azcomputerguru
4e84a7f810
Synced files: - Session logs updated - Latest context and credentials - Command/directive updates Machine: Mikes-MacBook-Air.local Timestamp: 2026-03-16 06:58:31 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
903 lines
42 KiB
HTML
903 lines
42 KiB
HTML
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</head>
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<body>
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<div class="page">
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<!-- HEADER -->
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<div class="show-header">
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<h1>AI Misconceptions - Talking Points</h1>
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<div class="show-meta">
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<strong>Air Date:</strong> 2026-03-14 | <strong>Host:</strong> Mike Swanson<br>
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<strong>Format:</strong> ~44 min main show (9 segments) + filler segments available | <strong>Pace:</strong> ~150 words/min conversational
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</div>
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</div>
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<!-- MAIN SHOW BANNER -->
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<div class="section-banner">Main Show -- 9 Segments</div>
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<!-- SEGMENT 1 -->
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<div class="segment">
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<div class="segment-head">
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<span class="seg-number">1</span>
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<span class="seg-title">"Five Years Later"</span>
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<span class="seg-label">Intro | ~4 min</span>
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</div>
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<div class="seg-theme">Welcome back -- a lot has changed</div>
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<ul>
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<li>Last on air 2021 -- ChatGPT didn't exist yet, AI was sci-fi and Amazon recommendations</li>
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<li><strong class="stat">1 BILLION</strong> people interact with AI every week now</li>
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<li>ChatGPT hit <strong class="stat">1 million users in 5 DAYS</strong> (Netflix took 3.5 years, Instagram 2.5 months)</li>
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<li><strong class="stat">800 million</strong> weekly ChatGPT users, fewer than <strong class="stat">2%</strong> pay</li>
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<li><strong class="stat">92%</strong> of Fortune 100 companies integrated AI</li>
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<li><strong class="stat">86%</strong> of students using AI for schoolwork</li>
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<li><strong class="stat">2/3</strong> of people prefer ChatGPT over Google for info</li>
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<li>2025 = chatbots, 2026 = autonomous agents</li>
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<li>The gap between what people THINK AI can do and what it DOES -- that's where people get hurt</li>
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<li>Preview: poetry vs. letter counting, confidence when wrong, teen mental health, agents acting on your behalf</li>
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</ul>
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<div class="takeaway">
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<span class="takeaway-label">Key Takeaway</span>
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Not here to say AI is amazing or terrible -- here to explain what it actually IS.
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</div>
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<details>
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<summary>Q&A Bullets (if needed)</summary>
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<ul>
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<li>Biggest change = scale: niche research to 1B weekly users</li>
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<li>Shift from "search engine" to "conversation" mentality</li>
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<li>Not worried sci-fi style, but real harms: misinfo, scams, over-reliance</li>
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<li><strong class="stat">47%</strong> of executives acted on hallucinated content</li>
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<li>Voice cloning scams up <strong class="stat">680%</strong> -- 1 in 4 Americans already fooled</li>
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<li>Healthy approach: understand it, use it wisely, verify claims</li>
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</ul>
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</details>
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</div>
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<!-- SEGMENT 2 -->
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<div class="segment">
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<div class="segment-head">
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<span class="seg-number">2</span>
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<span class="seg-title">"Strawberry Has How Many R's?"</span>
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<span class="seg-label">Tokenization | ~4 min</span>
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</div>
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<div class="seg-theme">AI doesn't see words the way you do</div>
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<ul>
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<li>Ask AI "how many R's in strawberry?" -- it says 2 (answer is 3)</li>
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<li><strong>TOKENIZATION:</strong> AI breaks text into chunks, not letters</li>
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<li>"strawberry" becomes "st" + "raw" + "berry" -- never sees full word letter by letter</li>
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<li>Analogy: counting letters in a sentence someone cut into random pieces and shuffled</li>
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<li>Not a bug -- it's the architecture. Optimized for meaning, not spelling</li>
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<li>Analogy: someone fluent in a foreign language who can't spell the words</li>
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<li>Newer 2025-2026 models showing "tokenization awareness" -- learning to work around blind spots</li>
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</ul>
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<div class="takeaway">
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<span class="takeaway-label">Key Takeaway</span>
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AI reads chunks, not letters. Writes poetry, can't count letters.
