Remove 2010 Remove Algorithm Remove Natural Language Processing
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NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

Natural Language Processing Getting desirable data out of published reports and clinical trials and into systematic literature reviews (SLRs) — a process known as data extraction — is just one of a series of incredibly time-consuming, repetitive, and potentially error-prone steps involved in creating SLRs and meta-analyses.

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How artificial intelligence went from science fiction to science itself?

Dataconomy

Nonetheless, starting from around 2010, there has been a renewed surge of interest in the field. This can be attributed primarily to remarkable advancements in computer processing power and the availability of vast amounts of data.

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Top 9 AI conferences and events in USA – 2023

Data Science Dojo

A Glimpse into the future : Want to be like a scientist who predicted the rise of machine learning back in 2010? Learning from real-world applications : Who doesn’t want to revolutionize their manufacturing process by integrating AI, a strategy learned from a case study at an AI conference.

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Innovative Generative AI Companies

Pickl AI

By leveraging powerful Machine Learning algorithms, Generative AI models can create novel content such as images, text, audio, and even code. Founded in 2010, DeepMind was acquired by Google in 2014 and has since become one of the most respected AI research companies in the world.

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Customizing coding companions for organizations

AWS Machine Learning Blog

This retrieval can happen using different algorithms. Her research interests lie in Natural Language Processing, AI4Code and generative AI. His research interests lie in the area of AI4Code and Natural Language Processing. He received his PhD from University of Maryland, College Park in 2010.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

Parallel computing uses these multiple processing elements simultaneously to solve a problem. This is accomplished by breaking the problem into independent parts so that each processing element can complete its part of the workload algorithm simultaneously. It also means not all workloads are equally suitable for acceleration.

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

AWS Machine Learning Blog

Overview of RAG RAG solutions are inspired by representation learning and semantic search ideas that have been gradually adopted in ranking problems (for example, recommendation and search) and natural language processing (NLP) tasks since 2010. The search precision can also be improved with metadata filtering.

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