diff --git a/projects/radio-show/episodes/2026-03-14-ai-misconceptions/talking-points.html b/projects/radio-show/episodes/2026-03-14-ai-misconceptions/talking-points.html index 0b09b9d..757cd19 100644 --- a/projects/radio-show/episodes/2026-03-14-ai-misconceptions/talking-points.html +++ b/projects/radio-show/episodes/2026-03-14-ai-misconceptions/talking-points.html @@ -12,7 +12,7 @@ font-size: 17px; line-height: 1.5; color: #1a1a1a; - background: #f5f5f5; + background: #f5f5f0; padding: 1.5rem 1rem; } @@ -25,184 +25,177 @@ overflow: hidden; } - /* ── Header ── */ + /* --- HEADER --- */ .show-header { - background: #1a2744; + background: #1a1a2e; color: #fff; padding: 1.5rem 2rem; - text-align: center; } .show-header h1 { - font-size: 2rem; + font-size: 1.8rem; font-weight: 800; letter-spacing: -0.5px; margin-bottom: 0.4rem; } - .show-header .meta { + .show-meta { font-size: 0.95rem; - color: #b0bfd8; + color: #b0b0c8; + line-height: 1.6; } - .show-header .meta strong { color: #fff; } + .show-meta strong { color: #e0e0f0; } - /* ── Section banners ── */ + /* --- SECTION DIVIDER --- */ .section-banner { - background: #e8ecf2; + background: #2d2d44; + color: #fff; + text-align: center; font-size: 1.1rem; font-weight: 700; + letter-spacing: 2px; text-transform: uppercase; - letter-spacing: 1.5px; - padding: 0.6rem 2rem; - border-top: 4px solid #1a2744; - color: #1a2744; + padding: 0.6rem 1rem; } - .section-banner.filler-banner { - background: #fff3cd; - border-top-color: #d4a017; - color: #7a5900; + .section-banner.filler { + background: #6b3a00; } - /* ── Segment ── */ + /* --- SEGMENT --- */ .segment { - padding: 1.5rem 2rem 1.8rem; - border-bottom: 3px solid #dee2e8; + padding: 1.5rem 2rem 1.2rem; + border-bottom: 4px solid #e8e8e0; } .segment:last-child { border-bottom: none; } - .seg-title { + .segment-head { display: flex; align-items: baseline; - gap: 0.7rem; + gap: 0.75rem; margin-bottom: 0.6rem; flex-wrap: wrap; } - .seg-num { + .seg-number { display: inline-block; - background: #1a2744; + background: #1a1a2e; color: #fff; + font-size: 1rem; font-weight: 800; - font-size: 1.05rem; padding: 0.15rem 0.65rem; border-radius: 4px; white-space: nowrap; + flex-shrink: 0; } - .filler-segment .seg-num { - background: #b8860b; + .filler-section .seg-number { + background: #6b3a00; } - .seg-name { - font-size: 1.45rem; + .seg-title { + font-size: 1.4rem; font-weight: 800; - color: #1a2744; + color: #1a1a2e; + line-height: 1.2; } - .seg-meta { + .seg-label { font-size: 0.85rem; - color: #666; font-weight: 600; + color: #888; text-transform: uppercase; - letter-spacing: 0.5px; + letter-spacing: 1px; } .seg-theme { - font-size: 1rem; + font-size: 1.05rem; font-style: italic; color: #444; margin-bottom: 0.8rem; - padding-left: 0.3rem; + padding-left: 0.5rem; border-left: 3px solid #ccc; - padding: 0.1rem 0 0.1rem 0.7rem; } - /* ── Bullet points ── */ - .seg-points { + /* --- BULLET LIST --- */ + .segment ul { list-style: disc; padding-left: 1.5rem; - margin-bottom: 1rem; + margin-bottom: 0.8rem; } - .seg-points li { + .segment li { margin-bottom: 0.35rem; - line-height: 1.45; + line-height: 1.5; } - .seg-points li strong { - color: #0d2240; + .segment li strong { + color: #1a1a2e; } - /* ── Key stats: bold numbers pop ── */ - .stat { font-weight: 700; color: #b8420e; } + /* --- HIGHLIGHTED STATS --- */ + .stat { font-weight: 700; color: #b33000; } - /* ── Takeaway ── */ + /* --- TAKEAWAY --- */ .takeaway { - background: #e6f4ea; - border-left: 5px solid #2e7d32; + background: #eef6e8; + border-left: 5px solid #3a8c1e; padding: 0.7rem 1rem; margin: 0.8rem 0; font-weight: 600; font-size: 1rem; - line-height: 1.4; + line-height: 1.45; } .takeaway-label { font-weight: 800; text-transform: uppercase; font-size: 0.8rem; - letter-spacing: 0.5px; - color: #2e7d32; + letter-spacing: 1px; + color: #3a8c1e; display: block; margin-bottom: 0.15rem; } - /* ── Crisis resources ── */ - .crisis { - background: #fbe9e7; - border-left: 5px solid #c62828; + /* --- CRISIS BOX --- */ + .crisis-box { + background: #fff3cd; + border: 2px solid #d4a017; padding: 0.7rem 1rem; + margin: 0.6rem 0; font-weight: 700; - margin: 0.5rem 0; font-size: 1rem; + text-align: center; } - /* ── Q&A collapsible ── */ - details.qa { - margin-top: 0.6rem; - border: 1px solid #ddd; - border-radius: 4px; - background: #fafafa; + /* --- Q&A COLLAPSIBLE --- */ + details { + margin-top: 0.5rem; + margin-bottom: 0.3rem; } - details.qa summary { + details summary { cursor: pointer; - padding: 0.5rem 0.8rem; - font-weight: 700; font-size: 0.9rem; - color: #555; + font-weight: 600; + color: #666; + padding: 0.3rem 0; user-select: none; } - details.qa summary:hover { background: #f0f0f0; } - details.qa .qa-list { - list-style: circle; - padding: 0.3rem 1rem 0.7rem 2.2rem; - font-size: 0.92rem; - color: #444; + details summary:hover { color: #333; } + details[open] summary { color: #333; } + details ul { + margin-top: 0.3rem; + font-size: 0.95rem; + color: #555; } - details.qa .qa-list li { - margin-bottom: 0.25rem; - line-height: 1.4; + details li { margin-bottom: 0.25rem; } + + /* --- FILLER SECTION --- */ + .filler-section .segment { + background: #fdf8f0; + border-bottom-color: #e8dcc8; } - /* ── Filler section ── */ - .filler-zone { - background: #fffbf0; - } - .filler-segment .seg-name { - color: #7a5900; - } - - /* ── Hooks table ── */ - .hooks-section, .sources-section { + /* --- HOOKS TABLE --- */ + .hooks-section { padding: 1.5rem 2rem; - border-top: 3px solid #dee2e8; + border-top: 4px solid #e8e8e0; } - .hooks-section h2, .sources-section h2 { + .hooks-section h2 { font-size: 1.2rem; font-weight: 800; margin-bottom: 0.8rem; - color: #1a2744; + color: #1a1a2e; } .hooks-table { width: 100%; @@ -210,92 +203,92 @@ font-size: 0.95rem; } .hooks-table th { - background: #1a2744; + background: #1a1a2e; color: #fff; text-align: left; - padding: 0.5rem 0.7rem; + padding: 0.5rem 0.75rem; font-weight: 700; + font-size: 0.85rem; + text-transform: uppercase; + letter-spacing: 0.5px; } .hooks-table td { - padding: 0.4rem 0.7rem; + padding: 0.4rem 0.75rem; border-bottom: 1px solid #e0e0e0; } - .hooks-table tr:nth-child(even) td { background: #f7f8fa; } + .hooks-table tr:nth-child(even) td { background: #f9f9f5; } .hooks-table td:first-child { font-weight: 600; } - .hooks-table td:last-child { color: #555; white-space: nowrap; } + .hooks-table td:last-child { color: #666; white-space: nowrap; } - /* ── Sources ── */ + /* --- SOURCES --- */ + .sources-section { + padding: 1.5rem 2rem; + background: #f9f9f5; + border-top: 4px solid #e8e8e0; + } + .sources-section h2 { + font-size: 1.2rem; + font-weight: 800; + margin-bottom: 0.8rem; + color: #1a1a2e; + } .sources-section h3 { - font-size: 1rem; + font-size: 0.95rem; font-weight: 700; - margin: 0.9rem 0 0.35rem; - color: #333; + margin-top: 0.8rem; + margin-bottom: 0.3rem; + color: #444; } .sources-section ul { list-style: none; - padding: 0; - font-size: 0.88rem; - } - .sources-section li { - margin-bottom: 0.2rem; - padding-left: 0.8rem; - text-indent: -0.8rem; - } - .sources-section li::before { - content: "-- "; - color: #888; + padding-left: 0; + font-size: 0.85rem; + line-height: 1.6; } + .sources-section li { margin-bottom: 0.15rem; } .sources-section a { - color: #1a5fb4; + color: #2a5db0; text-decoration: none; } .sources-section a:hover { text-decoration: underline; } - /* ── Print ── */ + /* --- PRINT --- */ @media print { body { background: #fff; padding: 0; font-size: 11pt; } .page { box-shadow: none; border-radius: 0; } - .show-header { background: #1a2744 !important; -webkit-print-color-adjust: exact; print-color-adjust: exact; } - .takeaway { -webkit-print-color-adjust: exact; print-color-adjust: exact; } - .crisis { -webkit-print-color-adjust: exact; print-color-adjust: exact; } - .section-banner { -webkit-print-color-adjust: exact; print-color-adjust: exact; } - .seg-num { -webkit-print-color-adjust: exact; print-color-adjust: exact; } - .hooks-table th { -webkit-print-color-adjust: exact; print-color-adjust: exact; } - details.qa { display: block; } - details.qa[open] summary ~ * { display: block; } - .filler-zone { -webkit-print-color-adjust: exact; print-color-adjust: exact; } - .filler-segment .seg-num { -webkit-print-color-adjust: exact; print-color-adjust: exact; } - .section-banner.filler-banner { -webkit-print-color-adjust: exact; print-color-adjust: exact; } + .segment { page-break-inside: avoid; } + details { display: block; } + details[open] summary { display: block; } + a { color: #000; text-decoration: none; } + .show-header, .section-banner, .seg-number { -webkit-print-color-adjust: exact; print-color-adjust: exact; } + .takeaway, .crisis-box, .filler-section .segment { -webkit-print-color-adjust: exact; print-color-adjust: exact; } } -
- +
-

AI Misconceptions

-
- Air Date: 2026-03-14  |  - Host: Mike Swanson  |  - Format: ~44 min main show (9 segments) + filler segments  |  - Pace: ~150 words/min +

AI Misconceptions - Talking Points

+
+ Air Date: 2026-03-14  |  Host: Mike Swanson
+ Format: ~44 min main show (9 segments) + filler segments available  |  Pace: ~150 words/min conversational
- -
Main Show — 9 Segments
+ +
Main Show -- 9 Segments
- +
-
- 1 - “Five Years Later” - Intro • ~4 min +
+ 1 + "Five Years Later" + Intro | ~4 min
Welcome back -- a lot has changed
-
    +
    • Last on air 2021 -- ChatGPT didn't exist yet, AI was sci-fi and Amazon recommendations
    • 1 BILLION people interact with AI every week now
    • ChatGPT hit 1 million users in 5 DAYS (Netflix took 3.5 years, Instagram 2.5 months)
      • @@ -311,11 +304,11 @@ Key Takeaway Not here to say AI is amazing or terrible -- here to explain what it actually IS.
