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Advancing AI trust with new responsible AI tools, capabilities, and resources

AWS Machine Learning Blog

As generative AI continues to drive innovation across industries and our daily lives, the need for responsible AI has become increasingly important. At AWS, we believe the long-term success of AI depends on the ability to inspire trust among users, customers, and society.

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Meta raises the bar with open source Llama 3 LLM

AI News

Meta has introduced Llama 3 , the next generation of its state-of-the-art open source large language model (LLM). The company’s 8 billion parameter pretrained model also sets new benchmarks on popular LLM evaluation tasks: “We believe these are the best open source models of their class, period,” stated Meta.

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All You Need to Know About Gemma, the Open-Source LLM Powerhouse

Analytics Vidhya

Google has been a frontrunner in AI research, contributing significantly to the open-source community with transformative technologies like TensorFlow, BERT, T5, JAX, AlphaFold, and AlphaCode. What is Gemma LLM?

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With Generative AI Advances, The Time to Tackle Responsible AI Is Now

Unite.AI

Today, seven in 10 companies are experimenting with generative AI, meaning that the number of AI models in production will skyrocket over the coming years. As a result, industry discussions around responsible AI have taken on greater urgency.

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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning Blog

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Similar to how a customer service team maintains a bank of carefully crafted answers to frequently asked questions (FAQs), our solution first checks if a users question matches curated and verified responses before letting the LLM generate a new answer. No LLM invocation needed, response in less than 1 second.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning Blog

Evaluating large language models (LLMs) is crucial as LLM-based systems become increasingly powerful and relevant in our society. Rigorous testing allows us to understand an LLMs capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk.

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