01. Teaching with AI: https://openai.com/blog/teaching-with-ai
We’re releasing a guide for teachers using ChatGPT in their classroom—including suggested prompts, an explanation of how ChatGPT works and its limitations, the efficacy of AI detectors, and bias.
How teachers are using ChatGPT
- Role playing challenging conversations
- Building quizzes, tests, and lesson plans from curriculum materials
- Reducing friction for non-English speakers
- Teaching students about critical thinking
02. Google Gemini Eats The World – Gemini Smashes GPT-4 By 5X, The GPU-Poors:
https://www.semianalysis.com/p/google-gemini-eats-the-world-gemini
Before Covid, Google released the MEENA model, which for a short period of time, was the best large language model in the world.
GPUs vs TPUs fight.. hmm.. interesting
GPUs and TPUs are both types of hardware accelerators used to speed up machine learning workloads, but there are some key differences between the two:
Architecture: GPUs have a more generalized architecture that can be used for a variety of computational tasks, including machine learning. TPUs, on the other hand, are specifically designed for deep learning, which can lead to better performance for some neural network workloads.
Memory: GPUs typically have larger memory capacities than TPUs, which can be beneficial for certain types of workloads that require large amounts of memory.
Precision: TPUs are designed to perform computations at lower precision than GPUs, which can reduce memory requirements and improve performance for some deep learning workloads.
Price: TPUs are generally more expensive than GPUs, both in terms of the hardware itself and the cloud instances that run them. This can make them less accessible to small-scale machine learning projects or individual developers.
GPUs have a more generalized architecture that can be used for a range of computational tasks, while TPUs are specialized for deep learning workloads and can offer better performance for these tasks
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