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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world.

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Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

AWS Machine Learning Blog

We perform a k-nearest neighbor (k-NN) search to retrieve the most relevant embeddings matching the user query. According to the information provided in the summary, GPT-3 from 2020 had 175B (175 billion) parameters, while GPT-2 from 2019 had 1.5B (1.5 Compared to GPT-2, how many more parameters does GPT-3 have?

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Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

First, “Selection via Proxy,” which appeared in ICLR 2020. And please see our work, our paper “Selection via Proxy” from ICLR 2020 for more details on core-set selection, as well as all of the other datasets and methods that we tried there. I was super fortunate to work with amazing researchers from Stanford on this. AB : Got it.

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Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

First, “Selection via Proxy,” which appeared in ICLR 2020. And please see our work, our paper “Selection via Proxy” from ICLR 2020 for more details on core-set selection, as well as all of the other datasets and methods that we tried there. I was super fortunate to work with amazing researchers from Stanford on this. AB : Got it.

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Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

First, “Selection via Proxy,” which appeared in ICLR 2020. And please see our work, our paper “Selection via Proxy” from ICLR 2020 for more details on core-set selection, as well as all of the other datasets and methods that we tried there. I was super fortunate to work with amazing researchers from Stanford on this. AB : Got it.

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

For a given frame, our features are inspired by the 2020 Big Data Bowl Kaggle Zoo solution ( Gordeev et al. ): we construct an image for each time step with the defensive players at the rows and offensive players at the columns. This is achieved through the Guided GradCAM algorithm ( Ramprasaath et al. ). probability.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

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