OpenAI Red Teaming Network
We’re announcing an open call for the OpenAI Red Teaming Network and invite domain experts interested in improving the safety of OpenAI’s models to join our efforts.
Improving your LLMs with RLHF on Amazon SageMaker
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
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
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 …
IBM TechXchange underscores the importance of AI skilling and partner innovation
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
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 …
How United Airlines built a cost-efficient Optical Character Recognition active learning pipeline
In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab, build an active learning framework on AWS to automate the processing of passenger documents. “In order to deliver the best flying experience for our passengers and make our internal business process as efficient as possible, we have developed …
“Teams will get smarter and faster”: A conversation with Eli Manning
For the last three years, IBM has worked with two-time champion Eli Manning to help spread the word about our partnership with ESPN. The nature of that partnership is pretty technical, involving powerful AI models—built with watsonx—that analyze massive data sets to generate insights that help ESPN Fantasy Football team owners manage their teams. Eli …
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Train and deploy ML models in a multicloud environment using Amazon SageMaker
As customers accelerate their migrations to the cloud and transform their business, some find themselves in situations where they have to manage IT operations in a multicloud environment. For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from …
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Realizing telecommunications transformation with Google Cloud generative AI
Generative AI is driving the next phase of cloud transformation for communications service providers (CSPs). Although we are still in early days, significant progress has been made towards realizing the promise of gen AI in telecommunications. Google Cloud has been broadening and enhancing the capabilities of its foundation models, democratizing gen AI usage by simplifying …
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