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Imagine walking into the largest library you’ve ever seen. You have a specific book in mind, but you have no idea where to find it. Fortunately, the library has a computer at the front desk you can use to search its entire inventory by title, author, genre, and more. You enter the title of the …
Over the last week, millions of people around the world have interacted with OpenAI’s ChatGPT, which represents a significant advance for generative artificial intelligence (AI) and the foundation models that underpin many of these use cases. It’s a fitting way to end what has been another big year for the industry. We’re at an exciting …
Read more “The importance of governance: What we’re learning from AI advances in 2022”
Today’s data leaders are expected to make organizations run more efficiently, improve business value, and foster innovation. Their role has expanded from providing business intelligence to management, to ensuring high-quality data is accessible and useful across the enterprise. In other words, they must ensure that data strategy aligns to business strategy. Only from this foundation …
Read more “How data, AI and automation can transform the enterprise”
Posted by Tal Schuster, Research Scientist, Google Research Language models (LMs) are the driving force behind many recent breakthroughs in natural language processing. Models like T5, LaMDA, GPT-3, and PaLM have demonstrated impressive performance on various language tasks. While multiple factors can contribute to improving the performance of LMs, some recent studies suggest that scaling …
Read more “Accelerating Text Generation with Confident Adaptive Language Modeling (CALM)”
Foundation models are large deep learning models trained on a vast quantity of data at scale. They can be further fine-tuned to perform a variety of downstream tasks and form the core backbone of enabling several AI applications. The most prominent category is large-language models (LLM), including auto-regressive models such as GPT variants trained to complete …
Read more “New performance improvements in Amazon SageMaker model parallel library”
Today, we’re happy to announce updates to our Amazon SageMaker Experiments capability of Amazon SageMaker that lets you organize, track, compare and evaluate machine learning (ML) experiments and model versions from any integrated development environment (IDE) using the SageMaker Python SDK or boto3, including local Jupyter Notebooks. Machine learning (ML) is an iterative process. When solving …
Proper estimation of predictive uncertainty is fundamental in applications that involve critical decisions. Uncertainty can be used to assess the reliability of model predictions, trigger human intervention, or decide whether a model can be safely deployed in the wild. We introduce Fortuna, an open-source library for uncertainty quantification. Fortuna provides calibration methods, such as conformal …
Read more “Introducing Fortuna: A library for uncertainty quantification”
Amazon SageMaker Training Managed Warm Pools gives you the flexibility to opt in to reuse and hold on to the underlying infrastructure for a user-defined period of time. This is done while also maintaining the benefit of passing the undifferentiated heavy lifting of managing compute instances in to Amazon SageMaker Model Training. In this post, …
Read more “Best practices for Amazon SageMaker Training Managed Warm Pools”
In an increasingly data-centric world, enterprises must focus on gathering both valuable physical information and generating the information that they need but can’t easily capture. Data access, regulation, and compliance are an increasing source of friction for innovation in analytics and artificial intelligence (AI). For highly regulated sectors such as Financial Services, Healthcare, Life Sciences, …
Developing and training successful machine learning (ML) fraud models requires access to large amounts of high-quality data. Sourcing this data is challenging because available datasets are sometimes not large enough or sufficiently unbiased to usefully train the ML model and may require significant cost and time. Regulation and privacy requirements further prevent data use or …
Read more “Augment fraud transactions using synthetic data in Amazon SageMaker”