The summary of ‘Google releases Gemma 2 and it's IMPRESSIVE!’

The video discusses Google's Chai team's new developments, including Gemini 1.5 Pro and Gemma 2 models for developers. These models offer enhanced context windows, code execution capabilities, and applications in analyzing diverse content types. The importance of long context and code execution using Gemini API is highlighted. The testing of Gemma 2's capabilities, including code generation and subjective prompts, is demonstrated. A comparison between Gemini 2 and Gemini 1.5 Pro is made, emphasizing Gemini 2's intelligence in code generation. The significance of testing GPT models, handling injection attacks, and providing clear instructions for complex tasks is discussed. The video also includes a cognitive challenge involving candle blowing to illustrate model reasoning and suggests using different symbols and techniques for better modeling outcomes. Viewers are encouraged to engage, provide feedback, and explore learning opportunities.

00:00:00

In this segment of the video, the speaker discusses exciting announcements from Google’s Chai team for developers. They introduce Gemini 1.5 Pro with support for 2 million context windows and code execution capability. The Gemma 2 model is also available for testing in Google AI Studio. The increased context window allows for analyzing longer documents like PDFs, videos for transcription, and generating technical blogs. Code execution is different from function calling, enabling developers to run their own code. Gemma 2 is an open model linked to Gemini technology, providing smaller models for various tasks. They plan to test Gemma 2 later in the video.

00:03:00

In this segment of the video, the speaker discusses the use of long context in large-scale analysis and its benefits, such as context caching to save on querying costs. They also introduce code execution using the Gemini API, where code is run in a virtual machine on Google servers. The demonstration includes running code to find the last four digits of the sum of the first 70 prime numbers, yielding the output 0887. This showcases the successful execution of code and validation of the result.

00:06:00

In this segment of the video, the speaker introduces Gemma 2 and proceeds to test its capabilities using Google AI Studio. They test the model by running prompts related to sushi recommendations and code generation. The first test involves subjective prompts to observe the model’s responses, noting the conservative nature of the model’s replies. The second test involves generating a Python function that multiplies two numbers and subtracts 10 from the result. The speaker provides an example of a working code for this function.

00:09:00

In this segment of the video, the speaker discusses testing a GPT model, specifically comparing Gemini 2 and Gemini 1.5 Pro models. They highlight that Gemini 1.5 Pro requires explicit instructions for code generation, unlike Gemini 2, which uses intelligence to generate code. A math problem is then presented to the Gemini 2 model, which successfully breaks down the problem into steps and provides the correct answer. The video also covers the model’s capabilities in information extraction, such as extracting machine learning paper abstracts. The importance of testing and potentially providing explicit instructions for more sophisticated problems is emphasized.

00:12:00

In this segment of the video, the speaker tests a model using abstracts without model names and prompts with injection attacks. They demonstrate manipulating the model’s response by injecting misleading information and successfully changing its behavior. The speaker also discusses a popular prompt injection attack example that typically fails with most models. They emphasize testing the model’s ability to defend against different levels of attacks.

00:15:00

In this segment of the video, Peter lights five candles and blows them out one by one. The task is to determine which candle was blown out first. The model in the video incorrectly identifies candle number four as the first one blown out, due to a misunderstanding and incorrect reasoning. The correct answer is candle number three, the longest candle, as it would have burned for the shortest time. The video suggests using different symbols than the equal sign to improve understanding. It also recommends experimenting with models like Generalized Pooling to find the correct solution. It encourages viewers to provide feedback and engage with the content for valuable insights and learning opportunities.