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How artificial intelligence went from science fiction to science itself?

Dataconomy

The timeline of artificial intelligence takes us on a captivating journey through the evolution of this extraordinary field. It all began in the mid-20th century, when visionary pioneers delved into the concept of creating machines that could simulate human intelligence.

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Siri

Dataconomy

Understanding how Siri functions and its evolution sheds light on the remarkable advancements in artificial intelligence and voice recognition. By integrating voice recognition and artificial intelligence, Siri effectively bridges the gap between human interaction and technology. which Apple acquired in 2010.

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The Dezeen guide to AI

Flipboard

Dezeen's new editorial series, AItopia , is all about artificial intelligence. 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.

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How AI Is Paving the Way for Improved Surveillance and Cybersecurity

Dataversity

Cybersecurity is increasingly leaning towards artificial intelligence (AI) to help mitigate threats because of the innate ability AI has to turn big data into actionable insights. For instance, while there were fewer than 50 million unique malware cases in 2010, the number had […].

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

AWS Machine Learning Blog

Dhawal Patel is a Principal Machine Learning Architect at AWS. He has worked with organizations ranging from large enterprises to mid-sized startups on problems related to distributed computing, and Artificial Intelligence. He focuses on Deep learning including NLP and Computer Vision domains.

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NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

This ongoing process straddles the intersection between evidence-based medicine, data science, and artificial intelligence (AI). New research has also begun looking at deep learning algorithms for automatic systematic reviews, According to van Dinter et al. This study by Bui et al.

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The Blurring Lines Between AI Academia and Industry

Dataconomy

These datasets provide the necessary scale for training advanced machine learning models, which would be difficult for most academic labs to collect independently. Increasingly, big tech companies play a pivotal role in AI research, blurring the lines between academia and industry.

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