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    News

    Unsupervised Learning

    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.

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    Copyright: © 2024 Daniel Miessler

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    Latest Episodes:
    Unsupervised Learning: No. 120 Apr 09, 2018
    Show notes

    It's 2 billion users now, Liinux beep, Digital Shadows finds fail files, cloud misconfiguration, AlterEgo, AI applications, Alexa sending payments, Tech, Ideas, Recommendation, Aphorism, and more…

    Become a Member: https://danielmiessler.com/upgrade

    See omnystudio.com/listener for privacy information.


    Unsupervised Learning: No. 119 Apr 02, 2018
    Show notes

    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…

    Become a Member: https://danielmiessler.com/upgrade

    See omnystudio.com/listener for privacy information.


    Unsupervised Learning: No. 116 Mar 13, 2018
    Show notes

    Chinese at CanSecWest, Applebees POS, Palantir, Poisoning, TensorFlow DoD, Amazon laughing, Google 72-qbits, Amazon FinTech, Android P, and more…

    Become a Member: https://danielmiessler.com/upgrade

    See omnystudio.com/listener for privacy information.


    Unsupervised Learning: No. 115 Mar 06, 2018
    Show notes

    GitHub DDoS, Celebrite Attacks, AI warnings, Palantir in New Orleans, Grub Backspace, 4G attacks, Space Corps, Amazon wins Defense Department deal, tech news, human news, discovery, notes, recommendation, aphorism, and more…

    Become a Member: https://danielmiessler.com/upgrade

    See omnystudio.com/listener for privacy information.


    Unsupervised Learning: No. 113 Feb 20, 2018
    Show notes

    Parkland tampering, Avoid Huawei, Bongo S3, Facebook 2FA Spam, Android Cryptojacking, Spyware Hacking, Password Dating, Technology News, Human News, Trends, Ideas & Analysis, Data & Statistics, Discovery, Recommendations, Aphorism, and more…

    Become a Member: https://danielmiessler.com/upgrade

    See omnystudio.com/listener for privacy information.


    Unsupervised Learning: No. 112 Feb 12, 2018
    Show notes

    Chinese AR glasses, Cisco ASA flaws, Russian Nuclear Cryptomining, Marine quadcopters, POS Skimmers, Chrome HTTP, technology news, human news, discovery, notes, recommendations, and the aphorism of the week…

    Become a Member: https://danielmiessler.com/upgrade

    See omnystudio.com/listener for privacy information.


    Unsupervised Learning: No. 111 Feb 05, 2018
    Show notes

    Olympic security drones, Alexa trickery, Chinese quantum satellite, Audio Adversary Examples, BeeToken Ethereum theft, App Store Security, Cryptomining, technology news, human news, discovery, notes, recommendations, and the aphorism of the week…

    Become a Member: https://danielmiessler.com/upgrade

    See omnystudio.com/listener for privacy information.


    Unsupervised Learning: No. 109 Jan 22, 2018
    Show notes

    Social engineering, breach impact, Chinese turncoat, Android spy kit, Hawaiian OPSEC, Russian cables, bypassing CloudFlare, technology news, human news, discovery, notes, recommendations, and the aphorism of the week…

    Become a Member: https://danielmiessler.com/upgrade

    See omnystudio.com/listener for privacy information.


    Unsupervised Learning: No. 107 Jan 08, 2018
    Show notes

    Meltdown & Spectre, India's Database, Criminals and Monero, Equifax Non-action, technology news, human news, discovery, notes, recommendations, and the aphorism of the week…

    Become a Member: https://danielmiessler.com/upgrade

    See omnystudio.com/listener for privacy information.


    The Biggest Advantage in Machine Learning Will Come From Superior Coverage, Not Superior Analysis Jan 03, 2018
    Show notes

    Many people, in many fields, think Machine Learning won't replace their analysts because their humans are better than an algorithm. But it's not just about side-by-side comparisons. The bigger question is, "what percentage of the data can humans actually look at?", and the answer to that question (a tiny fraction) is the reason ML will be so helpful.

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