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

The conversation around AGI and ASI is louder than ever—but the definitions are often abstract, technical, and disconnected from what actually matters. In this episode, I break down a human-centered way of thinking about these terms, why they’re important, and a system that could help us get there.

I talk about: 

• A Better Definition of AGI and ASI
Instead of technical abstractions, AGI is defined as the ability to perform most cognitive tasks as well as a 2022 U.S.-based knowledge worker. ASI is intelligence that surpasses that level. Framing it this way helps us immediately understand why it matters—and what it threatens.

• Invention as the Core Output of Intelligence
The real value of AGI and ASI is their ability to generate novel solutions. Drawing inspiration from the Enlightenment, we explore how humans innovate—and how we can replicate that process using AI, automation, and structured experimentation.

• Scaling the Scientific Method with AI
By building systems that automate idea generation, recombination, and real-world testing, we can massively scale the rate of innovation. This framework—automated scientific iteration—could be the bridge from human intelligence to AGI and beyond.

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https://danielmiessler.com/upgrade

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https://www.linkedin.com/in/danielmiessler

Chapters:

00:00 - Why AGI and ASI Definitions Should Be Human-Centric
01:55 - Defining AGI as a 2022-Era US Knowledge Worker
03:04 - Defining ASI and Why It’s Harder to Conceptualize
04:04 - The Real Reason to Care: AGI and ASI Enable Invention
05:04 - How Human Innovation Happens: Idea Collisions and Enlightenment Lessons
06:56 - Building a System That Mimics Human Idea Generation at Scale
09:00 - The Challenge of Testing: From A/B Tests to Biotech Labs
10:52 - Creating an Automated, Scalable Scientific Method With AI
12:50 - A Timeline to AGI and ASI: Predictions for 2027–2030

The conversation around AGI and ASI is louder than ever&mdash;but the definitions are often abstract, technical, and disconnected from what actually matters. In this episode, I break down a human-centered way of thinking about these terms, why they&rsquo;re important, and a system that could help us get there.
I talk about:&nbsp;
&bull; A Better Definition of AGI and ASI Instead of technical abstractions, AGI is defined as the ability to perform most cognitive tasks as well as a 2022 U.S.-based knowledge worker. ASI is intelligence that surpasses that level. Framing it this way helps us immediately understand why it matters&mdash;and what it threatens.
&bull; Invention as the Core Output of Intelligence The real value of AGI and ASI is their ability to generate novel solutions. Drawing inspiration from the Enlightenment, we explore how humans innovate&mdash;and how we can replicate that process using AI, automation, and structured experimentation.
&bull; Scaling the Scientific Method with AI By building systems that automate idea generation, recombination, and real-world testing, we can massively scale the rate of innovation. This framework&mdash;automated scientific iteration&mdash;could be the bridge from human intelligence to AGI and beyond.
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 - Why AGI and ASI Definitions Should Be Human-Centric 01:55 - Defining AGI as a 2022-Era US Knowledge Worker 03:04 - Defining ASI and Why It&rsquo;s Harder to Conceptualize 04:04 - The Real Reason to Care: AGI and ASI Enable Invention 05:04 - How Human Innovation Happens: Idea Collisions and Enlightenment Lessons 06:56 - Building a System That Mimics Human Idea Generation at Scale 09:00 - The Challenge of Testing: From A/B Tests to Biotech Labs 10:52 - Creating an Automated, Scalable Scientific Method With AI 12:50 - A Timeline to AGI and ASI: Predictions for 2027&ndash;2030

A Possible Path to ASI

https://api.omny.fm/orgs/070af456-729b-4a0f-9c09-a6c100397b59/clips/d05d8ebc-1da2-44f2-b859-b2b9017492b0/transcript

➡ Build, run, and monitor workflows with Tines at: tines.com

In this episode, I speak with Matt Muller, Field CSCO at Tines, about how automation and AI are transforming security operations at scale.

We talk about:

• Tines' Mission to Eliminate Manual Security Work Through Automation
 How Tines helps security teams streamline incident response and workflow automation without needing to write code, saving time and reducing burnout.