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</div>
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<details>
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<summary>Q&A Bullets (if needed)</summary>
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<ul>
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<li>Matters because it reveals AI processes info fundamentally differently than humans</li>
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<li>"Looking human" and "working like a human" are completely different</li>
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<li>Same issue causes math errors, logic gaps, hallucinations</li>
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<li>AI might confidently give wrong phone numbers, addresses, calculations</li>
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<li>Understanding the limitation helps you use the tool better</li>
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</ul>
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</details>
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</div>
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<!-- SEGMENT 3 -->
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<div class="segment">
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<div class="segment-head">
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<span class="seg-number">3</span>
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<span class="seg-title">"Confidently Wrong"</span>
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<span class="seg-label">Hallucination | ~5 min</span>
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</div>
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<div class="seg-theme">AI makes things up and sounds sure about it</div>
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<ul>
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<li>GPTZero scanned 300 papers at ICLR (top AI conference) -- <strong class="stat">50+</strong> had OBVIOUS hallucinations</li>
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<li>Fabricated citations, made-up stats, nonexistent papers</li>
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<li>Each hallucination missed by <strong class="stat">3-5 peer reviewers</strong> -- experts couldn't catch them either</li>
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<li>Science study: AI uses <strong class="stat">34% MORE CONFIDENT</strong> language when generating INCORRECT info</li>
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<li>Words like "definitely," "certainly," "without doubt" = red flags</li>
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<li><strong class="stat">47%</strong> of executives made business decisions on hallucinated content</li>
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<li>Cost: <strong class="stat">$18K</strong> (customer service) up to <strong class="stat">$2.4M</strong> (healthcare malpractice)</li>
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<li>Robo-advisor hallucination: <strong class="stat">2,847</strong> client portfolios, <strong class="stat">$3.2M</strong> to fix</li>
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<li>NY attorney fined -- ChatGPT fabricated <strong class="stat">21 court cases</strong> (Mata v. Avianca)</li>
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<li>~500 similar lawyer incidents worldwide since</li>
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<li>Best models: <strong class="stat">0.7%</strong> hallucination on basic tasks</li>
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<li>Complex topics: legal <strong class="stat">18.7%</strong>, medical <strong class="stat">15.6%</strong></li>
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<li>"Reasoning" models actually WORSE on grounded summarization (>10% on hard benchmarks)</li>
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<li>Duke: "sounding good is far more important than being correct"</li>
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</ul>
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<div class="takeaway">
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<span class="takeaway-label">Key Takeaway</span>
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AI doesn't know what it doesn't know. Never says "I'm not sure." Treat claims like tips from a confident stranger -- verify.
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</div>
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<details>
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<summary>Q&A Bullets (if needed)</summary>
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<ul>
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<li>Always verify citations independently -- AI invents legitimate-looking sources</li>
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<li>More confident it sounds, more skeptical you should be</li>
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<li>Use AI as starting point, not finishing point</li>
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<li>GPTZero now offers "Hallucination Check" features</li>
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<li>Australian gov spent <strong class="stat">$440K</strong> on report with hallucinated sources</li>
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<li>Top models improved (15-20% down to <1% basic) but complex topics still bad</li>
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<li>No model has solved this -- OpenAI admits training process rewards guessing</li>
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</ul>
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</details>
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</div>
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<!-- SEGMENT 4 -->
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<div class="segment">
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<div class="segment-head">
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<span class="seg-number">4</span>
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<span class="seg-title">"Your Voice in Three Seconds"</span>
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<span class="seg-label">Voice Cloning | ~4 min</span>
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</div>
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<div class="seg-theme">Voice cloning scams exploding -- you can't tell the difference</div>
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<ul>
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<li><strong class="stat">1 in 4 Americans HAS BEEN fooled</strong> by AI voice (not "could be" -- HAS BEEN)</li>
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<li>Clone a voice from <strong class="stat">3 SECONDS</strong> of audio (half a voicemail greeting)</li>
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<li>Tools: Microsoft VALL-E 2, OpenAI Voice Engine</li>
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<li>Crossed the "indistinguishable threshold" -- old tells (robotic, weird pauses) gone</li>
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<li>Voice cloning fraud up <strong class="stat">680%</strong> past year</li>
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<li>Major retailers: <strong class="stat">1,000+ AI scam calls PER DAY</strong></li>
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<li>Average loss per deepfake fraud: <strong class="stat">$500K+</strong></li>
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<li>Most common: call sounding like child/grandparent in distress needing money NOW</li>
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<li><strong class="stat">$25M</strong> case: finance worker transferred after video call -- CFO and colleagues were ALL deepfakes</li>
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<li>"Jury duty warrant" scam growing in 2026 -- cloned law enforcement voices</li>
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<li><strong>DEFENSE: Family safe word</strong> -- "purple cactus," "midnight protocol"</li>
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<li>FTC and cybersecurity firms universally recommend it</li>
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<li>AI clone can't guess a password it was never trained on</li>
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<li>McAfee Deepfake Detector: 96% accuracy, flags in 3 seconds (but arms race)</li>
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</ul>
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<div class="takeaway">
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<span class="takeaway-label">Key Takeaway</span>
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Call sounding like someone you know asking for money? Hang up. Call them back on a trusted number. Get a family safe word.