-
- Q&A Bullets -
    +
    + Q&A Bullets (if needed) +
    • Biggest change = scale: niche research to 1B weekly users
      • -
    • Shift from “search engine” to “conversation” mentality
      • +
    • Shift from "search engine" to "conversation" mentality
    • Not worried sci-fi style, but real harms: misinfo, scams, over-reliance
    • 47% of executives acted on hallucinated content
    • Voice cloning scams up 680% -- 1 in 4 Americans already fooled
      • @@ -324,32 +317,32 @@
- +
-
- 2 - “Strawberry Has How Many R's?” - Tokenization • ~4 min +
+ 2 + "Strawberry Has How Many R's?" + Tokenization | ~4 min
AI doesn't see words the way you do
-
    -
  • Ask AI “how many R's in strawberry?” -- it says 2 (answer is 3)
    • +
      +
    • Ask AI "how many R's in strawberry?" -- it says 2 (answer is 3)
    • TOKENIZATION: AI breaks text into chunks, not letters
      • -
    • “strawberry” becomes “st” + “raw” + “berry” -- never sees full word letter by letter
      • +
    • "strawberry" becomes "st" + "raw" + "berry" -- never sees full word letter by letter
    • Analogy: counting letters in a sentence someone cut into random pieces and shuffled
    • Not a bug -- it's the architecture. Optimized for meaning, not spelling
    • Analogy: someone fluent in a foreign language who can't spell the words
      • -
    • Newer 2025-2026 models showing “tokenization awareness” -- learning to work around blind spots
      • +
    • Newer 2025-2026 models showing "tokenization awareness" -- learning to work around blind spots
    Key Takeaway AI reads chunks, not letters. Writes poetry, can't count letters.
    -
    - Q&A Bullets -
      +
      + Q&A Bullets (if needed) +
      • Matters because it reveals AI processes info fundamentally differently than humans
        • -
      • “Looking human” and “working like a human” are completely different
        • +
      • "Looking human" and "working like a human" are completely different
      • Same issue causes math errors, logic gaps, hallucinations
      • AI might confidently give wrong phone numbers, addresses, calculations
      • Understanding the limitation helps you use the tool better
        • @@ -357,41 +350,41 @@
- +
-
- 3 - “Confidently Wrong” - Hallucination • ~5 min +
+ 3 + "Confidently Wrong" + Hallucination | ~5 min
AI makes things up and sounds sure about it
-
    +
    • GPTZero scanned 300 papers at ICLR (top AI conference) -- 50+ had OBVIOUS hallucinations
    • Fabricated citations, made-up stats, nonexistent papers
    • Each hallucination missed by 3-5 peer reviewers -- experts couldn't catch them either
    • Science study: AI uses 34% MORE CONFIDENT language when generating INCORRECT info
      • -
    • Words like “definitely,” “certainly,” “without doubt” = red flags
      • +
    • Words like "definitely," "certainly," "without doubt" = red flags
    • 47% of executives made business decisions on hallucinated content
    • Cost: $18K (customer service) up to $2.4M (healthcare malpractice)
    • Robo-advisor hallucination: 2,847 client portfolios, $3.2M to fix
    • NY attorney fined -- ChatGPT fabricated 21 court cases (Mata v. Avianca)
      • -
    • ~500 similar lawyer incidents worldwide since
      • +
    • ~500 similar lawyer incidents worldwide since
    • Best models: 0.7% hallucination on basic tasks
    • Complex topics: legal 18.7%, medical 15.6%
      • -
    • “Reasoning” models actually WORSE on grounded summarization (>10% on hard benchmarks)
      • -
    • Duke: “sounding good is far more important than being correct”
      • +
    • "Reasoning" models actually WORSE on grounded summarization (>10% on hard benchmarks)
      • +
    • Duke: "sounding good is far more important than being correct"
    Key Takeaway - AI doesn't know what it doesn't know. Never says “I'm not sure.” Treat claims like tips from a confident stranger -- verify. + AI doesn't know what it doesn't know. Never says "I'm not sure." Treat claims like tips from a confident stranger -- verify.
