Atlanta disabled, MyFitnessPal hacked, Cambridge Analytica election tampering, Drupal, Saks, DARPA drones, Cloudflare 1.1.1.1, Slack bosses, Democratic Chinese AIs, Georgia facepalm, tech, humans, ideas, and more…

Atlanta disabled, MyFitnessPal hacked, Cambridge Analytica election tampering, Drupal, Saks, DARPA drones, Cloudflare 1.1.1.1, Slack bosses, Democratic Chinese AIs, Georgia facepalm, tech, humans, ideas, and more&hellip;

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

Exploring the fascinating intersection of security, technology, and humans.

Unsupervised Learning

Unsupervised Learning: No. 119

In this episode, I break down what I believe is the emerging structure of the AI-powered world we're all building—consciously or not. I call it the “Four A’s”: Assistants, APIs, Agents, and Augmented Reality. This framework helps make sense of recent developments and where it’s all headed.

I talk about:

1. Digital Assistants That Understand and Optimize Your Life
Your DA (like “Kai”) will know your goals, preferences, health, schedule, and context—and proactively optimize your day, from filtering messages to planning meals or surfacing relevant information in real time.

2. APIs and the Real Internet of Things
Everything becomes an API—from businesses to people to physical objects. Your assistant interacts with these APIs to act on your behalf, turning the world into a navigable ecosystem of services, tools, and resources.

3. Agents and AR Bringing It All Together
Agents act autonomously to complete multi-step goals, and AR glasses will display their outputs contextually as you move through the world. 

These systems will collaborate, search, and act—quietly transforming how we live, work, and perceive reality.

Subscribe to the newsletter at:
https://danielmiessler.com/subscribe

Join the UL community at:
https://danielmiessler.com/upgrade

Follow on X:
https://x.com/danielmiessler

Follow on LinkedIn:
https://www.linkedin.com/in/danielmiessler

See you in the next one!

Chapters:

00:00 - The AI Ecosystem We’re Building Without Realizing It
01:33 - Assistant: Your Most Powerful Digital Companion
03:08 - APIs: How DAs Interact with the World
07:54 - Agents: The Step Beyond Automation
11:00 - Augmented Reality: The Interface Layer of the AI Ecosystem
14:20 - Combining APIs, Agents, and UI for Real-Time Situational Awareness
17:17 - Summary: A Unified Ecosystem Driven by the Four A’s
23:36 - Industry Trends: How Companies Like OpenAI, Apple, and Meta Fit In
25:11 - Final Thoughts on Timelines, Winners, and Interpreting AI News

In this episode, I break down what I believe is the emerging structure of the AI-powered world we're all building&mdash;consciously or not. I call it the &ldquo;Four A&rsquo;s&rdquo;: Assistants, APIs, Agents, and Augmented Reality. This framework helps make sense of recent developments and where it&rsquo;s all headed.
I talk about:
1. Digital Assistants That Understand and Optimize Your Life Your DA (like &ldquo;Kai&rdquo;) will know your goals, preferences, health, schedule, and context&mdash;and proactively optimize your day, from filtering messages to planning meals or surfacing relevant information in real time.
2. APIs and the Real Internet of Things Everything becomes an API&mdash;from businesses to people to physical objects. Your assistant interacts with these APIs to act on your behalf, turning the world into a navigable ecosystem of services, tools, and resources.
3. Agents and AR Bringing It All Together Agents act autonomously to complete multi-step goals, and AR glasses will display their outputs contextually as you move through the world.&nbsp;
These systems will collaborate, search, and act&mdash;quietly transforming how we live, work, and perceive reality.
Subscribe to the newsletter at: <a href="https://danielmiessler.com/subscribe">https://danielmiessler.com/subscribe</a>
Join the UL community at: <a href="https://danielmiessler.com/upgrade">https://danielmiessler.com/upgrade</a>
Follow on X: <a href="https://x.com/danielmiessler">https://x.com/danielmiessler</a>
Follow on LinkedIn: <a href="https://www.linkedin.com/in/danielmiessler">https://www.linkedin.com/in/danielmiessler</a>
See you in the next one!
Chapters:
00:00 - The AI Ecosystem We&rsquo;re Building Without Realizing It 01:33 - Assistant: Your Most Powerful Digital Companion 03:08 - APIs: How DAs Interact with the World 07:54 - Agents: The Step Beyond Automation 11:00 - Augmented Reality: The Interface Layer of the AI Ecosystem 14:20 - Combining APIs, Agents, and UI for Real-Time Situational Awareness 17:17 - Summary: A Unified Ecosystem Driven by the Four A&rsquo;s 23:36 - Industry Trends: How Companies Like OpenAI, Apple, and Meta Fit In 25:11 - Final Thoughts on Timelines, Winners, and Interpreting AI News