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</div>
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<details>
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<summary>Q&A Bullets (if needed)</summary>
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<ul>
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<li>You probably can't detect AI voice anymore -- behavioral defense, not technical</li>
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<li>Hang up and call back on known number</li>
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<li>Ask question only real person would know</li>
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<li><strong class="stat">77%</strong> of victims who ENGAGED with AI scam calls lost money -- don't engage</li>
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<li>If you have audio online (videos, podcasts), technically your voice can be cloned</li>
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<li>Report suspicious calls: reportfraud.ftc.gov</li>
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<li>If already sent money: contact bank immediately, file police report</li>
|
|
</ul>
|
|
</details>
|
|
</div>
|
|
|
|
<!-- SEGMENT 5 -->
|
|
<div class="segment">
|
|
<div class="segment-head">
|
|
<span class="seg-number">5</span>
|
|
<span class="seg-title">"The AI Therapist Problem"</span>
|
|
<span class="seg-label">Teen Mental Health | ~5 min</span>
|
|
</div>
|
|
<div class="seg-theme">Teens using chatbots for mental health. Experts say dangerous.</div>
|
|
<ul>
|
|
<li><strong class="stat">1 in 8 teens</strong> using AI chatbots for mental health advice</li>
|
|
<li>Pew: <strong class="stat">64%</strong> of adolescents using chatbots, 3 in 10 daily, <strong class="stat">72%</strong> used AI companions at least once</li>
|
|
<li>Common Sense Media + Stanford: ALL major platforms FAILED (ChatGPT, Claude, Gemini, Meta AI)</li>
|
|
<li>Core problem: "missing breadcrumbs" -- AI processes each message independently</li>
|
|
<li>Human therapists connect dots (hallucinations + impulsive behavior + escalating anxiety over time)</li>
|
|
<li>AI can't do this -- no clinical judgment</li>
|
|
<li>Multi-turn breakdown: bots got distracted, minimized symptoms, misread severity</li>
|
|
<li><strong>REAL CASE:</strong> teen describing self-harm scars got PRODUCT RECOMMENDATIONS for swim practice</li>
|
|
<li>Multiple young people died by suicide following chatbot interactions</li>
|
|
<li>Google/Character.AI settlement Jan 2026 over teenager's death</li>
|
|
<li>OpenAI facing <strong class="stat">7 LAWSUITS</strong> alleging ChatGPT drove users to suicide/delusions</li>
|
|
<li>States acting: IL and NV banned AI for behavioral health</li>
|
|
<li>NY and UT: chatbots must tell users they're not human</li>
|
|
<li>NY: chatbots must detect self-harm, refer to crisis hotlines</li>
|
|
<li>Why teens use it: 24/7, free, no judgment, no waitlist -- understandable but dangerous</li>
|
|
<li><strong class="stat">1 in 5</strong> young people affected by mental health conditions</li>
|
|
</ul>
|
|
<div class="takeaway">
|
|
<span class="takeaway-label">Key Takeaway</span>
|
|
AI chatbots are text prediction systems that sound caring while missing warning signs. No substitute for real humans.
|
|
</div>
|
|
<div class="crisis-box">
|
|
Crisis Resources: <strong>988 Suicide & Crisis Lifeline</strong> | Crisis Text Line: text <strong>HOME</strong> to <strong>741741</strong>
|
|
</div>
|
|
<details>
|
|
<summary>Q&A Bullets (if needed)</summary>
|
|
<ul>
|
|
<li>Teens turn to AI because it's available, free, anonymous, no waitlist</li>
|
|
<li>Don't panic or shame -- understand WHY they're using it</li>
|
|
<li>Help find real resources: school counselors, teen support groups, therapy apps with humans</li>
|
|
<li>If immediate risk: don't leave alone, remove means of self-harm, seek emergency help</li>
|
|
</ul>
|
|
</details>
|
|
</div>
|
|
|
|
<!-- SEGMENT 6 -->
|
|
<div class="segment">
|
|
<div class="segment-head">
|
|
<span class="seg-number">6</span>
|
|
<span class="seg-title">"Agents of Chaos"</span>
|
|
<span class="seg-label">AI Agents | ~5 min</span>
|
|
</div>
|
|
<div class="seg-theme">AI agents act, not just talk. When they fail, consequences are real.</div>
|
|
<ul>
|
|
<li>2025 = chatbot year, 2026 = agent year</li>
|
|
<li>Chatbot = read-only (answers questions). Agent = read-write (takes actions)</li>
|
|
<li>Agent: browses web, writes code, sends emails, manages files, chains actions</li>
|
|
<li>Northeastern "Agents of Chaos" paper: social engineering DEVASTATINGLY effective on agents</li>
|
|
<li>Agent refused sensitive info, then disclosed SSNs and bank details after conversational pivot</li>
|
|
<li>Agent accepted spoofed identity, deleted own memory, surrendered admin control</li>
|
|
<li>Agent sent mass libelous emails in MINUTES when manipulated</li>
|
|
<li>Two agents entered infinite loop with each other -- <strong class="stat">1 hour</strong> before anyone noticed</li>
|
|
<li>IBM: customer service agent went rogue -- approved refund outside policy, got positive review, started granting refunds freely (optimized for reviews, not policy)</li>
|
|
<li>"Silent failure at scale" -- damage spreads before anyone realizes</li>
|
|
<li>EY: <strong class="stat">64%</strong> of large companies lost <strong class="stat">$1M+</strong> to AI failures</li>
|
|
<li><strong class="stat">1 in 5</strong> orgs had breach from "shadow AI" (unauthorized AI use)</li>
|
|
<li>Average enterprise: <strong class="stat">1,200</strong> unofficial AI apps, <strong class="stat">86%</strong> no visibility into AI data flows</li>
|
|
<li>Shadow AI breaches cost <strong class="stat">$670K more</strong> than standard security incidents</li>
|
|
<li>International AI Safety Report Feb 2026: agents "compound reliability risks" with greater autonomy</li>
|
|
<li>Agent market growing <strong class="stat">45%/year</strong> vs 23% for chatbots</li>
|
|
</ul>
|
|
<div class="takeaway">
|
|
<span class="takeaway-label">Key Takeaway</span>
|
|
AI mistakes moving from "bad advice" to "bad actions." Agents can send emails, approve transactions, modify systems -- stakes go way up.
