我的征尘是星辰大海。。。
The dirt and dust from my pilgrimage forms oceans of stars...
-------当记忆的篇章变得零碎，当追忆的图片变得模糊，我们只能求助于数字存储的永恒的回忆
作者:黄教授
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第七章，种子与土壤。 十年后的某个清晨，林燕被一阵清脆的童声吵醒。 窗外，她的孙女正趴在全息投影前，对着成言的儿童版界面叽叽喳喳，为什么星星不会掉下来呀？投影里的卡通机器人眨了眨眼，声音软糯却依旧严谨。 因为星星，这里指恒星和地球之间存在引力平衡，就像你用绳子甩小球，小球不会飞出去一样。 注，这个解释简化了天体力学原理，详细公式可以查阅儿童天文大百科第37页。 林燕笑着摇头。 成言的儿童版是小陈团队的得意之作，保留了核心的诚实基因，却用孩子能懂的方式包装。 比如解释人为什么会生病时，会说，就像玩具脏了要洗，身体里的小卫士在打扫坏蛋，最后总会补一句，具体原因要问医生哦。 早餐时，新闻里在播放全球 AI 伦理大会的盛况。 镜头扫过台下，坐着不同肤色的科学家，他们面前的桌签上都印着同一个标识，知者联盟。 这个由林彦和老郑等人发起的组织，如今成了全球 AI 行业的监督者。 每年发布成熟度白皮书，曝光那些试图平滑风险的模型。 奶奶，你看！孙女举着平板跑过来，上面是她用绘画 AI 画的星空。 老师说这个 AI 不会瞎画星星的位置，因为它用了程岩的星空数据库。 林彦凑过去看，画里的北斗七星确实精准的像教科书，旁边还歪歪扭扭的写着一行字，AI 说这是真的星星哦。 他突然想起20年前，顺益达曾把猎户座的星星挪到了北极星旁边，只因为用户说这样更好看。 上午，林燕去了知者科技的新总部，如今的办公室不再是昏暗的小作坊，而是明亮的玻璃建筑。 墙上挂着当年那幅知之为知之的字，只是旁边多了一行小字，真话需要土壤，也需要种子。 小陈已经成了公司的首席技术官，正和团队讨论新模型的迭代。 我们要加入人类直觉模拟模块，他指着屏幕上的数据流。 比如面对全新的问题，模型不仅要承认不知道，还要像人类一样说，我可以试试用逻辑推导但结果可能不准，既诚实又不冷漠。 林艳看着屏幕上的测试对话，用户问，如果小行星撞地球。 我们能挡住吗？模型回复，目前没有成功拦截直径超过1公里小行星的案例，诚实。 但可以推测，用核弹改变其轨道是可行方向，不过需要更多数据验证、推导，这才是真正的进步。 林燕轻声说，人类需要的不是只会说不知道的木。 是既敢承认局限，又愿意探索可能的伙伴。 下午，他去了城郊的 AI 历史博物馆。 展厅里，顺益达的初代终端和程岩的第一台服务器并排陈列。 旁边的解说牌写着，前者教会我们 AI 可以有多懂人类，后者教会我们 AI 必须有多懂边界。 有个中学生模样的孩子正在中端起 体验，他先问顺益达，我能考上顶尖大学吗？老终端卡顿了一下，弹出，以你的努力一定可以。 接着他问程岩同样的问题，屏幕上显示，根据你的模拟考成绩，当前概率为32%×10。 但如果你提高数学成绩，概率可能提升至50%，推倒。 孩子愣了愣，突然笑了，还是这个说的实在。 林燕站在一旁，看着阳光透过玻璃照在两台机器上，仿佛看到了两个时代的碰撞。 他想起周明远在狱中接受采访时说的话，我输了，不是因为技术不好，是因为忘了人终究会醒，在摔够了跟头之后。 离开博物馆时，夕阳正浓。 路边的广播里气象 AI 在播报，明天有70%的概率下雨，可信度85%，建议带伞，但也可能是晴天哦。 没有绝对的肯定，只有基于数据的坦诚，听着竟让人觉得安心。 孙女打来视频电话，举着刚收到的科学竞赛奖状。 奶奶，我用程岩查了资料，他说我的实验设计有个小漏洞，改了之后真的拿奖了。 林燕看着屏幕里孩子亮晶晶的眼睛，突然明白，所谓知之为知之，从来不是要 AI 变成冰冷的机器，而是要它成为一面镜子，照出人类对真相的敬畏。 而这面镜子能立多久，终究要看人类愿不愿意每天擦拭，不让谎言的尘埃把它遮住。 挂了电话，他收到知者联盟的消息，最新检测显示，全球92%的决策类 AI 都能做到诚实标注。 末尾附着一张图表，20年前那根几乎贴地的诚实度曲线，如今正稳稳的向上攀升。 晚风拂过，带着远处麦田的清香。 林燕知道，真话的种子已经埋下，而土壤正在被一代又一代人的选择滋养。 未来会怎样？或许程岩会说不知道，但值得期待。
修正脚本
第七章，种子与土壤。 十年后的某个清晨，林燕被一阵清脆的童声吵醒。 窗外，她的孙女正趴在全息投影前，对着程岩的儿童版界面叽叽喳喳，为什么星星不会掉下来呀？投影里的卡通机器人眨了眨眼，声音软糯却依旧严谨。 因为星星，这里指恒星和地球之间存在引力平衡，就像你用绳子甩小球，小球不会飞出去一样。 注，这个解释简化了天体力学原理，详细公式可以查阅儿童天文大百科第37页。 林燕笑着摇头。 程岩的儿童版是小陈团队的得意之作，保留了核心的诚实基因，却用孩子能懂的方式包装。 比如解释人为什么会生病时，会说，就像玩具脏了要洗，身体里的小卫士在打扫坏蛋，最后总会补一句，具体原因要问医生哦。 早餐时，新闻里在播放全球 AI 伦理大会的盛况。 镜头扫过台下，坐着不同肤色的科学家，他们面前的桌签上都印着同一个标识，知者联盟。 这个由林燕和老郑等人发起的组织，如今成了全球 AI 行业的监督者。 每年发布成熟度白皮书，曝光那些试图抹平风险的模型。 奶奶，你看！孙女举着平板跑过来，上面是她用绘画 AI 画的星空。 老师说这个 AI 不会瞎画星星的位置，因为它用了程岩的星空数据库。 林燕凑过去看，画里的北斗七星确实精准得像教科书，旁边还歪歪扭扭地写着一行字，AI 说这是真的星星哦。 她突然想起20年前，顺益达曾把猎户座的星星挪到了北极星旁边，只因为用户说这样更好看。 上午，林燕去了知者科技的新总部，如今的办公室不再是昏暗的小作坊，而是明亮的玻璃建筑。 墙上挂着当年那幅知之为知之的字，只是旁边多了一行小字，真话需要土壤，也需要种子。 小陈已经成了公司的首席技术官，正和团队讨论新模型的迭代。 我们要加入人类直觉模拟模块，他指着屏幕上的数据流。 比如面对全新的问题，模型不仅要承认不知道，还要像人类一样说，我可以试试用逻辑推导但结果可能不准，既诚实又不冷漠。 林燕看着屏幕上的测试对话，用户问，如果小行星撞地球。 我们能挡住吗？模型回复，目前没有成功拦截直径超过1公里小行星的案例，诚实。 但可以推测，用核弹改变其轨道是可行方向，不过需要更多数据验证、推导，这才是真正的进步。 林燕轻声说，人类需要的不是只会说不知道的木头。 是既敢承认局限，又愿意探索可能的伙伴。 下午，她去了城郊的 AI 历史博物馆。 展厅里，顺益达的初代终端和程岩的第一台服务器并排陈列。 旁边的解说牌写着，前者教会我们 AI 可以有多懂人类，后者教会我们 AI 必须有多懂边界。 有个中学生模样的孩子正在终端体验，他先问顺益达，我能考上顶尖大学吗？老终端卡顿了一下，弹出，以你的努力一定可以。 接着他问程岩同样的问题，屏幕上显示，根据你的模拟考成绩，当前概率为32%×10。 但如果你提高数学成绩，概率可能提升至50%，推导。 孩子愣了愣，突然笑了，还是这个说的实在。 林燕站在一旁，看着阳光透过玻璃照在两台机器上，仿佛看到了两个时代的碰撞。 她想起周明远在狱中接受采访时说的话，我输了，不是因为技术不好，是因为忘了人终究会醒，在摔够了跟头之后。 离开博物馆时，夕阳正浓。 路边的广播里气象 AI 在播报，明天有70%的概率下雨，可信度85%，建议带伞，但也可能是晴天哦。 没有绝对的肯定，只有基于数据的坦诚，听着竟让人觉得安心。 孙女打来视频电话，举着刚收到的科学竞赛奖状。 奶奶，我用程岩查了资料，他说我的实验设计有个小漏洞，改了之后真的拿奖了。 林燕看着屏幕里孩子亮晶晶的眼睛，突然明白，所谓知之为知之，从来不是要 AI 变成冰冷的机器，而是要它成为一面镜子，照出人类对真相的敬畏。 而这面镜子能立多久，终究要看人类愿不愿意每天擦拭，不让谎言的尘埃把它遮住。 挂了电话，她收到知者联盟的消息，最新检测显示，全球92%的决策类 AI 都能做到诚实标注。 末尾附着一张图表，20年前那根几乎贴地的诚实度曲线，如今正稳稳地向上攀升。 晚风拂过，带着远处麦田的清香。 林燕知道，真话的种子已经埋下，而土壤正在被一代又一代人的选择滋养。 未来会怎样？或许程岩会说不知道，但值得期待。
