The goal? Developing a system that accurately predicts your estimated travel time from point A to point B. If you've ever ordered food online or requested a ride through an app, you've likely interacted with a version of this system developed at Google DeepMind.
MACHINE LEARNING
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Google Maps Pathfinding Beyond Navigation Prediction Algorithms
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It goes beyond a simple pathfinding problem. Google Maps' predictions account for dynamically changing conditions such as weather, roadblocks, and traffic lights. Even the data, collected from phone positions, is susceptible to noise and adversarial behavior.
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How PetarV Built AI for Google Maps with DeepMind
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@PetarV_93 on How He Built AI for Google Maps with @GoogleDeepMind ! https://
youtu.be/GwbUKic2Hj8 -
How Google Maps Uses AI to Predict Travel Times
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Ever wondered how Google Maps predicts your travel time? The process involves a complex algorithm with underlying research powered by AI. Let's take a behind-the-scenes look at how it works:
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Xformers terminology: transformer variants and efficiency improvements
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In the article, I used "xformers" referring to zillions variants of transformers that claim to improve the efficiency of transformer(
@YiTayML et al., https://
arxiv.org/abs/2009.06732). I am not aware of xformers in SD. -
LoRA, PEFT, and ChatGPT Backend Architecture Discussion
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I am glad you enjoyed the article. I plan to write about LoRA and PEFT(parameter-efficient fine-tuning) in general in a separate article. Yeah, ChatGPT uses GPT-3.5/4 in the backend, all of which are decoder models trained to predict the next token autoregressively.
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Google Gemini Outperforms GPT-4 by 5X in Latest Benchmark
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Google Gemini Eats The World – Gemini Smashes GPT-4 By 5X, The GPU-Poors https://
bit.ly/3PcLUtK
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Neural Operators Enable Zero-Shot Super-Resolution with Sufficient Training Data
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Indeed, neural operators can do zero-shot super-resolution as long as they are trained on data of sufficient resolution. There is a tradeoff between training costs and resulting fidelity. If we can get rich training data, then we can do faithful operator learning.
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Training Data Resolution Insufficient for FNO Neural Operators
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Thank you for creating this to publicize #AI for #PDE However, the training data is too low of a resolution and not enough to learn the operator faithfully for FNO and other neural operators. I also told @jo_brandstetter about this for PDE arena. This is not the right regime.
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Machine Learning Revolutionizes Targeted Disease Treatment Approaches
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Machine Learning for Targeted Disease Treatment
#AI #AIio #BigData #ML #NLU #Futureofwork @gp_pulipaka @stratorob @PetiotEric @EvanKirstel @Fgraillot @HaroldSinnott @HeinzVHoenen @helene_wpli http://
ow.ly/w9ss30sxe5q