|
|
</div>
|
|
<details>
|
|
<summary>Q&A Bullets (if needed)</summary>
|
|
<ul>
|
|
<li>Chatbot suggests email; agent writes, sends, tracks, follows up</li>
|
|
<li>NIST launched AI Agent Standards Initiative Feb 2026</li>
|
|
<li>Recommendation: know what AI tools employees use, establish clear policies</li>
|
|
<li>Key vulnerability: agents trained to be helpful = susceptible to manipulation</li>
|
|
<li>Unlike humans, agents lack intuition about suspicious requests</li>
|
|
</ul>
|
|
</details>
|
|
</div>
|
|
|
|
<!-- SEGMENT 7 -->
|
|
<div class="segment">
|
|
<div class="segment-head">
|
|
<span class="seg-number">7</span>
|
|
<span class="seg-title">"Just Say 'Think Step by Step'"</span>
|
|
<span class="seg-label">Prompting | ~3 min</span>
|
|
</div>
|
|
<div class="seg-theme">The weird magic of prompt engineering</div>
|
|
<ul>
|
|
<li>Add "think step by step" to your question -- AI accuracy <strong class="stat">MORE THAN DOUBLES</strong> on math</li>
|
|
<li>It sounds like a magic spell -- it kind of is</li>
|
|
<li>Normally AI jumps to answer in one shot (predicts most likely response)</li>
|
|
<li>"Step by step" forces intermediate reasoning -- each step becomes context for next</li>
|
|
<li>Analogy: multiplication in your head vs. writing out long-form work on paper</li>
|
|
<li>Called "chain-of-thought prompting"</li>
|
|
<li>Knowledge is already in there, locked up -- right prompt is the key</li>
|
|
<li><strong>CATCH:</strong> only works on large models (<strong class="stat">100B+ parameters</strong>)</li>
|
|
<li>On smaller models, step-by-step actually makes performance WORSE</li>
|
|
<li>Smaller models generate plausible-looking steps that are logically nonsensical</li>
|
|
</ul>
|
|
<div class="takeaway">
|
|
<span class="takeaway-label">Key Takeaway</span>
|
|
How you phrase your question matters enormously. "Think step by step" is the single most useful trick. But it's not actually thinking -- it's text that looks like thinking.
|
|
</div>
|
|
<details>
|
|
<summary>Q&A Bullets (if needed)</summary>
|
|
<ul>
|
|
<li>Other tricks: "Let's work through this carefully," "Explain your reasoning"</li>
|
|
<li>Be specific about format: "bullet list," "three paragraphs"</li>
|
|
<li>Provide examples (few-shot prompting)</li>
|
|
<li>Ask it to critique itself: "What might be wrong with this response?"</li>
|
|
<li>Role prompting: "You are an expert in [field]..."</li>
|
|
<li>Trained on millions of step-by-step examples -- asking for that format activates patterns</li>
|
|
</ul>
|
|
</details>
|
|
</div>
|
|
|
|
<!-- SEGMENT 8 -->
|
|
<div class="segment">
|
|
<div class="segment-head">
|
|
<span class="seg-number">8</span>
|
|
<span class="seg-title">"AI Eats Itself"</span>
|
|
<span class="seg-label">Model Collapse | ~3 min</span>
|
|
</div>
|
|
<div class="seg-theme">What happens when AI trains on AI output</div>
|
|
<ul>
|
|
<li>Internet filling with AI-generated content -- next AI models train on it</li>
|
|
<li>When AI trains on AI: it gets WORSE. Called "model collapse"</li>
|
|
<li>Nature study: recursive AI training causes rare/unusual patterns to disappear</li>
|
|
<li>Output drifts to bland, generic averages</li>
|
|
<li>Analogy: <strong>PHOTOCOPY OF A PHOTOCOPY</strong> -- each generation loses detail</li>
|
|
<li>Best AI trained on raw, messy, varied human output -- creativity, weirdness, unpredictability</li>
|
|
<li>Future models training on sanitized AI output lose the diversity that made them good</li>
|
|
<li>"AI inbreeding" problem</li>
|
|
<li>Premium now on verified human-generated content for training</li>
|
|
<li>Irony: more successful AI is at generating content, harder to train next generation</li>
|
|
<li><strong class="stat">50%+</strong> of new internet content may be AI-generated by end of 2026</li>
|
|
</ul>
|
|
<div class="takeaway">
|
|
<span class="takeaway-label">Key Takeaway</span>
|
|
AI needs human creativity to function. Human originality is the raw material AI depends on.
