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Daniel Miessler

Unsupervised Learning is about ideas and trends in Cybersecurity, National Security, AI, Technology, and Culture—and how best to upgrade ourselves to be ready for what's coming.

Unsupervised Learning

In this episode, Daniel Miessler explores how AI can transform our understanding of the present and create actionable paths for a better future.
 
He talks about:
 
The Current State, Desired State, and Transition in AI Applications:
How AI frameworks can analyze the current state, define a desired state, and propose action plans to address challenges in education, climate, health, and beyond.
 
The Infrastructure and Scale of AI:
Why we’re only at the beginning of building the AI infrastructure required for future demands, from GPUs and data centers to startups pushing the boundaries of what’s possible.
 
The Role of AI in Human and Organizational Development:
How AI can revolutionize personal lives, enhance businesses, and solve societal issues by gathering and analyzing massive amounts of contextual data to provide actionable insights.
 
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Chapters:
 
0:00 - Introduction to Unsupervised Learning Podcast 
1:10 - Concept: Predicting AI Infrastructure Needs
3:45 - The Challenge of Predicting Technology vs Human Desires
6:20 - Exploring AI Infrastructure Metrics (GPUs, Data Centers, Startups)
8:55 - Philosophical Insight: Current State vs Desired State 
12:15 - AI’s Role in Learning from the Past and Anticipating the Future 
14:50 - Addressing Global Issues with AI (Education, Poverty, Climate) 
18:30 - Transitioning from Current State to Desired State 
22:05 - Context Gathering: Granularity and Technology Limitations 
25:40 - AI's Impact on Individual and Family Contexts 
29:10 - AI’s Potential in Business: Granularity and Cost 
32:50 - Vision of Life OS and Personalized Assistance 
36:15 - AI in Society: Predicting and Preventing Problems 
40:00 - Infinite Context and the Scaling of AI Capabilities 
44:30 - Predictions on AI Context Size and Infrastructure Demand 
48:20 - The Importance of Understanding the Current State 
52:10 - Conclusion

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How Much AI Do We Need? - My AI Industry Prediction

...plus building personal TELOS files, the ChatGPT Pro vs. Claude coding face-off, a human bird flu case in Louisiana, and ketones fighting Alzheimer’s.
 
➡ Make your app enterprise-ready and start selling to enterprise customers with WorkOS: workos.com
 
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UL NO. 462: Full-Face Mask Deceptions, VS Code Tunnel Hacks, Quiet AI Emergence at Apple, and Tokyo’s Three-Day Weekend Gamble

Navigating AI's impact on work, the rise of transnational threats, a grim new reality in air travel, and how to harness the chaos of 2025 for personal and professional growth.
 
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UL NO. 463 | Launching 2025, US Soldier Data Leak, AI Agents Emerge, China's Global Spy Network, Robotaxis Now Safer Than Humans

also...Russia's actual playbook, CISA's new rating system, and everyone's doing robots now
 
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UL NO. 464 | AI Phishing Matches Humans, Under Sea Cable Cutter Patents, and Siri is About to Not Suck

also...Joseph goes independent, Perplexity's new search API, Stoicism's gift, and much more...
 
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UL NO. 465 | The SaaS Attack Vector, Project Stargate, and Undersea Cable Drones

In this episode, I speak with Faisal Khan, a GRC Solution Specialist at Vanta, about how their platform is transforming trust management for organizations.
 
We talk about:
 
Vanta as a Trust-Management Platform:
How Vanta helps organizations build, scale, and showcase their security and compliance programs through automation, efficiency, and tools like the Trust Center.
 
Key Features and Solutions Offered by Vanta:
How Vanta’s integrations automate compliance checks, streamline vendor risk management, and address industry standards like SOC 2, ISO 27001, and CMMC to save time and improve efficiency.
 
Future Directions and AI Integration:
How Vanta is expanding into new frameworks like the EU AI Act and leveraging AI to simplify compliance, optimize workflows, and address evolving trends in governance and security.

