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Understanding the different types and kinds of Artificial Intelligence

IBM Journey to AI blog

However, AI capabilities have been evolving steadily since the breakthrough development of artificial neural networks in 2012, which allow machines to engage in reinforcement learning and simulate how the human brain processes information.

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Making Sense of the Mess: LLMs Role in Unstructured Data Extraction

Unite.AI

With nine times the speed of the Nvidia A100, these GPUs excel in handling deep learning workloads. This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction.

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From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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The History of Artificial Intelligence (AI)

Pickl AI

” During this time, researchers made remarkable strides in natural language processing, robotics, and expert systems. Notable achievements included the development of ELIZA, an early natural language processing program created by Joseph Weizenbaum, which simulated human conversation.

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A comprehensive guide to learning LLMs (Foundational Models)

Mlearning.ai

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1)

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Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

AWS Machine Learning Blog

Dive into Deep Learning ( D2L.ai ) is an open-source textbook that makes deep learning accessible to everyone. If you are interested in learning more about these benchmark analyses, refer to Auto Machine Translation and Synchronization for “Dive into Deep Learning”.

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Behind the glory: the dark sides of AI models that big tech will not tell you.

Towards AI

Building natural language processing and computer vision models that run on the computational infrastructures of Amazon Web Services or Microsoft’s Azure is energy-intensive. The Myth of Clean Tech: Cloud Data Centers The data center has been a critical component of improvements in computing.