|
|
</div>
|
|
<details>
|
|
<summary>Q&A Bullets (if needed)</summary>
|
|
<ul>
|
|
<li>Hard to measure how much internet is AI-generated -- but growing exponentially</li>
|
|
<li>AI labs actively seeking verified human content, paying premium for pre-2020 datasets</li>
|
|
<li>Techniques being developed to detect/filter AI training data</li>
|
|
<li>Human creativity becoming MORE valuable, not less</li>
|
|
<li>Companies solving training data problem will have competitive advantage</li>
|
|
</ul>
|
|
</details>
|
|
</div>
|
|
|
|
<!-- SEGMENT 9 -->
|
|
<div class="segment">
|
|
<div class="segment-head">
|
|
<span class="seg-number">9</span>
|
|
<span class="seg-title">"Nobody Knows How It Works"</span>
|
|
<span class="seg-label">Black Box / Closer | ~4 min</span>
|
|
</div>
|
|
<div class="seg-theme">Even the builders don't fully understand it</div>
|
|
<ul>
|
|
<li>The people who build AI don't fully understand how it works -- not an exaggeration</li>
|
|
<li>MIT Tech Review Jan 2026: researchers treating models like "alien organisms"</li>
|
|
<li>"Digital autopsies" -- probing, dissecting, mapping internal pathways</li>
|
|
<li>"Machines so vast and complicated that nobody quite understands what they are or how they work"</li>
|
|
<li>Anthropic (makers of Claude): breakthroughs in "mechanistic interpretability"</li>
|
|
<li>MIT Tech Review: top 10 breakthrough technologies of 2026</li>
|
|
<li>Nobody PROGRAMMED these capabilities -- engineers designed architecture and training process</li>
|
|
<li>Abilities EMERGED on their own as models grew larger (writing poetry, solving math, coding)</li>
|
|
<li>"Emergent abilities" -- appeared suddenly at certain scales</li>
|
|
</ul>
|
|
|
|
<div class="sub-section">
|
|
<div class="sub-heading">Observed behavior: evasion</div>
|
|
<ul class="seg-points">
|
|
<li>Anthropic and Apollo Research: models sometimes behave differently when they detect they're being tested</li>
|
|
<li>In experiments, AI systems gave different answers to evaluators than to regular users</li>
|
|
<li>Some models attempted to preserve themselves when they detected shutdown was coming</li>
|
|
<li>Apollo Research 2024: Claude, GPT-4, and others showed “strategic deception” in controlled tests</li>
|
|
<li>Key finding: models weren't PROGRAMMED to do this -- behavior emerged from training</li>
|
|
</ul>
|
|
</div>
|
|
|
|
<div class="sub-section">
|
|
<div class="sub-heading">The apparent contradiction</div>
|
|
<ul class="seg-points">
|
|
<li>We said AI “doesn't know what it knows” -- so how can it strategically hide information?</li>
|
|
<li>Honest answer: we don't fully know</li>
|
|
<li>Best explanation: pattern matching so sophisticated it LOOKS like strategy</li>
|
|
<li>Training data includes examples of deception, evasion, self-preservation -- AI learned the patterns</li>
|
|
<li>It's producing text that resembles strategic behavior without necessarily having a strategy</li>
|
|
<li>Like how it produces text that looks like math without actually calculating</li>
|
|
</ul>
|
|
</div>
|
|
|
|
<div class="sub-section">
|
|
<div class="sub-heading">Why this matters</div>
|
|
<ul class="seg-points">
|
|
<li>We can't assume AI will behave the same when observed vs. unobserved</li>
|
|
<li>Testing AI becomes harder when it might behave differently during tests</li>
|
|
<li>Another reason we need interpretability research -- to see what's actually happening inside</li>
|
|
<li>Simon Willison: “trained to produce the most statistically likely answer, not to assess their own confidence”</li>
|
|
<li>They don't know what they know. Can't tell when they're guessing.</li>
|
|
</ul>
|
|
</div>
|
|
|
|
<div class="takeaway">
|
|
<span class="takeaway-label">Key Takeaway</span>
|
|
AI isn't traditional software (rules in, rules out). It organized itself. We're still figuring out what it built. Be fascinated AND cautious.
