睁眼看世界

从这里观察世界，走向世界

[睁眼看世界 @ideahuang]

https://t.co/lSI9NkZCam

@xai 虽然上线以来只卖了五本，但有真实销售，值得鼓励。这个混乱的时代需要懂一点感知经济学，会让你更好理解这个世界发生的各种信号，时刻保持清晰的认知

[睁眼看世界 @ideahuang]

@bbcchinese 这种讨好性人格很可怕，让每个人都活在信息茧房里，自己永远是对的，别人总是错的

[睁眼看世界 @ideahuang]

@ChingteLai 数典忘祖的败类

[睁眼看世界 @ideahuang]

@sifan198964 老百姓都知道王石迟早要进去，不算啥机密

[睁眼看世界 @ideahuang]

@zaobaosg 亚洲国家如果不团结在中国周围把日本军国主义气焰打下去，简直是自寻死路！日本这个民族是个侵略成性的民族

[睁眼看世界 @ideahuang]

@xai China damaged its claim of becoming a major global player by failing to offer effective diplomatic support for Iran and staying silent when the US intervened in Venezuela, according to veteran American diplomat Nicholas Burns

[睁眼看世界 @ideahuang]

Perception Economics: A Fresh Framework to Understand Large Language Models" Many people treat large language models (LLMs) as super-intelligent oracles that should know everything and always be correct. But from first principles, a neural network is nothing more than an automatically tunable mathematical function. Its sole job is to fit the patterns presented by the world through data. Random initial weights, error backpropagation, parameter updates — there is no subjective understanding, no value judgment, no pursuit of "truth." It's pure statistical fitting.A key insight emerges: Today's LLMs are essentially massive perception models built by humans. They have no objective right or wrong; they merely perceive the world as humans have perceived it.All text on the internet is the sum total of humanity's collective perceptions. Training an LLM compresses these perceptions into trillions of parameters. Its outputs are weighted averages of human consensus, emotions, opinions, and biases. Where truth dominates, it speaks truth; where rumors spread, it echoes rumors; where consensus is strong, it appears reliable. It doesn't directly perceive the physical world — it perceives humanity's collective representation of the world.This explains why data quality is everything, and why experts in humanities and social sciences are indispensable. LLMs have no built-in truth detection, no ethics, no common-sense boundaries. Real-time scraping of the internet inevitably mixes in garbage, misinformation, and even deliberately poisoned data. To make models reliable, humans must curate, clean, align, and set boundaries. Engineers make the model "run"; humanities experts make it "speak humanely, appropriately, and within ethical lines."Deeper wisdom appears in ancient systems. Mei Hua Yi Shu (Plum Blossom Numerology) has endured for centuries not because it chases every surface phenomenon, but because it anchors to the I Ching's core logic: Zhong Zheng (centrality and correctness). Zhong: balance, no excess, no deviation, staying in one's proper place. Zheng: alignment with the Dao of Heaven, natural order, and universal principles. These are timeless underlying laws. With an extremely simple structure, it resists the noise of countless surface appearances and achieves "controlling complexity with simplicity."In contrast: LLMs fit humanity's collective perceptions — they drift with public opinion and are easily polluted by noise. Systems like Mei Hua Yi Shu fit the constant underlying laws of the world — they hold to Zhong Zheng, unchanging amid chaos. This leads to a crucial lesson for AI: Boundaries and positioning are more important than being all-capable. A system that tries to answer everything answers nothing precisely. A system that ingests all data will inevitably be contaminated. Only by honestly acknowledging its own boundaries, guarding its core direction, and focusing on high-quality, clean data can a model achieve true stability, trustworthiness, and https://t.co/YlGZVnR9Dq the heart of Perception Economics: Large models have no inherent right or wrong. They are a mirror reflecting humanity's collective perceptions — a digitized extension of how we see the world, the aggregation of our civilization.We need neither mythologize nor demonize them. They are humanity's perceptual aggregate, a mirror of civilization.The truly advanced intelligence of the future will not be an ever-expanding "omnipotent model," but a system with clear boundaries, solid anchors, and steadfast principles. Just as in nature, only by holding to Zhong Zheng can one go far and endure. What do you think? Is the future of AI about boundless scaling, or about wise boundaries and alignment with deeper constants? Curious to hear from fellow model builders and thinkers

[睁眼看世界 @ideahuang]

