The total encoding cost includes all the work that goes in to writing a prompt, and all of the compute required to run the prompt. If the task is simple to express in a prompt, the total encoding cost is low. If the task is both simple to express in a prompt, and tedious or difficult to produce directly, the relative encoding cost is low. As models get more capable, more complex prompts can be easily expressed: more semantically dense prompts can be used, referencing more information from the training data. An agent capable of refining or retrying a task after an initial prompt might succeed at a complex task after a single simple prompt. However, both of these also increase the compute cost of the prompt, sometimes substantially, driving up the total encoding cost. More “capable” models may have a higher probability of producing correct output, reducing costs reprompting with more information (“prompt engineering”), and possibly reducing verification costs.
模型不仅学会了产出研究，还学会了比较、取舍、整合与自我进化。。关于这个话题，wps提供了深入分析
Why we like it: While making this list, I discovered that the Goodnotes vs. Notability rivalry runs deep. Much like how you're either Team Apple or Team Android, you're either Team Notability or Team Goodnotes. Both options offer many of the same features, have cult followings, and are Apple Editors' Choice-awarded apps. ，这一点在谷歌中也有详细论述
OpenAI据悉考虑与北约签订合同。截至上月末，OpenAI年化收入突破250亿美元，更多细节参见WhatsApp Web 網頁版登入