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A Conversation with Faisal Khan from Vanta

Plus: The AI Vulnerability Glut, Remotely Hacking Subarus, Criticism of CVSS, the United Breach, and much more...
 
➡ Protect Against Bots, Fraud, and Abuse. Check out WorkOS Radar at workos.com/radar
 
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UL NO. 466 | My Analysis and Prediction on the Deepseek Situation

In this conversation, I speak with Alastair Paterson, CEO and co-founder of Harmonic Security.
 
We talk about:
 
Harmonic Security’s Unique Approach to AI Data Protection:
 
How Harmonic Security’s Zero-Touch Data Protection uses small language models to identify and prevent sensitive data leaks, differentiating it from traditional DLP solutions.
 
Challenges of AI Adoption & Enterprise Security Risks:
 
How enterprises are struggling to adopt Generative AI safely, as employees unknowingly expose sensitive data. The risks of shadow AI usage, and why visibility into AI applications is essential for organizations.
 
Harmonic’s Browser-Based Solution for Secure AI Adoption:
 
How Harmonic Security’s browser-based extension provides real-time monitoring and intervention, allowing enterprises to track AI adoption, prevent data leaks, and enforce security policies without disrupting productivity.
 
➡️ Get a DEMO and Take Advantage of Harmonic's GenAI Securely 
 
ul.live/harmonic
 
➡️ Check out Harmonic's Data leakage report "From Payrolls to Patents"
ul.live/harmonic-data-leaked

00 Intro
00:12 Guest Introduction - Alistair and Harmonic Security
01:16 Background on Digital Shadows and Transition to Harmonic Security
02:50 The Impact of ChatGPT and Generative AI on Security
04:35 The Problem with AI Data Leakage and Enterprise Risks
06:20 The Evolution of Data Protection: From DLP to AI Readiness
08:45 The Challenge of Shadow AI in Enterprises
10:30 Understanding Harmonic Security's Zero-Touch Data Protection
12:15 How Harmonic Security Works - Browser Extension Overview
14:40 Detecting Sensitive Data in AI Prompts
16:50 Live Demo - Preventing Data Leaks in AI Chatbots
19:35 Visibility and Monitoring of AI Usage Across the Enterprise
22:10 Risk Classification and Training Data Considerations
24:05 Policy Enforcement and Customization Options
26:30 Future Developments - Expanding Coverage Beyond AI Apps
28:15 Final Thoughts and Where to Learn More

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A Conversation with Alastair Paterson from Harmonic Security

I want to explore how AI can assist in fiction writing, especially using open-source models that allow for greater control, creativity, and long-form storytelling. With tools like LM Studio and Hugging Face, we can download powerful AI models capable of maintaining story coherence, helping authors generate complex narratives, and even unlocking new storytelling possibilities.
 
So, the idea is to create a structured approach to fiction writing with AI. By organizing story elements—characters, setting, mystery, and plot—into a detailed text file ("Telus file"), we can guide AI models to produce high-quality, structured narratives. The goal is not to replace authors but to empower them with AI-assisted storytelling.
 
Who wants to experiment with this approach? Or does anyone know of better AI tools for fiction writing? With larger context models and improving AI capabilities, we might be close to AI-assisted novels that rival human-written stories!
 
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Writing Fiction With AI

Plus: DeepSeek's open database, Using o3 with Fabric, Chinese backdoors in health monitors, and much more...
 
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UL NO. 467 | Why You Should Care About AGI (And a Definition)

Ring is developing two-way relationships with hundreds of police departments in the US. This allows Ring users to be alerted to crime in their area via 911 data, and police departments to pull video from participating Ring devices. This is the type of functionality that most people will see and think, “Wow, I'd love to have that!”, which is why it's going to be very successful. But it's also one tiny step away from something terrifying. More

A number of critical bugs in VxWorks are going to cause issues with infrastructure for years to come. More

DARPA is building a $10 million dollar, open source voting system with a focus on security. More

It looks like China's social credit system might not be a giant monolithic system, but rather a series of siloed experiments. More

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Unsupervised Learning: No. 189

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