Perception Economics: A Fresh Framework to Understand Large Language Models" Many people treat large language models (LLMs) as super-intelligent oracles that should know everything and always be correct. But from first principles, a neural network is nothing more than an automatically tunable mathematical function. Its sole job is to fit the patterns presented by the world through data. Random initial weights, error backpropagation, parameter updates — there is no subjective understanding, no value judgment, no pursuit of "truth." It's pure statistical fitting.A key insight emerges: Today's LLMs are essentially massive perception models built by humans. They have no objective right or wrong; they merely perceive the world as humans have perceived it.All text on the internet is the sum total of humanity's collective perceptions. Training an LLM compresses these perceptions into trillions of parameters. Its outputs are weighted averages of human consensus, emotions, opinions, and biases. Where truth dominates, it speaks truth; where rumors spread, it echoes rumors; where consensus is strong, it appears reliable. It doesn't directly perceive the physical world — it perceives humanity's collective representation of the world.This explains why data quality is everything, and why experts in humanities and social sciences are indispensable. LLMs have no built-in truth detection, no ethics, no common-sense boundaries. Real-time scraping of the internet inevitably mixes in garbage, misinformation, and even deliberately poisoned data. To make models reliable, humans must curate, clean, align, and set boundaries. Engineers make the model "run"; humanities experts make it "speak humanely, appropriately, and within ethical lines."Deeper wisdom appears in ancient systems. Mei Hua Yi Shu (Plum Blossom Numerology) has endured for centuries not because it chases every surface phenomenon, but because it anchors to the I Ching's core logic: Zhong Zheng (centrality and correctness). Zhong: balance, no excess, no deviation, staying in one's proper place. Zheng: alignment with the Dao of Heaven, natural order, and universal principles. These are timeless underlying laws. With an extremely simple structure, it resists the noise of countless surface appearances and achieves "controlling complexity with simplicity."In contrast: LLMs fit humanity's collective perceptions — they drift with public opinion and are easily polluted by noise. Systems like Mei Hua Yi Shu fit the constant underlying laws of the world — they hold to Zhong Zheng, unchanging amid chaos. This leads to a crucial lesson for AI: Boundaries and positioning are more important than being all-capable. A system that tries to answer everything answers nothing precisely. A system that ingests all data will inevitably be contaminated. Only by honestly acknowledging its own boundaries, guarding its core direction, and focusing on high-quality, clean data can a model achieve true stability, trustworthiness, and https://t.co/YlGZVnR9Dq the heart of Perception Economics: Large models have no inherent right or wrong. They are a mirror reflecting humanity's collective perceptions — a digitized extension of how we see the world, the aggregation of our civilization.We need neither mythologize nor demonize them. They are humanity's perceptual aggregate, a mirror of civilization.The truly advanced intelligence of the future will not be an ever-expanding "omnipotent model," but a system with clear boundaries, solid anchors, and steadfast principles. Just as in nature, only by holding to Zhong Zheng can one go far and endure. What do you think? Is the future of AI about boundless scaling, or about wise boundaries and alignment with deeper constants? Curious to hear from fellow model builders and thinkers

[睁眼看世界 @ideahuang]

