Inside the Dark Web, AI and Cybersecurity with Christopher Ahlberg CEO of Recorded Future

Weights & Biases · Beginner ·🚀 Entrepreneurship & Startups ·11mo ago
In this episode of Gradient Dissent, host Lukas Biewald talks with Christopher Ahlberg, CEO of Recorded Future, a pioneering cybersecurity company leveraging AI to provide intelligence insights. Christopher shares his fascinating journey from founding data visualization startup Spotfire to building Recorded Future into an industry leader, eventually leading to its acquisition by Mastercard. They dive into gripping stories of cyber espionage, including how Recorded Future intercepted a hacker selling access to the U.S. Electoral Assistance Commission. Christopher also explains why the criminal…
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Chapters (13)

What is Recorded Future? Using the internet as an intelligence sensor
4:27 How Recorded Future finds threats on the dark web
7:24 Criminal marketplaces, Telegram, and ransomware-as-a-service
11:49 Starting Recorded Future: The idea behind connecting analytics to the internet
18:05 From entity extraction to generative reporting with LLMs
21:55 ChatGPT, the intelligence community, and adoption challenges
23:37 Helping Ukraine during the war: AI deployed in real-world conflict
27:19 How Recorded Future decides which governments to work with
34:22 Democracy, the internet, and AI manipulation risks
37:22 Election interference, phishing, and AI-powered cyberattacks
40:48 Signal vs. Telegram and why bad actors flock to Telegram
46:14 Russian threats and personal safety concerns
47:08 The Mastercard acquisition and what’s next for Reco

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0. What is machine learning?
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1. Build Your First Machine Learning Model
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4. Autoencoders
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8. Text Classification Using Convolutional Neural Networks
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9. Hybrid LSTMs [Long Short-Term Memory]
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 14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
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13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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Applied Deep Learning - Data Management with Josh Tobin (2019)
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 32 Designing a Machine Learning Project with Neal Khosla (2019)
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 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
Lukas Beiwald on ML Tools and Experiment Management (2019)
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 34 Building Machine Learning Teams with Josh Tobin (2019)
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 35 Pieter Abeel on Potential Deep Learning Research Directions (2019)
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 37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
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 39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
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 44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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 45 VDLS Lavanya Product Walkthrough
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 46 Testing Machine Learning Models with Eric Schles
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 51 Sequence Models with Pujaa Rajan
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 52 GitHub Actions & Machine Learning Workflows with Hamel Husain
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 56 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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