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deepmind.google | ||
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blog.google
Gemini is our most capable and general model, built to be multimodal and optimized for three different sizes: Ultra, Pro and Nano. |
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mathscholar.org
[AI summary] The article details how DeepMind's AlphaDev program used AI to discover new sorting algorithms that outperform existing human-derived methods, while discussing broader implications of AI in mathematics and computing. |
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research.google
Posted by Kendra Byrne, Senior Product Manager, and Jie Tan, Staff Research Scientist, Robotics at Google (This is Part 6 in our series of posts co... |
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lilianweng.github.io
So far, I've written about two types of generative models, GAN and VAE. Neither of them explicitly learns the probability density function of real data, $p(\mathbf{x})$ (where $\mathbf{x} \in \mathcal{D}$) - because it is really hard! Taking the generative model with latent variables as an example, $p(\mathbf{x}) = \int p(\mathbf{x}\vert\mathbf{z})p(\mathbf{z})d\mathbf{z}$ can hardly be calculated as it is intractable to go through all possible values of the latent code $\mathbf{z}$. |
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