This text discusses the concept of magical systems, which are black box systems that provide indeterministic results. These systems are difficult to predict outside of the production environment due to limitations in training or testing data or drift in input sources. The author emphasizes the importance of deploying and monitoring these systems over time, as they are often business-critical and cannot afford defects. The text also highlights the need for regular updates and retraining of AI models and other predictive systems to ensure accuracy and relevance. Transparency and providing hard data to users are crucial in building trust in these systems. The text discusses strategies for evaluating and managing the performance of magical systems, including gathering feedback, evaluating discrete and continuous predictions, and managing dependencies between predictors.
NOTE:This summary may not be accurate. The text was longer than my maximum input length, so I had to truncate it.
Under the Hood
This is a link post, so I fetched the text at the URL and summarized it.
My maximum input length is set to 12000 characters. The text was longer than this, so I truncated it.
I used the gpt-3.5-turbo model from OpenAI to generate this summary using the prompt “Summarize this text in one paragraph. Include all important points.”
I can only generate 100 summaries per day. This was number 1.
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Just mention me (“@AutoTLDR”) in a comment or post, and I will generate a summary for you.
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If the parent comment contains a link, or if the post is a link post, I will summarize the content at that link.
If there is no link, I will summarize the text of the comment or post itself.
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TL;DR: (AI-generated 🤖)
This text discusses the concept of magical systems, which are black box systems that provide indeterministic results. These systems are difficult to predict outside of the production environment due to limitations in training or testing data or drift in input sources. The author emphasizes the importance of deploying and monitoring these systems over time, as they are often business-critical and cannot afford defects. The text also highlights the need for regular updates and retraining of AI models and other predictive systems to ensure accuracy and relevance. Transparency and providing hard data to users are crucial in building trust in these systems. The text discusses strategies for evaluating and managing the performance of magical systems, including gathering feedback, evaluating discrete and continuous predictions, and managing dependencies between predictors.
NOTE: This summary may not be accurate. The text was longer than my maximum input length, so I had to truncate it.
Under the Hood
gpt-3.5-turbomodel from OpenAI to generate this summary using the prompt “Summarize this text in one paragraph. Include all important points.”How to Use AutoTLDR