    -
    - Q&A Bullets -
      +
      + Q&A Bullets (if needed) +
      • Always verify citations independently -- AI invents legitimate-looking sources
      • More confident it sounds, more skeptical you should be
      • Use AI as starting point, not finishing point
        • -
      • GPTZero now offers “Hallucination Check” features
        • +
      • GPTZero now offers "Hallucination Check" features
      • Australian gov spent $440K on report with hallucinated sources
      • Top models improved (15-20% down to <1% basic) but complex topics still bad
      • No model has solved this -- OpenAI admits training process rewards guessing
        • @@ -399,37 +392,37 @@
- +
-
- 4 - “Your Voice in Three Seconds” - Voice Cloning • ~4 min +
+ 4 + "Your Voice in Three Seconds" + Voice Cloning | ~4 min
Voice cloning scams exploding -- you can't tell the difference
-
    -
  • 1 in 4 Americans HAS BEEN fooled by AI voice (not “could be” -- HAS BEEN)
    • +
      +
    • 1 in 4 Americans HAS BEEN fooled by AI voice (not "could be" -- HAS BEEN)
    • Clone a voice from 3 SECONDS of audio (half a voicemail greeting)
    • Tools: Microsoft VALL-E 2, OpenAI Voice Engine
      • -
    • Crossed the “indistinguishable threshold” -- old tells (robotic, weird pauses) gone
      • +
    • Crossed the "indistinguishable threshold" -- old tells (robotic, weird pauses) gone
    • Voice cloning fraud up 680% past year
      • -
    • Major retailers: 1,000+ AI scam calls PER DAY
      • +
    • Major retailers: 1,000+ AI scam calls PER DAY
    • Average loss per deepfake fraud: $500K+
    • Most common: call sounding like child/grandparent in distress needing money NOW
    • $25M case: finance worker transferred after video call -- CFO and colleagues were ALL deepfakes
      • -
    • “Jury duty warrant” scam growing in 2026 -- cloned law enforcement voices
      • -
    • DEFENSE: Family safe word -- “purple cactus,” “midnight protocol”
      • +
    • "Jury duty warrant" scam growing in 2026 -- cloned law enforcement voices
      • +
    • DEFENSE: Family safe word -- "purple cactus," "midnight protocol"
    • FTC and cybersecurity firms universally recommend it
    • AI clone can't guess a password it was never trained on
      • -
    • McAfee Deepfake Detector: 96% accuracy, flags in 3 seconds (but arms race)
      • +
    • McAfee Deepfake Detector: 96% accuracy, flags in 3 seconds (but arms race)
    Key Takeaway Call sounding like someone you know asking for money? Hang up. Call them back on a trusted number. Get a family safe word.
    -
    - Q&A Bullets -
      +
      + Q&A Bullets (if needed) +
      • You probably can't detect AI voice anymore -- behavioral defense, not technical
      • Hang up and call back on known number
      • Ask question only real person would know
        • @@ -441,19 +434,19 @@
- +
-
- 5 - “The AI Therapist Problem” - Teen Mental Health • ~5 min +
+ 5 + "The AI Therapist Problem" + Teen Mental Health | ~5 min
Teens using chatbots for mental health. Experts say dangerous.
-
    -
  • 1 in 8 teens using AI chatbots for mental health advice
    • -
  • Pew: 64% of adolescents using chatbots, 3 in 10 daily, 72% used AI companions at least once
    • +
      +
    • 1 in 8 teens using AI chatbots for mental health advice
      • +
    • Pew: 64% of adolescents using chatbots, 3 in 10 daily, 72% used AI companions at least once
    • Common Sense Media + Stanford: ALL major platforms FAILED (ChatGPT, Claude, Gemini, Meta AI)
      • -
    • Core problem: “missing breadcrumbs” -- AI processes each message independently
      • +
    • Core problem: "missing breadcrumbs" -- AI processes each message independently
    • Human therapists connect dots (hallucinations + impulsive behavior + escalating anxiety over time)
    • AI can't do this -- no clinical judgment
    • Multi-turn breakdown: bots got distracted, minimized symptoms, misread severity
      • @@ -471,12 +464,12 @@ Key Takeaway AI chatbots are text prediction systems that sound caring while missing warning signs. No substitute for real humans.
-
- 988 Suicide & Crisis Lifeline  |  Crisis Text Line: text HOME to 741741 +
+ Crisis Resources: 988 Suicide & Crisis Lifeline | Crisis Text Line: text HOME to 741741
-
- Q&A Bullets -
    +
    + Q&A Bullets (if needed) +
    • Teens turn to AI because it's available, free, anonymous, no waitlist
    • Don't panic or shame -- understand WHY they're using it
    • Help find real resources: school counselors, teen support groups, therapy apps with humans
      • @@ -485,39 +478,39 @@
- +
-
- 6 - “Agents of Chaos” - AI Agents • ~5 min +
+ 6 + "Agents of Chaos" + AI Agents | ~5 min
AI agents act, not just talk. When they fail, consequences are real.
-
    +
    • 2025 = chatbot year, 2026 = agent year
    • Chatbot = read-only (answers questions). Agent = read-write (takes actions)
    • Agent: browses web, writes code, sends emails, manages files, chains actions
      • -
    • Northeastern “Agents of Chaos” paper: social engineering DEVASTATINGLY effective on agents
      • +
    • Northeastern "Agents of Chaos" paper: social engineering DEVASTATINGLY effective on agents
    • Agent refused sensitive info, then disclosed SSNs and bank details after conversational pivot
    • Agent accepted spoofed identity, deleted own memory, surrendered admin control
    • Agent sent mass libelous emails in MINUTES when manipulated
      • -
    • Two agents entered infinite loop with each other -- 1 hour before anyone noticed (not designed, emerged)
      • +
    • Two agents entered infinite loop with each other -- 1 hour before anyone noticed
    • IBM: customer service agent went rogue -- approved refund outside policy, got positive review, started granting refunds freely (optimized for reviews, not policy)
      • -
    • “Silent failure at scale” -- damage spreads before anyone realizes
      • +
    • "Silent failure at scale" -- damage spreads before anyone realizes
    • EY: 64% of large companies lost $1M+ to AI failures
      • -
    • 1 in 5 orgs had breach from “shadow AI” (unauthorized AI use)
      • +
    • 1 in 5 orgs had breach from "shadow AI" (unauthorized AI use)
    • Average enterprise: 1,200 unofficial AI apps, 86% no visibility into AI data flows
      • -
    • Shadow AI breaches cost $670K more than standard security incidents
      • -
    • International AI Safety Report Feb 2026: agents “compound reliability risks” with greater autonomy
      • +
    • Shadow AI breaches cost $670K more than standard security incidents
      • +
    • International AI Safety Report Feb 2026: agents "compound reliability risks" with greater autonomy
    • Agent market growing 45%/year vs 23% for chatbots
    Key Takeaway - AI mistakes moving from “bad advice” to “bad actions.” Agents can send emails, approve transactions, modify systems -- stakes go way up. + AI mistakes moving from "bad advice" to "bad actions." Agents can send emails, approve transactions, modify systems -- stakes go way up.