• Applying AI to Security Operations and Analyst Workflows
 How AI is used in phishing analysis, threat intel reporting, and data transformation—integrated safely into workflows using tools like Workbench with private LLMs.

• Tines Workbench and the Future of Agentic AI
 How Workbench combines chat with deterministic automation to help analysts take action securely, and how Tines is exploring agentic AI to take automation even further.

Chapters:

00:00 - How Tines Automates Security to Solve SOC Burnout
07:19 - The AI Arms Race: How Attackers and Defenders Are Evolving
09:08 - Why Security Still Comes Down to Workflow, Logging, and Action
13:41 - How CISOs Are Balancing AI Adoption and Enterprise Risk
17:36 - Using AI in Tines to Transform and Automate Security Workflows
20:40 - How AI Detects Business Email Compromise Better Than Rules
25:26 - From Security to Data Pipelines: Tines as Workflow Orchestration
28:59 - Inside Workbench: Secure AI-Powered Chat for Analysts
36:00 - Automating Phishing Investigations with Trusted Tool Integrations
39:19 - Where to Learn More and Try Tines for Free

➡ Build, run, and monitor workflows with Tines at: <a href="https://www.tines.com/">tines.com</a>
In this episode, I speak with Matt Muller, Field CSCO at Tines, about how automation and AI are transforming security operations at scale.
We talk about:
&bull; Tines' Mission to Eliminate Manual Security Work Through Automation How Tines helps security teams streamline incident response and workflow automation without needing to write code, saving time and reducing burnout.
&bull; Applying AI to Security Operations and Analyst Workflows How AI is used in phishing analysis, threat intel reporting, and data transformation&mdash;integrated safely into workflows using tools like Workbench with private LLMs.
&bull; Tines Workbench and the Future of Agentic AI How Workbench combines chat with deterministic automation to help analysts take action securely, and how Tines is exploring agentic AI to take automation even further.
Chapters:
00:00 - How Tines Automates Security to Solve SOC Burnout 07:19 - The AI Arms Race: How Attackers and Defenders Are Evolving 09:08 - Why Security Still Comes Down to Workflow, Logging, and Action 13:41 - How CISOs Are Balancing AI Adoption and Enterprise Risk 17:36 - Using AI in Tines to Transform and Automate Security Workflows 20:40 - How AI Detects Business Email Compromise Better Than Rules 25:26 - From Security to Data Pipelines: Tines as Workflow Orchestration 28:59 - Inside Workbench: Secure AI-Powered Chat for Analysts 36:00 - Automating Phishing Investigations with Trusted Tool Integrations 39:19 - Where to Learn More and Try Tines for Free

A Conversation With Matt Muller From Tines

https://api.omny.fm/orgs/070af456-729b-4a0f-9c09-a6c100397b59/clips/7f7a0de7-1629-4462-9ffb-b2b10132de44/transcript

STANDARD EDITION: Signal OPSEC, White-box Red-teaming LLMs, Unified Company Context (UCC), New Book Recommendations, Single Apple Note Technique, and much more...

You are currently listening to the Standard version of the podcast, consider upgrading and becoming a member to unlock the full version and many other exclusive benefits here: https://newsletter.danielmiessler.com/upgrade

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

STANDARD EDITION: Signal OPSEC, White-box Red-teaming LLMs, Unified Company Context (UCC), New Book Recommendations, Single Apple Note Technique, and much more...
You are currently listening to the Standard version of the podcast, consider upgrading and becoming a member to unlock the full version and many other exclusive benefits here:&nbsp;<a href="https://newsletter.danielmiessler.com/upgrade">https://newsletter.danielmiessler.com/upgrade</a>
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>

UL NO. 474 | Signal OPSEC, White-box Red-teaming LLMs, Unified Company Context (UCC), New Book Recommendations, Single Apple Note Technique, and much more...

https://api.omny.fm/orgs/070af456-729b-4a0f-9c09-a6c100397b59/clips/e6e569a5-c191-49a8-944f-b2b1012f5736/transcript

Unsupervised Learning: No. 119

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