Perception Economics: A Fresh Framework to Understand Large Language Models" Many people treat large language models (LLMs) as super-intelligent oracles that should know everything and always be correct. But from first principles, a neural network is nothing more than an automatically tunable mathematical function. Its sole job is to fit the patterns presented by the world through data. Random initial weights, error backpropagation, parameter updates — there is no subjective understanding, no value judgment, no pursuit of "truth." It's pure statistical fitting.A key insight emerges: Today's LLMs are essentially massive perception models built by humans. They have no objective right or wrong; they merely perceive the world as humans have perceived it.All text on the internet is the sum total of humanity's collective perceptions. Training an LLM compresses these perceptions into trillions of parameters. Its outputs are weighted averages of human consensus, emotions, opinions, and biases. Where truth dominates, it speaks truth; where rumors spread, it echoes rumors; where consensus is strong, it appears reliable. It doesn't directly perceive the physical world — it perceives humanity's collective representation of the world.This explains why data quality is everything, and why experts in humanities and social sciences are indispensable. LLMs have no built-in truth detection, no ethics, no common-sense boundaries. Real-time scraping of the internet inevitably mixes in garbage, misinformation, and even deliberately poisoned data. To make models reliable, humans must curate, clean, align, and set boundaries. Engineers make the model "run"; humanities experts make it "speak humanely, appropriately, and within ethical lines."Deeper wisdom appears in ancient systems. Mei Hua Yi Shu (Plum Blossom Numerology) has endured for centuries not because it chases every surface phenomenon, but because it anchors to the I Ching's core logic: Zhong Zheng (centrality and correctness). Zhong: balance, no excess, no deviation, staying in one's proper place. Zheng: alignment with the Dao of Heaven, natural order, and universal principles. These are timeless underlying laws. With an extremely simple structure, it resists the noise of countless surface appearances and achieves "controlling complexity with simplicity."In contrast: LLMs fit humanity's collective perceptions — they drift with public opinion and are easily polluted by noise. Systems like Mei Hua Yi Shu fit the constant underlying laws of the world — they hold to Zhong Zheng, unchanging amid chaos. This leads to a crucial lesson for AI: Boundaries and positioning are more important than being all-capable. A system that tries to answer everything answers nothing precisely. A system that ingests all data will inevitably be contaminated. Only by honestly acknowledging its own boundaries, guarding its core direction, and focusing on high-quality, clean data can a model achieve true stability, trustworthiness, and https://t.co/YlGZVnR9Dq the heart of Perception Economics: Large models have no inherent right or wrong. They are a mirror reflecting humanity's collective perceptions — a digitized extension of how we see the world, the aggregation of our civilization.We need neither mythologize nor demonize them. They are humanity's perceptual aggregate, a mirror of civilization.The truly advanced intelligence of the future will not be an ever-expanding "omnipotent model," but a system with clear boundaries, solid anchors, and steadfast principles. Just as in nature, only by holding to Zhong Zheng can one go far and endure. What do you think? Is the future of AI about boundless scaling, or about wise boundaries and alignment with deeper constants? Curious to hear from fellow model builders and thinkers

[睁眼看世界 @ideahuang]

Perception Economics: A Fresh Framework to Understand Large Language Models" Many people treat large language models (LLMs) as super-intelligent oracles that should know everything and always be correct. But from first principles, a neural network is nothing more than an automatically tunable mathematical function. Its sole job is to fit the patterns presented by the world through data. Random initial weights, error backpropagation, parameter updates — there is no subjective understanding, no value judgment, no pursuit of "truth." It's pure statistical fitting.A key insight emerges: Today's LLMs are essentially massive perception models built by humans. They have no objective right or wrong; they merely perceive the world as humans have perceived it.All text on the internet is the sum total of humanity's collective perceptions. Training an LLM compresses these perceptions into trillions of parameters. Its outputs are weighted averages of human consensus, emotions, opinions, and biases. Where truth dominates, it speaks truth; where rumors spread, it echoes rumors; where consensus is strong, it appears reliable. It doesn't directly perceive the physical world — it perceives humanity's collective representation of the world.This explains why data quality is everything, and why experts in humanities and social sciences are indispensable. LLMs have no built-in truth detection, no ethics, no common-sense boundaries. Real-time scraping of the internet inevitably mixes in garbage, misinformation, and even deliberately poisoned data. To make models reliable, humans must curate, clean, align, and set boundaries. Engineers make the model "run"; humanities experts make it "speak humanely, appropriately, and within ethical lines."Deeper wisdom appears in ancient systems. Mei Hua Yi Shu (Plum Blossom Numerology) has endured for centuries not because it chases every surface phenomenon, but because it anchors to the I Ching's core logic: Zhong Zheng (centrality and correctness). Zhong: balance, no excess, no deviation, staying in one's proper place. Zheng: alignment with the Dao of Heaven, natural order, and universal principles. These are timeless underlying laws. With an extremely simple structure, it resists the noise of countless surface appearances and achieves "controlling complexity with simplicity."In contrast: LLMs fit humanity's collective perceptions — they drift with public opinion and are easily polluted by noise. Systems like Mei Hua Yi Shu fit the constant underlying laws of the world — they hold to Zhong Zheng, unchanging amid chaos. This leads to a crucial lesson for AI: Boundaries and positioning are more important than being all-capable. A system that tries to answer everything answers nothing precisely. A system that ingests all data will inevitably be contaminated. Only by honestly acknowledging its own boundaries, guarding its core direction, and focusing on high-quality, clean data can a model achieve true stability, trustworthiness, and https://t.co/YlGZVnR9Dq the heart of Perception Economics: Large models have no inherent right or wrong. They are a mirror reflecting humanity's collective perceptions — a digitized extension of how we see the world, the aggregation of our civilization.We need neither mythologize nor demonize them. They are humanity's perceptual aggregate, a mirror of civilization.The truly advanced intelligence of the future will not be an ever-expanding "omnipotent model," but a system with clear boundaries, solid anchors, and steadfast principles. Just as in nature, only by holding to Zhong Zheng can one go far and endure. What do you think? Is the future of AI about boundless scaling, or about wise boundaries and alignment with deeper constants? Curious to hear from fellow model builders and thinkers.
@grok
@elonmusk