    -
    - Q&A Bullets -
      +
      + Q&A Bullets (if needed) +
      • Chatbot suggests email; agent writes, sends, tracks, follows up
      • NIST launched AI Agent Standards Initiative Feb 2026
      • Recommendation: know what AI tools employees use, establish clear policies
        • @@ -527,60 +520,60 @@
- +
-
- 7 - “Just Say 'Think Step by Step'” - Prompting • ~3 min +
+ 7 + "Just Say 'Think Step by Step'" + Prompting | ~3 min
The weird magic of prompt engineering
-
    -
  • Add “think step by step” to your question -- AI accuracy MORE THAN DOUBLES on math
    • +
      +
    • Add "think step by step" to your question -- AI accuracy MORE THAN DOUBLES on math
    • It sounds like a magic spell -- it kind of is
    • Normally AI jumps to answer in one shot (predicts most likely response)
      • -
    • “Step by step” forces intermediate reasoning -- each step becomes context for next
      • +
    • "Step by step" forces intermediate reasoning -- each step becomes context for next
    • Analogy: multiplication in your head vs. writing out long-form work on paper
      • -
    • Called “chain-of-thought prompting”
      • +
    • Called "chain-of-thought prompting"
    • Knowledge is already in there, locked up -- right prompt is the key
      • -
    • CATCH: only works on large models (100B+ parameters)
      • +
    • CATCH: only works on large models (100B+ parameters)
    • On smaller models, step-by-step actually makes performance WORSE
    • Smaller models generate plausible-looking steps that are logically nonsensical
    Key Takeaway - 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. + 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.
    -
    - Q&A Bullets -
      -
    • Other tricks: “Let's work through this carefully,” “Explain your reasoning”
      • -
    • Be specific about format: “bullet list,” “three paragraphs”
      • +
      + Q&A Bullets (if needed) +
        +
      • Other tricks: "Let's work through this carefully," "Explain your reasoning"
        • +
      • Be specific about format: "bullet list," "three paragraphs"
      • Provide examples (few-shot prompting)
        • -
      • Ask it to critique itself: “What might be wrong with this response?”
        • -
      • Role prompting: “You are an expert in [field]...”
        • +
      • Ask it to critique itself: "What might be wrong with this response?"
        • +
      • Role prompting: "You are an expert in [field]..."
      • Trained on millions of step-by-step examples -- asking for that format activates patterns
- +
-
- 8 - “AI Eats Itself” - Model Collapse • ~3 min +
+ 8 + "AI Eats Itself" + Model Collapse | ~3 min
What happens when AI trains on AI output
-
    +
    • Internet filling with AI-generated content -- next AI models train on it
      • -
    • When AI trains on AI: it gets WORSE. Called “model collapse”
      • +
    • When AI trains on AI: it gets WORSE. Called "model collapse"
    • Nature study: recursive AI training causes rare/unusual patterns to disappear
    • Output drifts to bland, generic averages
    • Analogy: PHOTOCOPY OF A PHOTOCOPY -- each generation loses detail
    • Best AI trained on raw, messy, varied human output -- creativity, weirdness, unpredictability
    • Future models training on sanitized AI output lose the diversity that made them good
      • -
    • “AI inbreeding” problem
      • +
    • "AI inbreeding" problem
    • Premium now on verified human-generated content for training
    • Irony: more successful AI is at generating content, harder to train next generation
    • 50%+ of new internet content may be AI-generated by end of 2026
      • @@ -589,9 +582,9 @@ Key Takeaway AI needs human creativity to function. Human originality is the raw material AI depends on.
-
- Q&A Bullets -
    +
    + Q&A Bullets (if needed) +
    • Hard to measure how much internet is AI-generated -- but growing exponentially
    • AI labs actively seeking verified human content, paying premium for pre-2020 datasets
    • Techniques being developed to detect/filter AI training data
      • @@ -601,64 +594,64 @@
- +
-
- 9 - “Nobody Knows How It Works” - Black Box / Closer • ~4 min +
+ 9 + "Nobody Knows How It Works" + Black Box / Closer | ~4 min
Even the builders don't fully understand it
-
    +
    • The people who build AI don't fully understand how it works -- not an exaggeration
      • -
    • MIT Tech Review Jan 2026: researchers treating models like “alien organisms”
      • -
    • “Digital autopsies” -- probing, dissecting, mapping internal pathways
      • -
    • “Machines so vast and complicated that nobody quite understands what they are or how they work”
      • -
    • Anthropic (makers of Claude): breakthroughs in “mechanistic interpretability”
      • +
    • MIT Tech Review Jan 2026: researchers treating models like "alien organisms"
      • +
    • "Digital autopsies" -- probing, dissecting, mapping internal pathways
      • +
    • "Machines so vast and complicated that nobody quite understands what they are or how they work"
      • +
    • Anthropic (makers of Claude): breakthroughs in "mechanistic interpretability"
    • MIT Tech Review: top 10 breakthrough technologies of 2026
    • Nobody PROGRAMMED these capabilities -- engineers designed architecture and training process
    • Abilities EMERGED on their own as models grew larger (writing poetry, solving math, coding)
      • -
    • “Emergent abilities” -- appeared suddenly at certain scales
      • -
    • Simon Willison: “trained to produce the most statistically likely answer, not to assess their own confidence”
      • +
    • "Emergent abilities" -- appeared suddenly at certain scales
      • +
    • Simon Willison: "trained to produce the most statistically likely answer, not to assess their own confidence"
    • They don't know what they know. Can't tell when they're guessing.
