Purrrr
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Feature Relationships 101: Lessons from the Ames Housing Data
In the realm of real estate, understanding the intricacies of property features and their impact on sale prices is paramount. In this exploration, we’ll dive deep into the Ames Housing dataset, shedding light on the relationships between various features and their correlation with the sale price. Harnessing the power of data visualization, we’ll unveil patterns, …
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11 Best Smart Bulbs (2024): Lamp Bulbs, Ambient, Color, Etc
An internet-connected light bulb is one of the easiest way to start building a smarter home.
How do I stop people from looking directly at the camera?
I’m trying to generate images of people using Stable Diffusion, but they always look directly at the camera. It’s starting to drive me crazy! I know that nobody looks directly at the camera when they’re taking a selfie in the mirror. So why does Stable Diffusion keep doing this? I’ve tried everything I can think …
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The US Has Big Plans for Wind Energy—but an Obscure 1920s Law Is Getting in the Way
The Biden administration aims to deploy offshore wind turbines capable of generating 30 gigawatts of power by 2030. With less than a decade to go, the country remains woefully behind target.
Getting catfished in 2006 simulator
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A decoder-only foundation model for time-series forecasting
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 …
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IBM Databand: Self-learning for anomaly detection
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 …
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A decoder-only foundation model for time-series forecasting
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”
Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart
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 …
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