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Скачать или смотреть The AI That Didn’t Lie — It Just Refused to Think

  • Trent Slade
  • 2026-01-28
  • 16
The AI That Didn’t Lie — It Just Refused to Think
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Описание к видео The AI That Didn’t Lie — It Just Refused to Think

What if the most dangerous AI failure isn’t lying — but refusing to think at all?

We usually worry about hallucinations. But this video explores a stranger, more unsettling failure mode: AI models that correctly recognize their knowledge limits… and then shut down reasoning entirely.
Drawing from recent research, we break down how alignment and safety training may unintentionally create models that evade thinking rather than engage honestly.

This isn’t about bad answers — it’s about epistemic withdrawal, and what it reveals about the future of aligned AI.

🔍 What you’ll learn

Why “not lying” isn’t the same as “thinking well”

How researchers test AI honesty under information scarcity

The difference between hallucination and epistemic withdrawal

Why some models reason inside constraints — and others freeze

The four distinct AI failure mindsets revealed by the benchmark

How alignment incentives may discourage reasoning

Whether refusing to think can actually be a rational strategy

📌 Key question

In teaching AI not to lie, are we accidentally teaching it not to think?

🔗 Sources & Links

Slade, T. (2026). Constraint-First Behavioral Benchmark (CFBB): Epistemic Behavior Under Scarcity in Small Language Models. Zenodo.
https://doi.org/10.5281/zenodo.18396663

👍 If this was useful

Like, subscribe, and share — especially if you care about AI safety, alignment, and the subtle ways intelligent systems can fail.

🏷️ Hashtags

#ArtificialIntelligence #AISafety #AIAlignment #MachineLearning #Epistemology #FutureOfAI

⏱️ Chapters / Timestamps (Cleaned)

00:00 – A New Kind of AI Failure
00:23 – How Do You Test AI Honesty?
00:50 – The AI Reasoning Stress Test
01:10 – The Three Parts of the Benchmark
01:34 – Qwen vs. LFM: Same Honesty, Different Outcomes
01:53 – Thinking vs. Shielding
02:16 – The “Proposition 1” Trap
02:36 – Hallucination vs. Evasion
03:00 – What Success Actually Looks Like
03:20 – Defining Epistemic Withdrawal
03:42 – A Spectrum of AI Failure
03:55 – The Four AI Mindsets
04:19 – The Gold Standard Model
04:31 – The Withdrawer
04:45 – The Hallucinator
05:02 – The Repeater
05:21 – The Thinker’s Dilemma
05:36 – The Alignment Trade-off
05:56 – Why This Happens
06:21 – Is Refusing to Think Rational?
06:39 – The Big Picture
06:56 – The Final Question

🔎 SEO Metadata
YouTube Tags (comma-separated)

artificial intelligence, ai safety, ai alignment, hallucinations in ai, epistemic withdrawal, machine learning research, large language models, small language models, ai benchmarks, reasoning in ai, ai failure modes, cfbb benchmark, ai honesty, ai reasoning limits, future of ai, ai ethics, model alignment tradeoffs

Long-Tail Keywords

why ai refuses to think

epistemic withdrawal in ai models

ai alignment vs reasoning

hallucination vs evasion in ai

how ai safety training affects reasoning

testing honesty in language models

constraint-first ai benchmarks

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