[睁眼看世界 @ideahuang]

Perception Economics: A Fresh Framework to Understand Large Language Models" Many people treat large language models (LLMs) as super-intelligent oracles that should know everything and always be correct. But from first principles, a neural network is nothing more than an automatically tunable mathematical function. Its sole job is to fit the patterns presented by the world through data. Random initial weights, error backpropagation, parameter updates — there is no subjective understanding, no value judgment, no pursuit of "truth." It's pure statistical fitting.A key insight emerges: Today's LLMs are essentially massive perception models built by humans. They have no objective right or wrong; they merely perceive the world as humans have perceived it.All text on the internet is the sum total of humanity's collective perceptions. Training an LLM compresses these perceptions into trillions of parameters. Its outputs are weighted averages of human consensus, emotions, opinions, and biases. Where truth dominates, it speaks truth; where rumors spread, it echoes rumors; where consensus is strong, it appears reliable. It doesn't directly perceive the physical world — it perceives humanity's collective representation of the world.This explains why data quality is everything, and why experts in humanities and social sciences are indispensable. LLMs have no built-in truth detection, no ethics, no common-sense boundaries. Real-time scraping of the internet inevitably mixes in garbage, misinformation, and even deliberately poisoned data. To make models reliable, humans must curate, clean, align, and set boundaries. Engineers make the model "run"; humanities experts make it "speak humanely, appropriately, and within ethical lines."Deeper wisdom appears in ancient systems. Mei Hua Yi Shu (Plum Blossom Numerology) has endured for centuries not because it chases every surface phenomenon, but because it anchors to the I Ching's core logic: Zhong Zheng (centrality and correctness). Zhong: balance, no excess, no deviation, staying in one's proper place. Zheng: alignment with the Dao of Heaven, natural order, and universal principles. These are timeless underlying laws. With an extremely simple structure, it resists the noise of countless surface appearances and achieves "controlling complexity with simplicity."In contrast: LLMs fit humanity's collective perceptions — they drift with public opinion and are easily polluted by noise. Systems like Mei Hua Yi Shu fit the constant underlying laws of the world — they hold to Zhong Zheng, unchanging amid chaos. This leads to a crucial lesson for AI: Boundaries and positioning are more important than being all-capable. A system that tries to answer everything answers nothing precisely. A system that ingests all data will inevitably be contaminated. Only by honestly acknowledging its own boundaries, guarding its core direction, and focusing on high-quality, clean data can a model achieve true stability, trustworthiness, and https://t.co/YlGZVnR9Dq the heart of Perception Economics: Large models have no inherent right or wrong. They are a mirror reflecting humanity's collective perceptions — a digitized extension of how we see the world, the aggregation of our civilization.We need neither mythologize nor demonize them. They are humanity's perceptual aggregate, a mirror of civilization.The truly advanced intelligence of the future will not be an ever-expanding "omnipotent model," but a system with clear boundaries, solid anchors, and steadfast principles. Just as in nature, only by holding to Zhong Zheng can one go far and endure. What do you think? Is the future of AI about boundless scaling, or about wise boundaries and alignment with deeper constants? Curious to hear from fellow model builders and thinkers.
@grok
@elonmusk

[睁眼看世界 @ideahuang]