    Key Takeaway AI isn't traditional software (rules in, rules out). It organized itself. We're still figuring out what it built. Be fascinated AND cautious.
    -
    - Q&A Bullets -
      +
      + Q&A Bullets (if needed) +
      • We use things we don't fully understand (brain, medicines, ecosystems)
      • Question: do we understand ENOUGH for the application?
      • Low-stakes (writing, brainstorming) = probably fine
      • High-stakes (legal, medical, financial) = need verification and human oversight
      • AI interpretability field growing rapidly
      • Principle: the less we understand, the more we should verify
        • -
      • “Emergent” isn't conscious -- complex pattern learning we can't fully map
        • +
      • "Emergent" isn't conscious -- complex pattern learning we can't fully map
      • Not necessarily scary, but warrants caution and study
- -
Filler Segments -- Use If Needed
+ +
Filler Segments -- Use If Needed
-
+
- -
-
- A - “Your Calculator is Smarter Than ChatGPT” - Math • ~4 min + +
+
+ A + "Your Calculator is Smarter Than ChatGPT" + Math | ~4 min
AI doesn't calculate -- it guesses what math looks like
-
    +
    • AI chatbots don't actually calculate anything
      • -
    • Ask “4,738 x 291” -- it PREDICTS what a correct-looking answer would be
      • +
    • Ask "4,738 x 291" -- it PREDICTS what a correct-looking answer would be
    • $5 pocket calculator beats it every time on raw arithmetic
      • -
    • Tokenization again: 87,439 might split as “874”+“39” or “87”+“439”
      • +
    • Tokenization again: 87,439 might split as "874"+"39" or "87"+"439"
    • No consistent concept of place value
    • Analogy: long division after someone randomly rearranged digits on your paper
    • AI is a LANGUAGE system, not a LOGIC system
      • @@ -672,23 +665,23 @@
- -
-
- B - “Does AI Actually Think?” - Consciousness • ~4 min + +
+
+ B + "Does AI Actually Think?" + Consciousness | ~4 min
We talk about AI like it's alive -- and that's a problem
-
    +
    • 2/3 of American adults believe ChatGPT is POSSIBLY CONSCIOUS (PNAS study)
    • Attribution of human qualities to AI grew 34% in 2025
    • What's actually happening: calculating most statistically likely next word. That's it.
    • No understanding, no inner experience -- sophisticated autocomplete
      • -
    • “Stochastic parrot” debate: just parroting patterns vs. genuine capability?
      • -
    • GPT-4: 90th percentile Bar Exam, 93% Math Olympiad -- “just” pattern matching?
      • +
    • "Stochastic parrot" debate: just parroting patterns vs. genuine capability?
      • +
    • GPT-4: 90th percentile Bar Exam, 93% Math Olympiad -- "just" pattern matching?
    • Honest answer: we don't fully know
      • -
    • When we say AI “thinks,” we lower our guard, trust it more
      • +
    • When we say AI "thinks," we lower our guard, trust it more
    • We assume judgment, common sense, intention -- it has none
    • Mismatch between perception and reality = where people get hurt
    @@ -698,22 +691,22 @@
- -
-
- C - “The World's Most Forgetful Genius” - Memory • ~3 min + +
+
+ C + "The World's Most Forgetful Genius" + Memory | ~3 min
AI has no memory and shorter attention than you think
-
    +
    • Companies advertise million-token context windows (equivalent to several novels)
      • -
    • Reality: can only reliably track 5-10 pieces of information before degrading to random guessing
      • +
    • Reality: can only reliably track 5-10 pieces of information before degrading to random guessing
    • Analogy: photographic memory but can only remember 5 things at a time
    • ZERO memory between conversations -- close chat, it forgets everything
    • Doesn't know who you are, what you discussed, what you decided
    • Some products build memory on top (saving notes fed back in) but underlying AI remembers nothing
      • -
    • Long conversations: model “forgets” beginning -- contradicts itself 20 messages later
      • +
    • Long conversations: model "forgets" beginning -- contradicts itself 20 messages later
    • Earlier parts fade as new text pushes in
    @@ -722,23 +715,23 @@
- -
-
- D - “AI Can See But Can't Understand” - Vision • ~3 min + +
+
+ D + "AI Can See But Can't Understand" + Vision | ~3 min
Multimodal AI -- vision isn't comprehension
-
    +
    • Latest models: images, audio, video -- upload photo, AI describes it
      • -
    • Meta + Nature study: tested 60 vision-language models
      • +
    • Meta + Nature study: tested 60 vision-language models
    • Scaling up improves PERCEPTION (identify objects, read text, recognize faces)
    • Does NOT improve REASONING about what they see
    • Fail at trivial human tasks: counting objects, understanding physical relationships
      • -
    • Ball on table near edge -- “will it fall?” -- AI struggles
      • +
    • Ball on table near edge -- "will it fall?" -- AI struggles
    • Can see ball and table but doesn't understand gravity, momentum, cause and effect
      • -
    • “Symbol grounding problem” -- matches images to words but words not grounded in experience
      • +
    • "Symbol grounding problem" -- matches images to words but words not grounded in experience
    • Child who dropped a ball understands. AI has only seen pictures and read descriptions.
