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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

This episode is sponsored by Thuma.

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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.

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(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?

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In this episode of Eye on AI, Mark Bailey, Associate Professor at the National Intelligence University, joins Craig Smith to explore the rapidly evolving role of AI in modern warfare—its promises, risks, and the ethical dilemmas it presents.

Mark shares his expertise on AI autonomy in military strategy, breaking down the differences between automation and true autonomy. We discuss how AI-driven systems could revolutionize combat by reducing human casualties, improving precision, and enhancing battlefield decision-making. But with these advancements come serious concerns—how do we prevent automation bias? Can we trust AI to make life-or-death decisions? And will AI-driven warfare lower the threshold for conflict, making war more frequent?

We also examine the global AI arms race, the impact of AI on defense policies, and the ethical implications of fully autonomous weapons. Mark unpacks key challenges like the black box problem, AI alignment issues, and the long-term consequences of integrating AI into military operations. He also shares insights from his latest book, where he calls for international AI regulations to prevent an uncontrolled escalation of AI warfare.

With AI-driven drone swarms, autonomous targeting systems, and defense innovations shaping the future of global security, this conversation is a must-watch for anyone interested in AI, defense technology, and the moral questions of war in the digital age.

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(00:00) AI’s Role in Warfare

(02:02) Introducing Dr. Mark Bailey

(04:02) Automation vs. Autonomy in Military AI

(12:02) AI Warfare: A Threat to Global Stability?

(17:10) Inside Dr. Bailey’s Book: Ethics & AI in War

(20:05) AI Reliability in Warfare

(23:28) The Future of AI Swarms & Autonomous Warfare

(24:17) Who Decides How AI is Used in War?

(28:05) The Future of AI & Military Ethics

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.

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(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

This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more.

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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/

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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 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

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!

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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

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!

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(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

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