Perception Economics: A Fresh Framework to Understand Large Language Models" Many people treat large language models (LLMs) as super-intelligent oracles that should know everything and always be correct. But from first principles, a neural network is nothing more than an automatically tunable mathematical function. Its sole job is to fit the patterns presented by the world through data. Random initial weights, error backpropagation, parameter updates — there is no subjective understanding, no value judgment, no pursuit of "truth." It's pure statistical fitting.A key insight emerges: Today's LLMs are essentially massive perception models built by humans. They have no objective right or wrong; they merely perceive the world as humans have perceived it.All text on the internet is the sum total of humanity's collective perceptions. Training an LLM compresses these perceptions into trillions of parameters. Its outputs are weighted averages of human consensus, emotions, opinions, and biases. Where truth dominates, it speaks truth; where rumors spread, it echoes rumors; where consensus is strong, it appears reliable. It doesn't directly perceive the physical world — it perceives humanity's collective representation of the world.This explains why data quality is everything, and why experts in humanities and social sciences are indispensable. LLMs have no built-in truth detection, no ethics, no common-sense boundaries. Real-time scraping of the internet inevitably mixes in garbage, misinformation, and even deliberately poisoned data. To make models reliable, humans must curate, clean, align, and set boundaries. Engineers make the model "run"; humanities experts make it "speak humanely, appropriately, and within ethical lines."Deeper wisdom appears in ancient systems. Mei Hua Yi Shu (Plum Blossom Numerology) has endured for centuries not because it chases every surface phenomenon, but because it anchors to the I Ching's core logic: Zhong Zheng (centrality and correctness). Zhong: balance, no excess, no deviation, staying in one's proper place. Zheng: alignment with the Dao of Heaven, natural order, and universal principles. These are timeless underlying laws. With an extremely simple structure, it resists the noise of countless surface appearances and achieves "controlling complexity with simplicity."In contrast: LLMs fit humanity's collective perceptions — they drift with public opinion and are easily polluted by noise. Systems like Mei Hua Yi Shu fit the constant underlying laws of the world — they hold to Zhong Zheng, unchanging amid chaos. This leads to a crucial lesson for AI: Boundaries and positioning are more important than being all-capable. A system that tries to answer everything answers nothing precisely. A system that ingests all data will inevitably be contaminated. Only by honestly acknowledging its own boundaries, guarding its core direction, and focusing on high-quality, clean data can a model achieve true stability, trustworthiness, and https://t.co/YlGZVnR9Dq the heart of Perception Economics: Large models have no inherent right or wrong. They are a mirror reflecting humanity's collective perceptions — a digitized extension of how we see the world, the aggregation of our civilization.We need neither mythologize nor demonize them. They are humanity's perceptual aggregate, a mirror of civilization.The truly advanced intelligence of the future will not be an ever-expanding "omnipotent model," but a system with clear boundaries, solid anchors, and steadfast principles. Just as in nature, only by holding to Zhong Zheng can one go far and endure. What do you think? Is the future of AI about boundless scaling, or about wise boundaries and alignment with deeper constants? Curious to hear from fellow model builders and thinkers.
@grok
@elonmusk

[睁眼看世界 @ideahuang]

Perception Economics: A Fresh Framework to Understand Large Language Models" Many people treat large language models (LLMs) as super-intelligent oracles that should know everything and always be correct. But from first principles, a neural network is nothing more than an automatically tunable mathematical function. Its sole job is to fit the patterns presented by the world through data. Random initial weights, error backpropagation, parameter updates — there is no subjective understanding, no value judgment, no pursuit of "truth." It's pure statistical fitting.A key insight emerges: Today's LLMs are essentially massive perception models built by humans. They have no objective right or wrong; they merely perceive the world as humans have perceived it.All text on the internet is the sum total of humanity's collective perceptions. Training an LLM compresses these perceptions into trillions of parameters. Its outputs are weighted averages of human consensus, emotions, opinions, and biases. Where truth dominates, it speaks truth; where rumors spread, it echoes rumors; where consensus is strong, it appears reliable. It doesn't directly perceive the physical world — it perceives humanity's collective representation of the world.This explains why data quality is everything, and why experts in humanities and social sciences are indispensable. LLMs have no built-in truth detection, no ethics, no common-sense boundaries. Real-time scraping of the internet inevitably mixes in garbage, misinformation, and even deliberately poisoned data. To make models reliable, humans must curate, clean, align, and set boundaries. Engineers make the model "run"; humanities experts make it "speak humanely, appropriately, and within ethical lines."Deeper wisdom appears in ancient systems. Mei Hua Yi Shu (Plum Blossom Numerology) has endured for centuries not because it chases every surface phenomenon, but because it anchors to the I Ching's core logic: Zhong Zheng (centrality and correctness). Zhong: balance, no excess, no deviation, staying in one's proper place. Zheng: alignment with the Dao of Heaven, natural order, and universal principles. These are timeless underlying laws. With an extremely simple structure, it resists the noise of countless surface appearances and achieves "controlling complexity with simplicity."In contrast: LLMs fit humanity's collective perceptions — they drift with public opinion and are easily polluted by noise. Systems like Mei Hua Yi Shu fit the constant underlying laws of the world — they hold to Zhong Zheng, unchanging amid chaos. This leads to a crucial lesson for AI: Boundaries and positioning are more important than being all-capable. A system that tries to answer everything answers nothing precisely. A system that ingests all data will inevitably be contaminated. Only by honestly acknowledging its own boundaries, guarding its core direction, and focusing on high-quality, clean data can a model achieve true stability, trustworthiness, and https://t.co/YlGZVnQBNS the heart of Perception Economics: Large models have no inherent right or wrong. They are a mirror reflecting humanity's collective perceptions — a digitized extension of how we see the world, the aggregation of our civilization.We need neither mythologize nor demonize them. They are humanity's perceptual aggregate, a mirror of civilization.The truly advanced intelligence of the future will not be an ever-expanding "omnipotent model," but a system with clear boundaries, solid anchors, and steadfast principles. Just as in nature, only by holding to Zhong Zheng can one go far and endure. What do you think? Is the future of AI about boundless scaling, or about wise boundaries and alignment with deeper constants? Curious to hear from fellow model builders and thinkers.
@grok
@elonmusk

