like, subscribe, and hit the notification bell for more!

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Craig Smith Twitter: https://twitter.com/craigss

Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

(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