AIコメンタリー

動画を再生してAIコメンタリーを見る

Oh wow, this sounds like a really deep dive into how AI thinks. It's fascinating how they're framing LLMs not just as text generators, but as systems that can be guided to 'think more deeply.' I'm curious to see what 'guiding' actually entails.

So, Gemini 1.5 Flash is having trouble with the travel planner task, only solving about 5% in a single pass. That's a pretty low success rate for a sophisticated model, which makes me wonder what kind of problems are tripping it up.

Even with the 'best of n' strategy, where they generate a ton of options, it only gets to 55.6%. That's a pretty stark illustration that just throwing more data or more attempts at it doesn't necessarily lead to better problem-solving.

もっと見たいですか？サインアップして全ての会話を見る

新規登録

動画の要約は視聴を開始すると表示されます

Early in the exploration [0:00], the video introduces large language models (LLMs) as advanced AI systems capable of understanding and generating human-like text. It highlights a key challenge: LLMs often struggle with complex problem-solving, with a specific example showing Gemini 1.5 Flash solving only a small fraction of travel planner tasks in a single pass [1:00]. Even with the "best of n" strategy, which generates many independent responses, the success rate only reaches about 55.6%, indicating that sheer quantity doesn't guarantee quality for deeper thinking [1:30].

全機能を利用するには

サインアップまたはログインして、完全な動画分析機能にアクセスしましょう

新規登録ログイン

現在のセクション要約