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Foundations of Computer Vision

Hacker News

shows the number of pages written as a function of time since we mentioned the idea to MIT press for the first time on November 24, 2010. Lots of things have happened since we started thinking about this book in November 2010; yes, it has taken us more than 10 years to write this book. MIT Press, Cambridge, MA, 2010. [5]

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DeepMind

Dataconomy

By harnessing techniques such as deep learning and reinforcement learning, DeepMind has not only redefined the potential of AI but also explored its various applications across fields, from games to real-world problems. This enables their AI systems to make informed decisions based on vast amounts of data.

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The Dezeen guide to AI

Flipboard

Artificial intelligence (AI) is usually summarised as computers performing tasks that would otherwise require a human brain, such as playing chess, recognising faces, driving a car or reading a piece of text and picking out the important information. It underpins most of the advanced capabilities of AI systems.

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Siri

Dataconomy

which Apple acquired in 2010. Origins and development The concept of Siri was rooted in complex AI research aimed at understanding human language. Utilizing advanced voice technology and AI, Siri relies on methods such as Automatic Speech Recognition (ASR) and Natural Language Processing (NLP).

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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

The landscape of enterprise application development is undergoing a seismic shift with the advent of generative AI. This intuitive platform enables the rapid development of AI-powered solutions such as conversational interfaces, document summarization tools, and content generation apps through a drag-and-drop interface.

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The Blurring Lines Between AI Academia and Industry

Dataconomy

The world of AI research is in constant flux, with breakthroughs emerging at a dizzying pace. Increasingly, big tech companies play a pivotal role in AI research, blurring the lines between academia and industry. Increasingly, big tech companies play a pivotal role in AI research, blurring the lines between academia and industry.

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The Evolution of Tabular Data: From Analysis to AI

Towards AI

Last Updated on August 16, 2023 by Editorial Team Author(s): Abid Ali Awan Originally published on Towards AI. Discover how tabular data space is being transformed by Kaggle competitions, the open-source community, and Generative AI. The synthetic datasets were created using a deep-learning generative network called CTGAN.[3]