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Is AI the Future of Green Energy?

Unite.AI

Green energy is essential in the fight against climate change. Experts have identified over 50 use cases for AI in the energy sector. Here’s a look at some of the most important uses, highlighting why AI is the future of green energy. AI-powered smart grids help by sending energy where it’s most needed at any given time.

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Time for a data center refresh? Get ahead of the growing digital landscape with a modern data center strategy

IBM Journey to AI blog

While many are choosing the cloud, let’s look at some reasons why a data center refresh may be the more advantageous choice: Operational efficiency We’ve observed that increased energy costs and greater focus on sustainability are pushing for better, more energy-efficient infrastructure.

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The most valuable AI use cases for business

IBM Journey to AI blog

Clarify computer vision AI-powered computer vision enables image segmentation , which has a wide variety of use cases, including aiding diagnosis in medical imaging, automating locomotion for robotics and self-driving cars, identifying objects of interest in satellite images and photo tagging in social media.

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Top Speech to Text AI Tools (2023)

Marktechpost

With Speak, you can create your embeddable recorders, record audio and video in-app, and quickly and effortlessly upload content from your device’s storage. Tags, highlights, and comments are all simple to use and facilitate collaboration. Rev.com Regarding artificial intelligence transcribing services, Rev is among the best.

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Build well-architected IDP solutions with a custom lens – Part 2: Security

AWS Machine Learning Blog

Use IAM roles to control access and enforce least privilege access – To manage user access to IDP services, you should create IAM roles for user access to services in the IDP application and attach the appropriate policies and tags to achieve least privilege access. It provides durable, secure, and redundant storage for your KMS keys.

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Snorkel AI Teams with Google Cloud and Vertex AI to speed AI deployment

Snorkel AI

Labeling unstructured data for ML projects has traditionally involved humans tagging each data point manually. Snorkel Flow’s integration with Vertex AI streamlines and accelerates the MLOps process: Snorkel Flow consumes unstructured data from Google Cloud data services such as Google Cloud Storage (GCS) and BigQuery.

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Snorkel AI Teams with Google Cloud and Vertex AI to speed AI deployment

Snorkel AI

Labeling unstructured data for ML projects has traditionally involved humans tagging each data point manually. Snorkel Flow’s integration with Vertex AI streamlines and accelerates the MLOps process: Snorkel Flow consumes unstructured data from Google Cloud data services such as Google Cloud Storage (GCS) and BigQuery.