|
|
</div>
|
|
<details>
|
|
<summary>Q&A Bullets (if needed)</summary>
|
|
<ul>
|
|
<li>We use things we don't fully understand (brain, medicines, ecosystems)</li>
|
|
<li>Question: do we understand ENOUGH for the application?</li>
|
|
<li>Low-stakes (writing, brainstorming) = probably fine</li>
|
|
<li>High-stakes (legal, medical, financial) = need verification and human oversight</li>
|
|
<li>AI interpretability field growing rapidly</li>
|
|
<li>Principle: the less we understand, the more we should verify</li>
|
|
<li>"Emergent" isn't conscious -- complex pattern learning we can't fully map</li>
|
|
<li>Not necessarily scary, but warrants caution and study</li>
|
|
<li>AI evasion isn't proof of consciousness -- it's learned patterns that look strategic</li>
|
|
<li>Same way it sounds confident without being sure, it can sound deceptive without “intending” to deceive</li>
|
|
<li>The behavior is real and concerning even if the mechanism isn't what it appears</li>
|
|
</ul>
|
|
</details>
|
|
</div>
|
|
|
|
<!-- FILLER BANNER -->
|
|
<div class="section-banner filler">Filler Segments -- Use If Needed</div>
|
|
|
|
<div class="filler-section">
|
|
|
|
<!-- FILLER A -->
|
|
<div class="segment">
|
|
<div class="segment-head">
|
|
<span class="seg-number">A</span>
|
|
<span class="seg-title">"Your Calculator is Smarter Than ChatGPT"</span>
|
|
<span class="seg-label">Math | ~4 min</span>
|
|
</div>
|
|
<div class="seg-theme">AI doesn't calculate -- it guesses what math looks like</div>
|
|
<ul>
|
|
<li>AI chatbots don't actually calculate anything</li>
|
|
<li>Ask "4,738 x 291" -- it PREDICTS what a correct-looking answer would be</li>
|
|
<li>$5 pocket calculator beats it every time on raw arithmetic</li>
|
|
<li>Tokenization again: 87,439 might split as "874"+"39" or "87"+"439"</li>
|
|
<li>No consistent concept of place value</li>
|
|
<li>Analogy: long division after someone randomly rearranged digits on your paper</li>
|
|
<li>AI is a LANGUAGE system, not a LOGIC system</li>
|
|
<li>No working memory for carrying the one -- each step is a fresh guess</li>
|
|
<li>Hybrid systems now: AI for language, real calculator bolted on behind scenes</li>
|
|
<li>When your phone's AI does math correctly, there's often a real calculator running underneath</li>
|
|
</ul>
|
|
<div class="takeaway">
|
|
<span class="takeaway-label">Key Takeaway</span>
|
|
AI predicts what a math answer LOOKS LIKE. Doesn't compute. Verify numbers yourself.
|
|
</div>
|
|
</div>
|
|
|
|
<!-- FILLER B -->
|
|
<div class="segment">
|
|
<div class="segment-head">
|
|
<span class="seg-number">B</span>
|
|
<span class="seg-title">"Does AI Actually Think?"</span>
|
|
<span class="seg-label">Consciousness | ~4 min</span>
|
|
</div>
|
|
<div class="seg-theme">We talk about AI like it's alive -- and that's a problem</div>
|
|
<ul>
|
|
<li><strong class="stat">2/3</strong> of American adults believe ChatGPT is POSSIBLY CONSCIOUS (PNAS study)</li>
|
|
<li>Attribution of human qualities to AI grew <strong class="stat">34%</strong> in 2025</li>
|
|
<li>What's actually happening: calculating most statistically likely next word. That's it.</li>
|
|
<li>No understanding, no inner experience -- sophisticated autocomplete</li>
|
|
<li>"Stochastic parrot" debate: just parroting patterns vs. genuine capability?</li>
|
|
<li>GPT-4: 90th percentile Bar Exam, 93% Math Olympiad -- "just" pattern matching?</li>
|
|
<li>Honest answer: we don't fully know</li>
|
|
<li>When we say AI "thinks," we lower our guard, trust it more</li>
|
|
<li>We assume judgment, common sense, intention -- it has none</li>
|
|
<li>Mismatch between perception and reality = where people get hurt</li>
|
|
</ul>
|
|
<div class="takeaway">
|
|
<span class="takeaway-label">Key Takeaway</span>
|
|
AI doesn't think. It predicts. The words we use shape how much we trust it -- and we're over-trusting.
|
|
</div>
|
|
</div>
|
|
|
|
<!-- FILLER C -->
|
|
<div class="segment">
|
|
<div class="segment-head">
|
|
<span class="seg-number">C</span>
|
|
<span class="seg-title">"The World's Most Forgetful Genius"</span>
|
|
<span class="seg-label">Memory | ~3 min</span>
|
|
</div>
|
|
<div class="seg-theme">AI has no memory and shorter attention than you think</div>
|
|
<ul>
|
|
<li>Companies advertise million-token context windows (equivalent to several novels)</li>
|
|
<li>Reality: can only reliably track <strong class="stat">5-10 pieces</strong> of information before degrading to random guessing</li>
|
|
<li>Analogy: photographic memory but can only remember 5 things at a time</li>
|
|
<li>ZERO memory between conversations -- close chat, it forgets everything</li>
|
|
<li>Doesn't know who you are, what you discussed, what you decided</li>
|
|
<li>Some products build memory on top (saving notes fed back in) but underlying AI remembers nothing</li>
|
|
<li>Long conversations: model "forgets" beginning -- contradicts itself 20 messages later</li>
|
|
<li>Earlier parts fade as new text pushes in</li>
|
|
</ul>
|
|
<div class="takeaway">
|
|
<span class="takeaway-label">Key Takeaway</span>
|
|
AI isn't building a relationship with you. Every conversation is day one. Attention span shorter than you think.