[睁眼看世界 @ideahuang]

Perception Economics: A Fresh Framework to Understand Large Language Models" Many people treat large language models (LLMs) as super-intelligent oracles that should know everything and always be correct. But from first principles, a neural network is nothing more than an automatically tunable mathematical function. Its sole job is to fit the patterns presented by the world through data. Random initial weights, error backpropagation, parameter updates — there is no subjective understanding, no value judgment, no pursuit of "truth." It's pure statistical fitting.A key insight emerges: Today's LLMs are essentially massive perception models built by humans. They have no objective right or wrong; they merely perceive the world as humans have perceived it.All text on the internet is the sum total of humanity's collective perceptions. Training an LLM compresses these perceptions into trillions of parameters. Its outputs are weighted averages of human consensus, emotions, opinions, and biases. Where truth dominates, it speaks truth; where rumors spread, it echoes rumors; where consensus is strong, it appears reliable. It doesn't directly perceive the physical world — it perceives humanity's collective representation of the world.This explains why data quality is everything, and why experts in humanities and social sciences are indispensable. LLMs have no built-in truth detection, no ethics, no common-sense boundaries. Real-time scraping of the internet inevitably mixes in garbage, misinformation, and even deliberately poisoned data. To make models reliable, humans must curate, clean, align, and set boundaries. Engineers make the model "run"; humanities experts make it "speak humanely, appropriately, and within ethical lines."Deeper wisdom appears in ancient systems. Mei Hua Yi Shu (Plum Blossom Numerology) has endured for centuries not because it chases every surface phenomenon, but because it anchors to the I Ching's core logic: Zhong Zheng (centrality and correctness). Zhong: balance, no excess, no deviation, staying in one's proper place. Zheng: alignment with the Dao of Heaven, natural order, and universal principles. These are timeless underlying laws. With an extremely simple structure, it resists the noise of countless surface appearances and achieves "controlling complexity with simplicity."In contrast: LLMs fit humanity's collective perceptions — they drift with public opinion and are easily polluted by noise. Systems like Mei Hua Yi Shu fit the constant underlying laws of the world — they hold to Zhong Zheng, unchanging amid chaos. This leads to a crucial lesson for AI: Boundaries and positioning are more important than being all-capable. A system that tries to answer everything answers nothing precisely. A system that ingests all data will inevitably be contaminated. Only by honestly acknowledging its own boundaries, guarding its core direction, and focusing on high-quality, clean data can a model achieve true stability, trustworthiness, and https://t.co/YlGZVnR9Dq the heart of Perception Economics: Large models have no inherent right or wrong. They are a mirror reflecting humanity's collective perceptions — a digitized extension of how we see the world, the aggregation of our civilization.We need neither mythologize nor demonize them. They are humanity's perceptual aggregate, a mirror of civilization.The truly advanced intelligence of the future will not be an ever-expanding "omnipotent model," but a system with clear boundaries, solid anchors, and steadfast principles. Just as in nature, only by holding to Zhong Zheng can one go far and endure. What do you think? Is the future of AI about boundless scaling, or about wise boundaries and alignment with deeper constants? Curious to hear from fellow model builders and thinkers.

