Ginger helps to translate documents into 40+ languages while Grammarly doesn't have a translation feature.
In an internal memo cutting the Pentagon’s long list of priority technologies down to six, he wrote that the previous list “did not provide the focus that the threat environment of today requires,” and declared that “in alignment with President Trump’s Artificial Intelligence (AI) Action Plan, the Department of War must become an ‘AI‑First’ organization.”
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2000年，由斯坦姆引荐至太仓的德企已超10家；2007年，百家德企在此扎根；到2024年，数量突破500家。这一过程中，太仓对德合作不断提速，从1家到100家德企用了14年；从400家跃升至500家，仅两年有余。
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.
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