"items": ["annual_subscription"],
上线3年来，平台在数据驱动下，已实现劳务结算超10亿元，助力施工企业降本增效超15%。一项项数字，真实记录了每位建设者走过的路、流过的汗，也守护着背后一个个家庭的幸福生活。
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比如，和誉医药的第二代FGFR小分子抑制剂ABSK061，在降低对FGFR1抑制的同时，保持对FGFR2/3高选择性，理论上安全性更高。在动物模型中，ABSK061的表现优于Infigratinib，目前ABSK061治疗3-12岁ACH儿童患者的2期临床正在进行中。
I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
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Oddly, it's been reported that one condition is that OpenAI achieves artificial general intelligence. AGI is when AI evolves to or beyond human-level abilities, at which point the entire world turns into rainbows and everyone gets a pony. This could happen later this year, according to those bullish on the technology, or never, according to many researchers. Sam Altman said it was coming in 2025 but has since grown weary of the term.
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