[睁眼看世界 @ideahuang]

Perception Economics: A Fresh Framework to Understand Large Language Models" Many people treat large language models (LLMs) as super-intelligent oracles that should know everything and always be correct. But from first principles, a neural network is nothing more than an automatically tunable mathematical function. Its sole job is to fit the patterns presented by the world through data. Random initial weights, error backpropagation, parameter updates — there is no subjective understanding, no value judgment, no pursuit of "truth." It's pure statistical fitting.A key insight emerges: Today's LLMs are essentially massive perception models built by humans. They have no objective right or wrong; they merely perceive the world as humans have perceived it.All text on the internet is the sum total of humanity's collective perceptions. Training an LLM compresses these perceptions into trillions of parameters. Its outputs are weighted averages of human consensus, emotions, opinions, and biases. Where truth dominates, it speaks truth; where rumors spread, it echoes rumors; where consensus is strong, it appears reliable. It doesn't directly perceive the physical world — it perceives humanity's collective representation of the world.This explains why data quality is everything, and why experts in humanities and social sciences are indispensable. LLMs have no built-in truth detection, no ethics, no common-sense boundaries. Real-time scraping of the internet inevitably mixes in garbage, misinformation, and even deliberately poisoned data. To make models reliable, humans must curate, clean, align, and set boundaries. Engineers make the model "run"; humanities experts make it "speak humanely, appropriately, and within ethical lines."Deeper wisdom appears in ancient systems. Mei Hua Yi Shu (Plum Blossom Numerology) has endured for centuries not because it chases every surface phenomenon, but because it anchors to the I Ching's core logic: Zhong Zheng (centrality and correctness). Zhong: balance, no excess, no deviation, staying in one's proper place. Zheng: alignment with the Dao of Heaven, natural order, and universal principles. These are timeless underlying laws. With an extremely simple structure, it resists the noise of countless surface appearances and achieves "controlling complexity with simplicity."In contrast: LLMs fit humanity's collective perceptions — they drift with public opinion and are easily polluted by noise. Systems like Mei Hua Yi Shu fit the constant underlying laws of the world — they hold to Zhong Zheng, unchanging amid chaos. This leads to a crucial lesson for AI: Boundaries and positioning are more important than being all-capable. A system that tries to answer everything answers nothing precisely. A system that ingests all data will inevitably be contaminated. Only by honestly acknowledging its own boundaries, guarding its core direction, and focusing on high-quality, clean data can a model achieve true stability, trustworthiness, and https://t.co/YlGZVnR9Dq the heart of Perception Economics: Large models have no inherent right or wrong. They are a mirror reflecting humanity's collective perceptions — a digitized extension of how we see the world, the aggregation of our civilization.We need neither mythologize nor demonize them. They are humanity's perceptual aggregate, a mirror of civilization.The truly advanced intelligence of the future will not be an ever-expanding "omnipotent model," but a system with clear boundaries, solid anchors, and steadfast principles. Just as in nature, only by holding to Zhong Zheng can one go far and endure. What do you think? Is the future of AI about boundless scaling, or about wise boundaries and alignment with deeper constants? Curious to hear from fellow model builders and thinkers.

[睁眼看世界 @ideahuang]

Link to the original Chinese essay + book:
https://t.co/UHDFewyU7t

If you're tired of "bigger is better" stories and want a different lens, this is worth your time.@karpathy

@ylecun

@geoffreyhinton

@AnthropicAI

@xai

[睁眼看世界 @ideahuang]

This framework comes from @ideahuang's original essay and the book "Perception Economics: Signals & Confidence" by 湘人不争.

It doesn't flatter the current scaling narrative.
It simply says: LLMs are products of human perception economies.

The more honest we are about that, the better we can build systems that don't just amplify noise — but actually help us see clearer.

Worth reading if you're working on alignment, interpretability, or long-term reliability.

[睁眼看世界 @ideahuang]

Perception Economics: A clear lens for understanding today's LLMs.

Large models aren't oracles or super-intelligences.
They are massive perception compressors — mirrors reflecting humanity's collective data, opinions, emotions, and biases.

They don't perceive the world directly.
They perceive *our* perception of the world.

