@khuyendayne81: troi oyyy 🦢#phoidoxinh #xh #grwm

khuyendayne81
khuyendayne81
Open In TikTok:
Region: VN
Saturday 20 June 2026 04:01:07 GMT
24681
190
5
64

Music

Download

Comments

_qwe.zan
qv. :
dây áo có điều chỉnh độ dài được hong ạ
2026-06-23 13:24:19
1
xun.hn142
Xuân Hân 🐰🍒 :
10 điểm kh có nhưng
2026-06-23 18:24:40
0
pnt_hj
Bại đẹp gí :
Ui xinh vá k có giỏ hả shop
2026-06-20 04:12:44
0
To see more videos from user @khuyendayne81, please go to the Tikwm homepage.

Other Videos

Why can an AI write a flawless sonnet about quantum physics, but confidently fail a simple game of Sudoku? 🧩👇
 
 Because we’ve confused *fluency* with *truth*.
 
 Large Language Models (LLMs) are statistical engines. They predict the next most probable word. When they make a tiny logical error in step 3, they don't stop—they confidently build on it until step 10. This is called **Logical Drift**. 
 
 To get absolute mathematical certainty, we have to abandon language entirely and use Automated Reasoning (SAT Solvers). 
 
 🧠 **THE QUICK-WIN MENTAL MODEL:**
 Think of the future of AI as a team of two: The Lawyer and The Auditor.
 • **The LLM (The Lawyer):** Reads messy human language, extracts the rules, and guesses beautifully.
 • **The SAT Solver (The Auditor):** Doesn't speak English. Only accepts pure Boolean math. It mathematically *proves* if those rules conflict.
 Combine them, and you get **Neuro-Symbolic AI**.
 
 If you want to understand the actual computer science behind this—how a SAT solver uses Conflict-Driven Clause Learning (CDCL) to mathematically derive new rules from its own failures—read today’s Substack Deep-Dive. 
 
 🔗 Link in bio for the full mathematical proof, the history of the SAT problem, and the exact LaTeX derivations.
 
 💬 **Question for you:** Have you ever caught an AI in a
Why can an AI write a flawless sonnet about quantum physics, but confidently fail a simple game of Sudoku? 🧩👇 Because we’ve confused *fluency* with *truth*. Large Language Models (LLMs) are statistical engines. They predict the next most probable word. When they make a tiny logical error in step 3, they don't stop—they confidently build on it until step 10. This is called **Logical Drift**. To get absolute mathematical certainty, we have to abandon language entirely and use Automated Reasoning (SAT Solvers). 🧠 **THE QUICK-WIN MENTAL MODEL:** Think of the future of AI as a team of two: The Lawyer and The Auditor. • **The LLM (The Lawyer):** Reads messy human language, extracts the rules, and guesses beautifully. • **The SAT Solver (The Auditor):** Doesn't speak English. Only accepts pure Boolean math. It mathematically *proves* if those rules conflict. Combine them, and you get **Neuro-Symbolic AI**. If you want to understand the actual computer science behind this—how a SAT solver uses Conflict-Driven Clause Learning (CDCL) to mathematically derive new rules from its own failures—read today’s Substack Deep-Dive. 🔗 Link in bio for the full mathematical proof, the history of the SAT problem, and the exact LaTeX derivations. 💬 **Question for you:** Have you ever caught an AI in a "Logical Drift" where it confidently explained something completely wrong? Let me know in the comments! 👇 #artificialintelligence #computerscience #machinelearning #mathematics #logic

About