杨晓光(Yang Xiaoguang)
@yangxiaoguangAI entrepreneurs in China Serial Entrepreneur,Technical Writer #AI | #PromptEngineer | #Digital 连续创业者,AI创业者,技术作家 数字化转型专家
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that's how you can calculate and choose your GPU machine based on your requirements (for FLOPs)
我给团队AI大模型应用的目标是,如何在有限时间和有限信息的条件下,以最快速度、最高效率作出错误率最低的结果。
Confucius remarked, "He who rules the people, depending upon the moral sentiment, is like the Pole-star, which keeps its place while all the other stars revolve round it." 子曰: “为政以德,譬如北辰,居其所而众星共之。”
尽管AI在下棋和内容生成等特定任务上取得了巨大进步,但AGI的概念却让人大跌眼镜,AGI仍然是个巨大的泡沫。
Remember the llm.c repro of the GPT-2 (124M) training run? It took 45 min on 8xH100. Since then, @kellerjordan0 (and by now many others) have iterated on that extensively in the new modded-nanogpt repo that achieves the same result, now in only 5 min! Love this repo 👏 600 LOC
尽管几次因超速而被罚,但有时仍忍不住超速驾车。飞驰中,我感受到了一种超越日常的存在感——那是作为人类才能体验的自由的感觉。如果只是中规中矩的驾车,我会觉得自己与一个执行程序的机器人没有区别,倘若生命只剩下机械无聊的重复,还不如都换成自动驾驶系统,人类开车的意义何在?
未来的VR可能会让我们摆脱物理世界的局限。在教育领域,学生可以身临其境地探索远古文明,体验物理实验的复杂过程,甚至进入人体内部观察细胞运作。在医疗领域,医生可以通过虚拟环境进行远程手术或高风险操作的模拟训练。VR不仅提供了新方式,也改变了我们学习与实践的范式。
从红杉的报告来看,AI的基础设施那几层已经都已经有很强的玩家入局,唯独AI应用层现在还没有几乎是一片蓝海,对于普通创业者AI应用地带机会更大。 链接:Generative AI’s Act o1 sequoiacap.com/article/genera…
Ask ChatGPT “based on what you know about me. draw a picture of what you think my current life looks like” past your responses below. thanks again @mreflow & @danshipper
只有真正理解了人类智能的本质特征,才能设计出更接近通用人工智能AGI的LLMd。人们现在需要更务实地看待AI技术的现状,不要期望过高。
Moravec's paradox in LLM evals I was reacting to this new benchmark of frontier math where LLMs only solve 2%. It was introduced because LLMs are increasingly crushing existing math benchmarks. The interesting issue is that even though by many accounts (/evals), LLMs are inching…
Only through a thorough understanding of the intrinsic properties and mechanisms of human intelligence can we architect systems that meaningfully approach Artificial General Intelligence (AGI).
Moravec's paradox in LLM evals I was reacting to this new benchmark of frontier math where LLMs only solve 2%. It was introduced because LLMs are increasingly crushing existing math benchmarks. The interesting issue is that even though by many accounts (/evals), LLMs are inching…
过于真实,不仅是民主党,这也是美国选举真实写照。
The party of hypocrites and frauds? No way!
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