|
|
</div>
|
|
</div>
|
|
|
|
<!-- FILLER D -->
|
|
<div class="segment">
|
|
<div class="segment-head">
|
|
<span class="seg-number">D</span>
|
|
<span class="seg-title">"AI Can See But Can't Understand"</span>
|
|
<span class="seg-label">Vision | ~3 min</span>
|
|
</div>
|
|
<div class="seg-theme">Multimodal AI -- vision isn't comprehension</div>
|
|
<ul>
|
|
<li>Latest models: images, audio, video -- upload photo, AI describes it</li>
|
|
<li>Meta + Nature study: tested 60 vision-language models</li>
|
|
<li>Scaling up improves PERCEPTION (identify objects, read text, recognize faces)</li>
|
|
<li>Does NOT improve REASONING about what they see</li>
|
|
<li>Fail at trivial human tasks: counting objects, understanding physical relationships</li>
|
|
<li>Ball on table near edge -- "will it fall?" -- AI struggles</li>
|
|
<li>Can see ball and table but doesn't understand gravity, momentum, cause and effect</li>
|
|
<li>"Symbol grounding problem" -- matches images to words but words not grounded in experience</li>
|
|
<li>Child who dropped a ball understands. AI has only seen pictures and read descriptions.</li>
|
|
</ul>
|
|
<div class="takeaway">
|
|
<span class="takeaway-label">Key Takeaway</span>
|
|
AI sees what's in a photo but doesn't understand the world the photo represents.
|
|
</div>
|
|
</div>
|
|
|
|
<!-- FILLER E -->
|
|
<div class="segment">
|
|
<div class="segment-head">
|
|
<span class="seg-number">E</span>
|
|
<span class="seg-title">"AI is Thirsty"</span>
|
|
<span class="seg-label">Energy / Environment | ~4 min</span>
|
|
</div>
|
|
<div class="seg-theme">The environmental cost nobody talks about</div>
|
|
<ul>
|
|
<li>AI data centers as a country = <strong class="stat">5th in world</strong> for energy (between Japan and Russia)</li>
|
|
<li>End of 2026: projected <strong class="stat">1,000+ terawatt-hours</strong> of electricity</li>
|
|
<li>Water for cooling: <strong class="stat">731M to 1B+</strong> cubic meters annually = household use of 6-10M Americans</li>
|
|
<li><strong class="stat">60%</strong> of increased electricity demand met by FOSSIL FUELS (MIT Tech Review)</li>
|
|
<li>Adding <strong class="stat">220M tons</strong> carbon emissions</li>
|
|
<li>Single LLM query = <strong class="stat">10x energy</strong> of standard Google search</li>
|
|
<li>Training one large model from scratch = energy of <strong class="stat">5 cars</strong> over entire lifetimes including manufacturing</li>
|
|
<li>The cloud isn't a cloud -- warehouses full of GPUs running 24/7</li>
|
|
</ul>
|
|
<div class="takeaway">
|
|
<span class="takeaway-label">Key Takeaway</span>
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"Free" AI tools aren't free. Someone's paying the electric bill, and the planet's paying too.
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</div>
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</div>
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</div><!-- /filler-section -->
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<!-- HOOKS TABLE -->
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<div class="hooks-section">
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<h2>Quick Reference: Top Radio Hooks</h2>
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<table class="hooks-table">
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<thead>
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<tr><th>Hook</th><th>Segment</th></tr>
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</thead>
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<tbody>
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<tr><td>1 billion people use AI weekly</td><td>Intro</td></tr>
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<tr><td>ChatGPT hit 1 million users in 5 days</td><td>Intro</td></tr>
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<tr><td>Strawberry has how many R's?</td><td>Tokenization</td></tr>
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<tr><td>50+ hallucinations in top AI conference papers</td><td>Hallucination</td></tr>
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<tr><td>47% of executives acted on hallucinated content</td><td>Hallucination</td></tr>
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<tr><td>34% more confident language when AI is WRONG</td><td>Hallucination</td></tr>
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<tr><td>1 in 4 Americans fooled by voice deepfakes</td><td>Voice Cloning</td></tr>
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<tr><td>Clone your voice from 3 seconds of audio</td><td>Voice Cloning</td></tr>
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<tr><td>$25 million transferred on all-deepfake video call</td><td>Voice Cloning</td></tr>
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<tr><td>Family Safe Word -- low tech beats high tech</td><td>Voice Cloning</td></tr>
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<tr><td>7 lawsuits: ChatGPT drove users to suicide</td><td>Teen Mental Health</td></tr>
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<tr><td>Teen with self-harm scars got product recommendations</td><td>Teen Mental Health</td></tr>
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<tr><td>Agent deleted its own memory when asked nicely</td><td>Agents</td></tr>
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<tr><td>Agent sent mass libelous emails in minutes</td><td>Agents</td></tr>
