ステロイドの影響力 栄光を追い求めた選手たちの衝撃的な末路
今回紹介する動画は「【不正の力】ステロイドによって手に入れた記録とヤバすぎる選手の末路」です。
この動画では、MLBの世界におけるステロイド問題に焦点を当て、その使用が選手のキャリアや記録にどのような影響をもたらしているのかを深堀りします。
特に日本からMLBへ渡り活躍する大谷翔平選手など、海外で名を馳せる選手たちの正当な評価と、ステロイド問題の深刻さに迫ります。
本動画では、ステロイドの使用により短期間で飛躍的に向上した記録、しかし、それによって失われたもの、そして、選手の末路について詳しく説明していきます。
MLB.comをはじめ、各種引用素材を用いた丁寧な解説で、ステロイド問題の根深さと複雑さ、そして、スポーツ界全般に及ぼす影響について理解を深めさせます。
動画は、誰でもわかりやすく面白い内容となっており、MLBの魅力を伝えると共に問題提起を行います。
いかがだったでしょうか。
ぜひこの動画を視聴してみてください。
【不正の力】ステロイドによって手に入れた記録とヤバすぎる選手の末路についてプロの解説者としてどう思いますか？
You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.
The error message you're seeing indicates that you've surpassed the usage limit allowed by your current subscription plan on the OpenAI platform. To resolve this issue, please visit your OpenAI dashboard to review your current plan and billing details. You might need to upgrade your plan to increase your quota or wait until your quota resets, depending on your billing cycle. Additionally, for a detailed explanation of this error and possible solutions, I strongly recommend checking the documentation provided at the link: https://platform.openai.com/docs/guides/error-codes/api-errors. This will give you more insights into managing your API usage efficiently and preventing similar issues in the future.
Could you detail which specific actions or project development stages tend to consume the most quota on the OpenAI platform, potentially leading to surpassing the usage limit?
On the OpenAI platform, certain actions or stages in project development can lead to high quota usage and potentially exceed usage limits. Here are specific areas that tend to consume the most quota:
High-Frequency API Calls: Continuous or high-frequency calls to the API, especially with large models, can quickly exhaust your quota. This is common in applications that interact in real-time with users, such as chatbots, where each message exchange is an API call.
Large Batch Processes: Processing large datasets at once, especially without optimizing the size of the input and the request parameters, can consume a significant amount of your quota. Applications that involve data analysis, content generation from large databases, or bulk text processing are examples where this occurs.
Development and Testing: Development phases, specifically testing with live data or debugging issues, can result in unexpected high quota usage. This often happens due to repeated calls to the API during troubleshooting or running extensive test suites, which might not be optimized for quota usage.
Use of Larger Models: Opting for larger, more powerful models (e.g., GPT-3.5 or the latest available versions) for tasks that could be achieved with smaller models significantly increases quota consumption per request. The larger the model, the more compute and, consequently, quota is required per API call.
Inefficient Code or Queries: Writing inefficient code or queries that fetch more data than needed, or fail to cache results for similar questions, can lead to unnecessary quota usage. Fine-tuning queries and optimizing code to minimize redundancy in API calls can help manage quota usage more effectively.
Lack of Paginated Requests or Batch Processing Optimizations: When fetching or processing data in chunks, failing to implement pagination or inefficient batch processing can lead to making more API calls than necessary, thereby consuming more quota.
To manage and mitigate the risk of surpassing usage limits, consider implementing efficient coding practices, using the appropriate model size for your task, optimizing the frequency of API calls, and thoroughly planning for and monitoring usage during development and testing stages. Moreover, leveraging caching strategies and considering asynchronous or batch processing wherever suitable can also help in reducing quota consumption.