I’m thrilled to invite you to embark on an enlightening journey with me on StefanSpeaks. As we stand at the forefront of a technological revolution, it’s time to explore and understand the profound impact of Artificial Intelligence on our future, our humanity, and the boundless opportunities it presents. In 2016, I stepped into the world …
Across the globe, financial institutions are rapidly modernizing to deliver secure, seamless payment experiences that meet the demands of digital-first consumers. Financial institutions face the challenge of enabling digital payments while simultaneously managing existing payment capabilities like checks. Although check usage is decreasing worldwide, in the US, checks remain the popular payment option for rent …
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Launched in 2021, Amazon SageMaker Canvas is a visual, point-and-click service for building and deploying machine learning (ML) models without the need to write any code. Ready-to-use Foundation Models (FMs) available in SageMaker Canvas enable customers to use generative AI for tasks such as content generation and summarization. We are thrilled to announce the latest …
A new study underscores the potential of AI and accelerated computing to deliver energy efficiency and combat climate change, efforts in which NVIDIA has long been deeply engaged. The study, called “Rethinking Concerns About AI’s Energy Use,” provides a well-researched examination into how AI can — and in many cases already does — play a …
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Posted by Rajat Sen and Yichen Zhou, Google Research Time-series forecasting is ubiquitous in various domains, such as retail, finance, manufacturing, healthcare and natural sciences. In retail use cases, for example, it has been observed that improving demand forecasting accuracy can meaningfully reduce inventory costs and increase revenue. Deep learning (DL) models have emerged as …
Read more “A decoder-only foundation model for time-series forecasting”
Almost a year ago, IBM encountered a data validation issue during one of our time-sensitive mergers and acquisitions data flows. We faced several challenges as we worked to resolve the issue, including troubleshooting, identifying the problem, fixing the data flow, making changes to downstream data pipelines and performing an ad hoc run of an automated …
Read more “IBM Databand: Self-learning for anomaly detection”
Posted by Rajat Sen and Yichen Zhou, Google Research Time-series forecasting is ubiquitous in various domains, such as retail, finance, manufacturing, healthcare and natural sciences. In retail use cases, for example, it has been observed that improving demand forecasting accuracy can meaningfully reduce inventory costs and increase revenue. Deep learning (DL) models have emerged as …
Read more “A decoder-only foundation model for time-series forecasting”
One of the most useful application patterns for generative AI workloads is Retrieval Augmented Generation (RAG). In the RAG pattern, we find pieces of reference content related to an input prompt by performing similarity searches on embeddings. Embeddings capture the information content in bodies of text, allowing natural language processing (NLP) models to work with …
Read more “Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart”
Introduction If you’re looking to transform the way you interact with unstructured data, you’ve come to the right place! In this blog, you’ll discover how the exciting field of Generative AI, specifically tools like Vector Search and large language models (LLMs), are revolutionizing search capabilities. You will learn the power of vector search and additionally, …
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This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling properties. Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model capacity and the quantity of …
Read more “Scalable Pre-training of Large Autoregressive Image Models”