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Dominic Williams’ Bold Vision for The Internet Computer (ICP) | The Future of Decentralized Computing

The internet is broken—can blockchain fix it? In this episode, Dominic Williams, the visionary behind The Internet Computer (ICP) and founder of DFINITY, reveals his plan to build a decentralized alternative to cloud computing. Discover how ICP is challenging Big Tech, replacing traditional IT infrastructure, and creating a tamper-proof, autonomous internet powered by smart contracts.

What You'll Learn in This Episode:

• Why Dominic Williams believes the current internet is flawed

• How ICP aims to replace centralized cloud providers like AWS & Google Cloud

• The role of smart contracts in making the internet more secure and censorship-resistant

• The mission of DFINITY and how it started in 2016

• The future of Web3, decentralized applications (dApps), and blockchain governance

Don't miss this deep dive into the future of the internet! If you're interested in blockchain, decentralization, and the next evolution of the web, this episode is for you.

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

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

(00:00) The Origins of The Internet Computer

(02:57) Dominic Williams’ Background in Tech

(04:28) Early Innovations in Distributed Computing

(07:08) The Birth of a 'World Computer' Concept

(11:22) Reimagining IT: A Decentralized Alternative

(13:45) The Creation of DFINITY and ICP

(16:29) How ICP Differs from Traditional Blockchains

(22:05) The Problem with Cloud-Based Blockchains

(25:35) How ICP Ensures True Decentralization

(29:25) AI & The Self-Writing Internet

(35:24) How ICP Hosts AI & Smart Contracts

(40:23) Understanding Reverse Gas and ICP’s Economy

(45:03) The Vision: A Truly Decentralized Internet

(49:09) How To Use The Internet Computer

(52:01) The Role of Nodes & Incentives in ICP

(56:53) The Future of Web3 & Decentralized Applications

(01:05:49) The Misconception of ‘On-Chain’ & Blockchain Hype

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In this episode of the Eye on AI podcast, Pedro Domingos—renowned AI researcher and author of The Master Algorithm—joins Craig Smith to break down the Symbolist approach to artificial intelligence, one of the Five Tribes of Machine Learning.

Pedro explains how Symbolic AI dominated the field for decades, from the 1950s to the early 2000s, and why it’s still playing a crucial role in modern AI. He dives into the Physical Symbol System Hypothesis, the idea that intelligence can emerge purely from symbol manipulation, and how AI pioneers like Marvin Minsky and John McCarthy built the foundation for rule-based AI systems.

The conversation unpacks inverse deduction—the Symbolists' "Master Algorithm"—and how it allows AI to infer general rules from specific examples. Pedro also explores how decision trees, random forests, and boosting methods remain some of the most powerful AI techniques today, often outperforming deep learning in real-world applications.

We also discuss why expert systems failed, the knowledge acquisition bottleneck, and how machine learning helped solve Symbolic AI’s biggest challenges. Pedro shares insights on the heated debate between Symbolists and Connectionists, the ongoing battle between logic-based reasoning and neural networks, and why the future of AI lies in combining these paradigms.

From AlphaGo’s hybrid approach to modern AI models integrating logic and reasoning, this episode is a deep dive into the past, present, and future of Symbolic AI—and why it might be making a comeback.

Don't forget to like, subscribe, and hit the notification bell for more expert discussions on AI, technology, and the future of intelligence!

Stay Updated:

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

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

(00:00) Pedro Domingos onThe Five Tribes of Machine Learning

(02:23) What is Symbolic AI?

(04:46) The Physical Symbol System Hypothesis Explained

(07:05) Understanding Symbols in AI

(11:51) What is Inverse Deduction?

(15:10) Symbolic AI in Medical Diagnosis

(17:35) The Knowledge Acquisition Bottleneck

(19:05) Why Symbolic AI Struggled with Uncertainty

(20:40) Machine Learning in Symbolic AI – More Than Just Connectionism

(24:08) Decision Trees & Their Role in Symbolic Learning

(26:55) The Myth of Feature Engineering in Deep Learning

(30:18) How Symbolic AI Invents Its Own Rules

(31:54) The Rise and Fall of Expert Systems – The CYCL Project

(38:53) Symbolic AI vs. Connectionism

(41:53) Is Symbolic AI Still Relevant Today?

(43:29) How AlphaGo Combined Symbolic AI & Neural Networks

(45:07) What Symbolic AI is Best At – System 2 Thinking

(47:18) Is GPT-4o Using Symbolic AI?