HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion

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Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from coordinates (e.g., xyz) to signals (e.g., signed distances), have shown remarkable promise as a high-fidelity and compact representation. However, the lack of a regular and explicit grid structure also makes it challenging to apply generative modeling directly on implicit neural fields in …

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Unleashing the power of Presto: The Uber case study

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The magic behind Uber’s data-driven success Uber, the ride-hailing giant, is a household name worldwide. We all recognize it as the platform that connects riders with drivers for hassle-free transportation. But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. Every …

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Innovation for Inclusion: Hack.The.Bias with Amazon SageMaker

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This post was co-authored with Daniele Chiappalupi, participant of the AWS student Hackathon team at ETH Zürich. Everyone can easily get started with machine learning (ML) using Amazon SageMaker JumpStart. In this post, we show you how a university Hackathon team used SageMaker JumpStart to quickly build an application that helps users identify and remove …

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Improving your LLMs with RLHF on Amazon SageMaker

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Reinforcement Learning from Human Feedback (RLHF) is recognized as the industry standard technique for ensuring large language models (LLMs) produce content that is truthful, harmless, and helpful. The technique operates by training a “reward model” based on human feedback and uses this model as a reward function to optimize an agent’s policy through reinforcement learning …

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Bring AI to Looker with the Machine Learning Accelerator

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Machine learning opens up opportunities to get more value out of data, and business users are eager to see that value. However, today’s machine learning experts are facing a lot of requests and their expertise is at a premium. What if data analysts had the ability to create and test their own machine learning models?  …

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Distilling step-by-step: Outperforming larger language models with less training data and smaller model sizes

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Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For …

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IBM TechXchange underscores the importance of AI skilling and partner innovation

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Generative AI and large language models are poised to impact how we all access and use information. But as organizations race to adopt these new technologies for business, it requires a global ecosystem of partners with industry expertise to identify the right enterprise use-cases for AI and the technical skills to implement the technology. During …

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Distilling step-by-step: Outperforming larger language models with less training data and smaller model sizes

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For …

Read more “Distilling step-by-step: Outperforming larger language models with less training data and smaller model sizes”