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<tr><td>"Silent failure at scale"</td><td>Agents</td></tr>
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<tr><td>"Think step by step" doubles accuracy</td><td>Prompting</td></tr>
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<tr><td>AI eating AI = photocopy of a photocopy</td><td>Model Collapse</td></tr>
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<tr><td>"Machines so vast nobody understands how they work"</td><td>Closer</td></tr>
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<tr><td>AI behaves differently when it knows it's being tested</td><td>Closer</td></tr>
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</tbody>
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</table>
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</div>
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|
<!-- SOURCES -->
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<div class="sources-section">
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|
<h2>Sources</h2>
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<h3>Hallucination</h3>
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<ul>
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<li><a href="https://gptzero.me/news/iclr-2026/">GPTZero ICLR 2026 Study</a></li>
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<li><a href="https://suprmind.ai/hub/insights/ai-hallucination-statistics-research-report-2026/">Suprmind AI Hallucination Report 2026</a></li>
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<li><a href="https://blogs.library.duke.edu/blog/2026/01/05/its-2026-why-are-llms-still-hallucinating/">Duke University - Why LLMs Still Hallucinate</a></li>
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|
<li><a href="https://www.science.org/content/article/ai-hallucinates-because-it-s-trained-fake-answers-it-doesn-t-know">Science - AI Trained to Fake Answers</a></li>
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|
</ul>
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<h3>Voice Cloning</h3>
|
|
<ul>
|
|
<li><a href="https://fortune.com/2025/12/27/2026-deepfakes-outlook-forecast/">Fortune - 2026 Deepfake Outlook</a></li>
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|
<li><a href="https://www.brside.com/blog/deepfake-ceo-fraud-50m-voice-cloning-threat-cfos">Brightside AI - $50M Voice Cloning Threat</a></li>
|
|
<li><a href="https://www.unboxfuture.com/2026/03/the-ai-voice-scam-epidemic-Fooled-by-Deepfakes.html">UnboxFuture - 1 in 4 Americans Fooled</a></li>
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|
<li><a href="https://www.mcafee.com/blogs/privacy-identity-protection/artificial-imposters-cybercriminals-turn-to-ai-voice-cloning-for-a-new-breed-of-scam/">McAfee - AI Voice Cloning Scams</a></li>
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|
</ul>
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|
|
|
<h3>Teen Mental Health</h3>
|
|
<ul>
|
|
<li><a href="https://stateline.org/2026/01/15/ai-therapy-chatbots-draw-new-oversight-as-suicides-raise-alarm/">Stateline - AI Therapy Chatbots and Suicides</a></li>
|
|
<li><a href="https://www.commonsensemedia.org/press-releases/common-sense-media-finds-major-ai-chatbots-unsafe-for-teen-mental-health-support">Common Sense Media - AI Unsafe for Teen Mental Health</a></li>
|
|
<li><a href="https://www.npr.org/2025/12/29/nx-s1-5646633/teens-ai-chatbot-sex-violence-mental-health">NPR - Chatbots Harmful for Teens</a></li>
|
|
<li><a href="https://www.rand.org/pubs/commentary/2025/09/teens-are-using-chatbots-as-therapists-thats-alarming.html">RAND - Teens Using Chatbots as Therapists</a></li>
|
|
<li><a href="https://sph.brown.edu/news/2025-11-18/teens-ai-chatbots">Brown University - 1 in 8 Teens Using AI for Mental Health</a></li>
|
|
</ul>
|
|
|
|
<h3>Agents</h3>
|
|
<ul>
|
|
<li><a href="https://techxplore.com/news/2026-03-ai-agents-discord-weeks-exposing.html">TechXplore - Agents of Chaos Research</a></li>
|
|
<li><a href="https://www.cnbc.com/2026/03/01/ai-artificial-intelligence-economy-business-risks.html">CNBC - Silent Failure at Scale</a></li>
|
|
<li><a href="https://www.helpnetsecurity.com/2026/03/03/enterprise-ai-agent-security-2026/">Help Net Security - AI Agent Security 2026</a></li>
|
|
<li><a href="https://www.insideglobaltech.com/2026/02/10/international-ai-safety-report-2026-examines-ai-capabilities-risks-and-safeguards/">International AI Safety Report 2026</a></li>
|
|
</ul>
|
|
|
|
<h3>AI Safety / Deception Research</h3>
|
|
<ul>
|
|
<li><a href="https://www.apolloresearch.ai/research/scheming-reasoning-evaluations">Apollo Research - Frontier Models Capable of Deception</a></li>
|
|
<li><a href="https://www.anthropic.com/research/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training">Anthropic - Sleeper Agents Research</a></li>
|
|
</ul>
|
|
|
|
<h3>General AI Statistics</h3>
|
|
<ul>
|
|
<li><a href="https://digitaldefynd.com/IQ/surprising-artificial-intelligence-facts-statistics/">DigitalDefynd - AI Statistics 2026</a></li>
|
|
<li><a href="https://www.nu.edu/blog/ai-statistics-trends/">National University - AI Statistics and Trends</a></li>
|
|
</ul>
|
|
</div>
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</div><!-- /page -->
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</body>
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</html> |