I used AI to watch an hour-long lecture

🌈 Abstract

The article discusses the capabilities of large language models, particularly Gemini 1.5 Pro, a new model released by Google. It highlights Gemini 1.5 Pro's ability to understand and reason about long-form content, including videos, and provides details on its performance on various tests. The article also describes the author's personal experience of testing Gemini 1.5 Pro on a video lecture and the insights they were able to extract.

🙋 Q&A

[01] Large Language Models

1. What is the context size of Gemini 1.5 Pro? Gemini 1.5 Pro has a 1,000,000 multimodal token context size, which is a significant improvement over existing models like Claude 2.1 (200k) and GPT4-turbo (128k).

2. How well can Gemini 1.5 Pro retrieve content from its long context? Gemini 1.5 Pro achieves over 99.7% recall on the needle-in-a-haystack test, even up to 10M tokens.

3. What is the author's most interesting finding about Gemini 1.5 Pro? The author finds the model's ability to analyze and reason about videos to be the most interesting, as demonstrated by its performance on the 45-minute silent movie "Sherlock Jr."

[02] Test Drive

1. What was the length of the video the author tested Gemini 1.5 Pro on? The author tested Gemini 1.5 Pro on a 59:59 minute video, which contained 946,800 tokens (equivalent to 710,100 words).

2. What were some of the questions the author asked Gemini 1.5 Pro about the video? The author asked Gemini 1.5 Pro questions about the "rule of 40/20/20", the "minimum viable segment", how to check on the value proposition, why a VC might say no to the product, and to summarize the lecture and provide a glossary of terms.

3. How accurate were the responses from Gemini 1.5 Pro to the author's questions? The responses were mixed, with some questions being answered accurately based on the content of the video, while others were inaccurate or contained hallucinated information not present in the video.

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