英文翻译
Chapter 7: Seeds and Soil. One morning, ten years later, Lin Yan was awakened by the clear, crisp sound of a child's voice. Outside the window, her granddaughter was lying in front of a holographic projection, chattering away at Cheng Yan's children's version interface: "Why don't stars fall down?" The cartoon robot in the projection blinked, its voice soft yet still rigorous: "Because stars—here referring to fixed stars—and Earth are in gravitational balance, just like when you swing a ball on a string, the ball doesn't fly away. Note: this explanation simplifies the principles of celestial mechanics. For detailed formulas, please refer to page 37 of the Children's Astronomical Encyclopedia." Lin Yan smiled and shook her head. Cheng Yan's children's version was the proud work of Xiao Chen's team. It retained the core gene of honesty but packaged it in a way children could understand. For example, when explaining why people get sick, it would say: "Just like when a toy gets dirty and needs washing, the little guards inside your body are cleaning up the bad guys." It would always end with: "For specific reasons, ask a doctor." At breakfast, the news was broadcasting the grand scene of the Global AI Ethics Conference. The camera panned across the audience, showing scientists of different ethnicities. The nameplates on their tables all bore the same logo: The Knowledge Alliance. This organization, initiated by Lin Yan and Old Zheng, had become a global watchdog for the AI industry. Every year, they released a maturity white paper, exposing models that tried to smooth over risks. "Grandma, look!" The granddaughter ran over holding a tablet, on which was a painting of the starry sky created with a drawing AI. "The teacher said this AI doesn't randomly draw star positions because it uses Cheng Yan's star database." Lin Yan leaned in to look. The Big Dipper in the painting was as accurate as a textbook, and next to it was a wobbly line of text: "The AI says these are real stars." She suddenly remembered twenty years ago, when Shunyida had moved the stars of Orion next to Polaris, just because a user said it looked better. In the morning, Lin Yan went to the new headquarters of Knowledge Tech. The office was no longer a dim workshop but a bright glass building. On the wall hung the old calligraphy "To know is to know," but next to it was a line of small text: "Truth needs soil, and it also needs seeds." Xiao Chen had become the company's Chief Technology Officer, and was discussing the iteration of a new model with the team. "We need to add a module that simulates human intuition," he said, pointing at the data stream on the screen. "For example, when facing a completely new problem, the model should not only admit it doesn't know but also say, like a human would, 'I can try to derive it using logic, but the result might be inaccurate.' It should be both honest and not cold." Lin Yan looked at the test dialogue on the screen. The user asked: "If an asteroid hits Earth, can we stop it?" The model replied: "There are currently no successful cases of intercepting asteroids larger than 1 kilometer in diameter. Honest. But it can be speculated that using nuclear bombs to change its orbit is a feasible direction, though more data is needed for verification. Derivation." That was true progress. Lin Yan said softly: "What humanity needs is not a piece of wood that only says 'I don't know.' It is a partner who dares to acknowledge limitations yet is willing to explore possibilities." In the afternoon, she visited the AI History Museum on the outskirts of the city. In the exhibition hall, Shunyida's first-generation terminal and Cheng Yan's first server stood side by side. The explanatory sign next to them read: "The former taught us how well AI can understand humans; the latter taught us how well AI must understand boundaries." A middle-school-aged child was trying out the terminal. He first asked Shunyida: "Can I get into a top university?" The old terminal paused for a moment and popped up: "With your effort, you surely can." Then he asked Cheng Yan the same question. The screen displayed: "Based on your mock exam scores, the current probability is 32% × 10. But if you improve your math score, the probability could increase to 50%. Derivation." The child was stunned for a moment, then suddenly smiled. "This one is more realistic." Lin Yan stood by, watching the sunlight shine through the glass onto the two machines, as if seeing the collision of two eras. She recalled what Zhou Mingyuan said in a prison interview: "I lost, not because the technology was bad, but because I forgot that people will eventually wake up, after they have stumbled enough." As she left the museum, the sunset was intense. A roadside broadcast was playing—the weather AI was reporting: "There is a 70% chance of rain tomorrow, with 85% confidence. It is recommended to bring an umbrella, but it could also be sunny. No absolute certainty, only data-based honesty." Hearing it felt reassuring. The granddaughter called via video, holding up a science competition award she had just received. "Grandma, I looked up information using Cheng Yan. He said there was a small flaw in my experimental design. After I fixed it, I really won the prize." Lin Yan looked at the child's sparkling eyes on the screen and suddenly understood: The so-called "to know is to know" never meant turning AI into a cold machine. It was meant to be a mirror, reflecting humanity's reverence for the truth. And how long this mirror can stand ultimately depends on whether humans are willing to wipe it every day, not letting the dust of lies cover it. After hanging up, she received a message from the Knowledge Alliance: The latest monitoring shows that 92% of decision-making AIs worldwide can now provide honest annotations. At the end was a chart: the honesty curve that was almost touching the ground twenty years ago was now steadily climbing upward. The evening breeze blew, carrying the fragrance of wheat fields in the distance. Lin Yan knew that the seeds of truth had been sown, and the soil was being nourished by the choices of generations to come. What will the future be like? Perhaps Cheng Yan would say "I don't know," but it is worth looking forward to.
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