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We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks: Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better: Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better: Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better: Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work! In summary: - As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding. - Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

AI 社区突然疯传 Google DeepMind 已经实现 RSI。 RSI 即递归自我改进，被很多人视为通往超级智能最关键的一步。AI 可以自己改进训练方法、代码和模型，造出更强的下一代，再让下一代继续升级。真正跑通后，AI 研发可能进入自我加速循环。 起因是爆料账号 lyra 给 Google DeepMind 发了一句「huge congRatulationS Indeed!」，其中三个异常大写字母正好拼成 RSI。随后 Lentils 又贴出一张疑似内部模型列表，里面出现 rsi-model-liverl-le。他还声称 Google 内部有 10 个同系列训练模型槽位，但没有贴出对应截图。 传闻也未必是假的。路透社 8 月报道，谷歌联合创始人 Sergey Brin 已要求 DeepMind 加快 Gemini 研发，并重点推动递归自我改进。DeepMind 6 月的官方报告也把「recursive improvement」列为 AGI 走向 ASI 的四条潜在路径之一。