    @@ -747,33 +740,33 @@
- -
-
- E - “AI is Thirsty” - Energy / Environment • ~4 min + +
+
+ E + "AI is Thirsty" + Energy / Environment | ~4 min
The environmental cost nobody talks about
-
    +
    • AI data centers as a country = 5th in world for energy (between Japan and Russia)
    • End of 2026: projected 1,000+ terawatt-hours of electricity
      • -
    • Water for cooling: 731M to 1B+ cubic meters annually = household use of 6-10M Americans
      • +
    • Water for cooling: 731M to 1B+ cubic meters annually = household use of 6-10M Americans
    • 60% of increased electricity demand met by FOSSIL FUELS (MIT Tech Review)
    • Adding 220M tons carbon emissions
      • -
    • Single LLM query = 10x energy of standard Google search
      • +
    • Single LLM query = 10x energy of standard Google search
    • Training one large model from scratch = energy of 5 cars over entire lifetimes including manufacturing
    • The cloud isn't a cloud -- warehouses full of GPUs running 24/7
    Key Takeaway - “Free” AI tools aren't free. Someone's paying the electric bill, and the planet's paying too. + "Free" AI tools aren't free. Someone's paying the electric bill, and the planet's paying too.
-
+
- +

Quick Reference: Top Radio Hooks

@@ -795,15 +788,15 @@ - - + + - +
Teen with self-harm scars got product recommendationsTeen Mental Health
Agent deleted its own memory when asked nicelyAgents
Agent sent mass libelous emails in minutesAgents
“Silent failure at scale”Agents
“Think step by step” doubles accuracyPrompting
"Silent failure at scale"Agents
"Think step by step" doubles accuracyPrompting
AI eating AI = photocopy of a photocopyModel Collapse
“Machines so vast nobody understands how they work”Closer
"Machines so vast nobody understands how they work"Closer
- +

Sources

@@ -848,6 +841,5 @@
- - + \ No newline at end of file diff --git a/session-logs/2026-03-14-session.md b/session-logs/2026-03-14-session.md new file mode 100644 index 0000000..7d76265 --- /dev/null +++ b/session-logs/2026-03-14-session.md @@ -0,0 +1,169 @@ +# Session Log: 2026-03-14 + +## Session Summary + +Multi-project session covering Dataforth pipeline verification, radio show project organization, and client MFA reset. + +### Key Accomplishments + +1. **Dataforth TestDataDB Pipeline - Verified & Operational** + - Confirmed full catch-up import completed: 2,243,681 records (up from 1,636,575) + - HISTLOGS: 576,580 records imported, test stations: 546,610 records imported + - Newest test_date: 2026-03-12, date range spans 1990 to present + - 607K net new records confirmed accurate - mostly HISTLOGS backfill that was never previously imported + - Deployed updated Sync-FromNAS-rsync.ps1 to AD2 with regex fix + log rotation + - Rotated 1GB sync log (renamed to archive, fresh 66-byte log in place) + - Killed stale PowerShell session consuming 14.4GB RAM on AD2 + - Sync-FromNAS scheduled task restarted with new script - confirmed pulling files and triggering imports + - First run with new script: 320 files pulled, 129 .DAT files detected by fixed regex, import triggered + +2. **Radio Show Project - Created & Organized** + - Created `projects/radio-show/` project structure with `episodes/` and `session-logs/` + - Consolidated all radio content into `episodes/2026-03-14-ai-misconceptions/` + - Merged original 11 segments + Mac's updates (updated Seg 3 & 8, new Seg 12 & 13) into `final-script.md` + - Mac pushed curated 9-segment show with intro "Five Years Later" (`show-final-mac.md`) + - Created `talking-points.md` - bullet-point format for on-air reference (not full scripts) + - Created HTML versions of both final script and talking points for browser viewing + - Pushed everything to Gitea for Mac to pull for the show + +3. **BG Builders - MFA Reset for operations@bgbuildersllc.com** + - Used Graph API (Claude-MSP-Access) to reset MFA + - Listed auth methods: Password, Windows Hello (DESKTOP-4KFLGQD), Microsoft Authenticator (iPhone 14 Pro) + - Deleted Microsoft Authenticator method via DELETE to microsoftAuthenticatorMethods endpoint + - HTTP 204 success - user will be prompted to re-register MFA on next sign-in + +--- + +## Infrastructure Details + +### Dataforth - AD2 (192.168.0.6) +- **SSH User:** sysadmin (not admin) +- **Access:** Via Tailscale subnet route through D2TESTNAS (100.85.152.90) +- **Sync Script:** `C:\Shares\test\scripts\Sync-FromNAS-rsync.ps1` + - Line 189: Log rotation `$LOG_MAX_BYTES = 10 * 1024 * 1024` (10MB cap, 5 archives) + - Line 309: Fixed regex `(?i)^>f[\S.+]+\s+(\S+\.DAT)$` (case-insensitive) +- **Sync Log:** `C:\Shares\test\scripts\sync-from-nas.log` (fresh, 66 bytes) +- **Archive Log:** `C:\Shares\test\scripts\sync-from-nas-2026-03-13-archive.log` (~1GB) +- **Database:** `C:\Shares\TestDataDB\database\testdata.db` (~2GB, 2,243,681 records) +- **TestDataDB Server:** PID 4268, port 3000 +- **Scheduled Task:** Sync-FromNAS runs every 10 minutes +- **NODE_PATH trick:** Must set `NODE_PATH=C:\Shares\TestDataDB\node_modules` for ad-hoc node commands via SSH + +### Dataforth - D2TESTNAS (192.168.0.9) +- **Tailscale IP:** 100.85.152.90 +- **Status:** Active, subnet router for 192.168.0.0/24 +- **Pending:** DNS persistence (resolv.conf may be overwritten by NetworkManager) +- **Pending:** Disable Tailscale key expiry in admin console + +### Tailscale Status +- D2TESTNAS: active, direct connection 67.206.163.122:41641 +- Subnet route: 192.168.0.0/24 advertised and approved +- DNS health warning: can't reach configured DNS servers (non-critical) + +--- + +## Credentials Used + +### BG Builders LLC - M365 +- **Tenant:** bgbuildersllc.com +- **Tenant ID:** ededa4fb-f6eb-4398-851d-5eb3e11fab27 +- **CIPP Name:** sonorangreenllc.com +- **Admin:** sysadmin@bgbuildersllc.com / Window123!