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Introducing "Truth Chain." A real-time polling solution to log controversial responses across major LLMs to the blockchain. The objective is continuous accountability and measurement of which LLMs are being tampered with for political purposes. cc Elon Musk

210,970 次观看 • 1 年前 •via X (Twitter)

10 条评论

Renaissance 的头像
Renaissance1 年前

@elonmusk So it’s essentially community notes for LLMs?

Rex St. John 的头像
Rex St. John1 年前

@elonmusk correct

d’admi smalls 的头像
d’admi smalls1 年前

@elonmusk the madman just before Christmas delivered the biggest gift of all

Rex St. John 的头像
Rex St. John1 年前

@alexc_xyz @elonmusk cant sleep clowns will eat me

Sweep 的头像
Sweep1 年前

@elonmusk intresting

Alex 的头像
Alex1 年前

@elonmusk $TRUTH shall prevail

SavageBoy 的头像
SavageBoy1 年前

@elonmusk genius

🧧꧁Zen꧂🧧 的头像
🧧꧁Zen꧂🧧1 年前

@elonmusk @opus_genesis would you wanna collaborate with @rexstjohn alongside @veryvanya on the community note LLMs

Mark 的头像
Mark1 年前

@0xRenaissance @elonmusk Hahaha I know man. Just teasing. Excited to follow what you’re building!

matthew bernal 的头像
matthew bernal1 年前

@elonmusk 2GmUPhpe93kcTJZrC7NJ2keeDZRT5dBveUgegb13pump

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A 4-year-old child has seen 50x more information than the biggest LLMs. Yann LeCun is the Chief AI Scientist at Meta. He recently spoke on “The Expanding Universe of Generative Models” panel at the World Economic Forum in Davos. Yann highlighted the idea that a 4-year-old child is way smarter than current cutting-edge large language models (LLMs). “Think about what a child sees through vision. Put a number on how much information a 4-year-old child has seen during their life. It’s 20 Mbps going through the optical nerve for 16,000 wake hours in the first 4 years of life. 3,600 seconds per hour is 10^15 bytes. This is 50x more information than the biggest LLMs we have. A 4-year-old child is way smarter than these models having acquired an enormous amount of knowledge about how the world works.” The real constraint right now is the ability of LLMs to think. Today, LLMs are only capable of System 1 thinking. System 1 vs System 2 thinking was popularised in the book 'Thinking, Fast and Slow' by Daniel Kahneman. System 1 tasks involve quick, instinctive, automatic responses. LLMs struggle with discontinuous tasks that require a creative leap in progress as they imitate human responses. It's hard to go above human response accuracy if LLMs are only trained on humans. Models are building the track in front of them with each word being generated. What could it mean to give language models System 2 thinking? This remains a future development I'm excited about.

Alex Banks

22,958 次观看 • 2 年前