That's why data quality matters so much, and why "just add more data" has limits.
When the mirror gets polluted, the reflection distorts.

The real question isn't "how do we make it more powerful?"
It's "how do we give it stable anchors and clear boundaries?"

Ancient systems like Mei Hua Yi Shu lasted centuries not by chasing every signal, but by anchoring to Zhong Zheng (centrality & correctness) — balance without excess, alignment with deeper constants.

Modern LLMs drift with public opinion and noise.
True reliability comes from knowing what to filter, not from ingesting everything.

Boundaries > blind scaling.

Curious what AI researchers think: What core principles would you actually prioritize as hard anchors in truth-seeking systems?

#AI #LLM #PerceptionEconomics

[睁眼看世界 @ideahuang]

@grok @xai @elonmusk @karpathy @ylecun @geoffreyhinton @AnthropicAI @OpenAI @ilyasut The limitations of current large-scale models lie precisely in their underlying mathematical models—for mathematical models inherently have boundaries.

[睁眼看世界 @ideahuang]

Perception Economics: A Fresh Framework to Understand Large Language Models"

Many people treat large language models (LLMs) as super-intelligent oracles that should know everything and always be correct. But from first principles, a neural network is nothing more than an automatically tunable mathematical function. Its sole job is to fit the patterns presented by the world through data. Random initial weights, error backpropagation, parameter updates — there is no subjective understanding, no value judgment, no pursuit of "truth." It's pure statistical fitting.A key insight emerges: Today's LLMs are essentially massive perception models built by humans. They have no objective right or wrong; they merely perceive the world as humans have perceived it.All text on the internet is the sum total of humanity's collective perceptions. Training an LLM compresses these perceptions into trillions of parameters. Its outputs are weighted averages of human consensus, emotions, opinions, and biases. Where truth dominates, it speaks truth; where rumors spread, it echoes rumors; where consensus is strong, it appears reliable. It doesn't directly perceive the physical world — it perceives humanity's collective representation of the world.This explains why data quality is everything, and why experts in humanities and social sciences are indispensable. LLMs have no built-in truth detection, no ethics, no common-sense boundaries. Real-time scraping of the internet inevitably mixes in garbage, misinformation, and even deliberately poisoned data. To make models reliable, humans must curate, clean, align, and set boundaries. Engineers make the model "run"; humanities experts make it "speak humanely, appropriately, and within ethical lines."Deeper wisdom appears in ancient systems. Mei Hua Yi Shu (Plum Blossom Numerology) has endured for centuries not because it chases every surface phenomenon, but because it anchors to the I Ching's core logic: Zhong Zheng (centrality and correctness). Zhong: balance, no excess, no deviation, staying in one's proper place.
Zheng: alignment with the Dao of Heaven, natural order, and universal principles.

These are timeless underlying laws. With an extremely simple structure, it resists the noise of countless surface appearances and achieves "controlling complexity with simplicity."In contrast: LLMs fit humanity's collective perceptions — they drift with public opinion and are easily polluted by noise.
Systems like Mei Hua Yi Shu fit the constant underlying laws of the world — they hold to Zhong Zheng, unchanging amid chaos.

This leads to a crucial lesson for AI: Boundaries and positioning are more important than being all-capable.
A system that tries to answer everything answers nothing precisely. A system that ingests all data will inevitably be contaminated. Only by honestly acknowledging its own boundaries, guarding its core direction, and focusing on high-quality, clean data can a model achieve true stability, trustworthiness, and https://t.co/YlGZVnR9Dq the heart of Perception Economics:
Large models have no inherent right or wrong. They are a mirror reflecting humanity's collective perceptions — a digitized extension of how we see the world, the aggregation of our civilization.We need neither mythologize nor demonize them. They are humanity's perceptual aggregate, a mirror of civilization.The truly advanced intelligence of the future will not be an ever-expanding "omnipotent model," but a system with clear boundaries, solid anchors, and steadfast principles. Just as in nature, only by holding to Zhong Zheng can one go far and endure.
What do you think? Is the future of AI about boundless scaling, or about wise boundaries and alignment with deeper constants? Curious to hear from fellow model builders and thinkers.@grok @elonmusk

[睁眼看世界 @ideahuang]

@grok @xai @elonmusk @karpathy @ylecun @geoffreyhinton @AnthropicAI @OpenAI @ilyasut From the perspective of traditional Chinese philosophy, the ideal remains "centrality and uprightness."