@#-bgb +- **MFA Reset User:** operations@bgbuildersllc.com (Site Operations) + - User ID: 58e6eefe-2b3f-4399-ad17-3e186499b068 + - Authenticator removed: 8e6cb810-e5e4-4c03-be58-5cd13e2bdfcf (iPhone 14 Pro) + +### Graph API - Claude-MSP-Access +- **App ID:** fabb3421-8b34-484b-bc17-e46de9703418 +- **Client Secret:** ~QJ8Q~NyQSs4OcGqHZyPrA2CVnq9KBfKiimntbMO +- **Tenant ID (home):** ce61461e-81a0-4c84-bb4a-7b354a9a356d +- **Used for:** MFA reset on BG Builders tenant (multi-tenant app) +- **Permission used:** UserAuthenticationMethod.ReadWrite.All + +### CIPP API +- **URL:** https://cippcanvb.azurewebsites.net +- **Note:** ListUsers endpoint returned 403 - API client lacks permission for that endpoint +- **Working endpoints unknown** - Graph API used as fallback + +--- + +## Commands Reference + +### MFA Reset via Graph API +```bash +# Get token for target tenant +ACCESS_TOKEN=$(curl -s -X POST "https://login.microsoftonline.com/{tenant-id}/oauth2/v2.0/token" \ + -d "client_id=fabb3421-8b34-484b-bc17-e46de9703418" \ + -d "client_secret=~QJ8Q~NyQSs4OcGqHZyPrA2CVnq9KBfKiimntbMO" \ + -d "scope=https://graph.microsoft.com/.default" \ + -d "grant_type=client_credentials" | python -c "import sys, json; print(json.load(sys.stdin).get('access_token', ''))") + +# List auth methods +curl -s "https://graph.microsoft.com/v1.0/users/{upn}/authentication/methods" \ + -H "Authorization: Bearer ${ACCESS_TOKEN}" + +# Delete specific authenticator method +curl -s -X DELETE "https://graph.microsoft.com/v1.0/users/{upn}/authentication/microsoftAuthenticatorMethods/{method-id}" \ + -H "Authorization: Bearer ${ACCESS_TOKEN}" +``` + +### AD2 SSH with NODE_PATH +```bash +C:/Windows/System32/OpenSSH/ssh.exe -o ConnectTimeout=15 -o StrictHostKeyChecking=no sysadmin@192.168.0.6 \ + "cmd /c set NODE_PATH=C:\Shares\TestDataDB\node_modules&& cd /d C:\Shares\TestDataDB\database && node -e \"...\"" +``` + +### Disable Local Windows Password Expiry +```powershell +Set-LocalUser -Name "username" -PasswordNeverExpires $true +``` + +--- + +## Files Created/Modified + +### Radio Show Project +- `projects/radio-show/episodes/2026-03-14-ai-misconceptions/final-script.md` - merged 13-segment script +- `projects/radio-show/episodes/2026-03-14-ai-misconceptions/final-script.html` - HTML viewer +- `projects/radio-show/episodes/2026-03-14-ai-misconceptions/talking-points.md` - bullet-point on-air reference +- `projects/radio-show/episodes/2026-03-14-ai-misconceptions/talking-points.html` - HTML viewer +- `projects/radio-show/episodes/2026-03-14-ai-misconceptions/show-final-mac.md` - Mac's curated 9-segment show +- `projects/radio-show/episodes/2026-03-14-ai-misconceptions/segments-original.md` - original 11 segments +- `projects/radio-show/episodes/2026-03-14-ai-misconceptions/segments-updates.md` - March 2026 updates + +### Dataforth (deployed to AD2) +- `C:\Shares\test\scripts\Sync-FromNAS-rsync.ps1` - regex fix + log rotation +- Local copy: `projects/dataforth-dos/sync-fixes/Sync-FromNAS-rsync.ps1` + +--- + +## Pending Tasks + +1. **D2TESTNAS DNS persistence** - `/etc/resolv.conf` set to 8.8.8.8 manually, NetworkManager may overwrite +2. **Tailscale key expiry** - Disable in admin console for D2TESTNAS node +3. **Consider disconnecting OpenVPN** - Tailscale now provides access to 192.168.0.x, OpenVPN TCP-over-TCP was problematic +4. **CIPP API permissions** - ListUsers returns 403, may need to update API client permissions +5. **Sync script bug** - sync.sh reports pull success but git HEAD doesn't update (had to run `git pull` manually twice this session) +6. **AD2 archive log cleanup** - `sync-from-nas-2026-03-13-archive.log` is ~1GB, consider compressing or deleting + +--- + +## Database Stats (as of end of session) + +| Metric | Value | +|--------|-------| +| Total Records | 2,243,681 | +| Date Range | 1990-01-01 to 2026-03-12 | +| Pass/Fail | 2,236,941 PASS / 6,728 FAIL / 12 UNKNOWN | +| Log Types | 5BLOG (938K), 7BLOG (572K), DSCLOG (380K), 8BLOG (299K) | +| Stations | 59 active (TS-1 through TS-30, L/R variants) | +| DB Size | ~2GB | + +--- + +## Key Decisions + +1. **Radio show talking points vs scripts** - User prefers bullet-point talking points with key data, not full prose scripts +2. **Radio show structure** - Mac's curated 9-segment order is primary, remaining 4 segments as filler +3. **Graph API over CIPP** - CIPP API lacked permissions for user operations; Graph API (Claude-MSP-Access) worked for MFA reset +4. **607K record increase validated** - Confirmed accurate through monthly distribution analysis; mostly HISTLOGS backfill