The 4 AAAAs of the AI ECOSYSTEM: Assistants, APIs, Agents, and Augmented Reality

https://api.omny.fm/orgs/070af456-729b-4a0f-9c09-a6c100397b59/clips/7b609f09-0c7b-4917-9d08-b2c7000bad8a/transcript

In this episode, I walk through a Fabric Pattern that assesses how well a given model does on a task relative to humans. This system uses your smartest AI model to evaluate the performance of other AIs—by scoring them across a range of tasks and comparing them to human intelligence levels.

I talk about:

1. Using One AI to Evaluate Another
The core idea is simple: use your most capable model (like Claude 3 Opus or GPT-4) to judge the outputs of another model (like GPT-3.5 or Haiku) against a task and input. This gives you a way to benchmark quality without manual review.

2. A Human-Centric Grading System
Models are scored on a human scale—from “uneducated” and “high school” up to “PhD” and “world-class human.” Stronger models consistently rate higher, while weaker ones rank lower—just as expected.

3. Custom Prompts That Push for Deeper Evaluation
The rating prompt includes instructions to emulate a 16,000+ dimensional scoring system, using expert-level heuristics and attention to nuance. The system also asks the evaluator to describe what would have been required to score higher, making this a meta-feedback loop for improving future performance.

Note: This episode was recorded a few months ago, so the AI models mentioned may not be the latest—but the framework and methodology still work perfectly with current models.

Subscribe to the newsletter at:
https://danielmiessler.com/subscribe

Join the UL community at:
https://danielmiessler.com/upgrade

Follow on X:
https://x.com/danielmiessler

Follow on LinkedIn:
https://www.linkedin.com/in/danielmiessler

See you in the next one!

In this episode, I walk through a Fabric Pattern that assesses how well a given model does on a task relative to humans.&nbsp;This system uses your smartest AI model to evaluate the performance of other AIs&mdash;by scoring them across a range of tasks and comparing them to human intelligence levels.
I talk about:
1. Using One AI to Evaluate Another The core idea is simple: use your most capable model (like Claude 3 Opus or GPT-4) to judge the outputs of another model (like GPT-3.5 or Haiku) against a task and input. This gives you a way to benchmark quality without manual review.
2. A Human-Centric Grading System Models are scored on a human scale&mdash;from &ldquo;uneducated&rdquo; and &ldquo;high school&rdquo; up to &ldquo;PhD&rdquo; and &ldquo;world-class human.&rdquo; Stronger models consistently rate higher, while weaker ones rank lower&mdash;just as expected.
3. Custom Prompts That Push for Deeper Evaluation The rating prompt includes instructions to emulate a 16,000+ dimensional scoring system, using expert-level heuristics and attention to nuance. The system also asks the evaluator to describe what would have been required to score higher, making this a meta-feedback loop for improving future performance.
Note: This episode was recorded a few months ago, so the AI models mentioned may not be the latest&mdash;but the framework and methodology still work perfectly with current models.
Subscribe to the newsletter at: <a href="https://danielmiessler.com/subscribe">https://danielmiessler.com/subscribe</a>
Join the UL community at: <a href="https://danielmiessler.com/upgrade">https://danielmiessler.com/upgrade</a>
Follow on X: <a href="https://x.com/danielmiessler">https://x.com/danielmiessler</a>
Follow on LinkedIn: <a href="https://www.linkedin.com/in/danielmiessler">https://www.linkedin.com/in/danielmiessler</a>
See you in the next one!

Using the Smartest AI to Rate Other AI

https://api.omny.fm/orgs/070af456-729b-4a0f-9c09-a6c100397b59/clips/8a5bf7e7-84d2-4b9b-9080-b2c1013e0fd5/transcript

➡ Secure what your business is made of with Martial Security: 
https://material.security/

In this episode, I speak with Patrick Duffy from Material Security about modern approaches to email and cloud workspace security—especially how to prevent and contain attacks across platforms like Google Workspace and Microsoft 365.

We talk about:

• Proactive Security for Email and Cloud Platforms
How Material goes beyond traditional detection by locking down high-risk documents and inboxes preemptively—using signals like time, access patterns, content sensitivity, and anomalous user behavior.

• Real-World Threats and Lateral Movement
What the team is seeing in the wild—from phishing and brute-force attacks to internal data oversharing—and how attackers are increasingly moving laterally through cloud ecosystems using a single set of compromised credentials.

• Customizable, Context-Aware Response Workflows
How Material helps teams right-size their responses based on risk appetite, enabling fine-grained actions like MFA prompts, access revocation, or full session shutdowns—triggered by dynamic, multi-signal rule sets.

Subscribe to the newsletter at:
https://danielmiessler.com/subscribe

Join the UL community at:
https://danielmiessler.com/upgrade

Follow on X:
https://x.com/danielmiessler

Follow on LinkedIn:
https://www.linkedin.com/in/danielmiessler

Chapters:

00:00 - Welcome & High-Level Overview of Material Security
02:04 - Common Threats: Phishing and Lateral Movement in Cloud Office
05:30 - Access Control in Collaborative Workspaces (2FA, Just-in-Time, Aging Content)
08:43 - Connecting Signals: From Login to Exfiltration via Rule Automation
12:25 - Real-World Scenario: Suspicious Login and Automated Response
15:08 - Rules, Templates, and Customer Customization at Onboarding
18:46 - Accidental Risk: Sensitive Document Sharing and Exposure
21:04 - Security Misconfigurations and Internal Abuse Cases
23:43 - Full Control Points: IP, Behavior, Classification, Sharing Patterns
27:50 - Integrations, Notifications, and Real-Time Security Team Coordination
31:13 - Lateral Movement: How Attacks Spread Across the Workspace
34:25 - Use Cases Involving Google Gemini and AI Exposure Risks
36:36 - Upcoming Features: Deeper Remediation and Contextual Integration
39:30 - Closing Thoughts and Where to Learn More

➡ Secure what your business is made of with Martial Security:&nbsp; <a href="https://material.security/">https://material.security/</a>
In this episode, I speak with Patrick Duffy from Material Security about modern approaches to email and cloud workspace security&mdash;especially how to prevent and contain attacks across platforms like Google Workspace and Microsoft 365.
We talk about:
&bull; Proactive Security for Email and Cloud Platforms How Material goes beyond traditional detection by locking down high-risk documents and inboxes preemptively&mdash;using signals like time, access patterns, content sensitivity, and anomalous user behavior.
&bull; Real-World Threats and Lateral Movement What the team is seeing in the wild&mdash;from phishing and brute-force attacks to internal data oversharing&mdash;and how attackers are increasingly moving laterally through cloud ecosystems using a single set of compromised credentials.
&bull; Customizable, Context-Aware Response Workflows How Material helps teams right-size their responses based on risk appetite, enabling fine-grained actions like MFA prompts, access revocation, or full session shutdowns&mdash;triggered by dynamic, multi-signal rule sets.
Subscribe to the newsletter at: <a href="https://danielmiessler.com/subscribe">https://danielmiessler.com/subscribe</a>
Join the UL community at: <a href="https://danielmiessler.com/upgrade">https://danielmiessler.com/upgrade</a>
Follow on X: <a href="https://x.com/danielmiessler">https://x.com/danielmiessler</a>
Follow on LinkedIn: <a href="https://www.linkedin.com/in/danielmiessler">https://www.linkedin.com/in/danielmiessler</a>
Chapters:
00:00 - Welcome &amp; High-Level Overview of Material Security 02:04 - Common Threats: Phishing and Lateral Movement in Cloud Office 05:30 - Access Control in Collaborative Workspaces (2FA, Just-in-Time, Aging Content) 08:43 - Connecting Signals: From Login to Exfiltration via Rule Automation 12:25 - Real-World Scenario: Suspicious Login and Automated Response 15:08 - Rules, Templates, and Customer Customization at Onboarding 18:46 - Accidental Risk: Sensitive Document Sharing and Exposure 21:04 - Security Misconfigurations and Internal Abuse Cases 23:43 - Full Control Points: IP, Behavior, Classification, Sharing Patterns 27:50 - Integrations, Notifications, and Real-Time Security Team Coordination 31:13 - Lateral Movement: How Attacks Spread Across the Workspace 34:25 - Use Cases Involving Google Gemini and AI Exposure Risks 36:36 - Upcoming Features: Deeper Remediation and Contextual Integration 39:30 - Closing Thoughts and Where to Learn More

A Conversation with Patrick Duffy from Material Security

https://api.omny.fm/orgs/070af456-729b-4a0f-9c09-a6c100397b59/clips/eabe6af1-fb43-48e0-9840-b2c0001e9dba/transcript

Unsupervised Learning: No. 119

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