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How a stubborn computer scientist accidentally launched the deep learning boom

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During my first semester as a computer science graduate student at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the …

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Enhancing AI-Powered Computer Vision Through Physics-Awareness

Unite.AI

The Future of Physics-Aware AI The researchers are optimistic that continued advancements in this dual modality approach might lead deep learning-based AIs to independently learn the laws of physics.

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Are LLMs Outsmarting Humans in Crafting Persuasive Misinformation? 

Analytics Vidhya

Computer scientists have uncovered […] The post Are LLMs Outsmarting Humans in Crafting Persuasive Misinformation? These sophisticated AI systems can generate human-like text, making them valuable tools for various applications. appeared first on Analytics Vidhya.

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AI is just a bad student.

Towards AI

An analogy to explain how deep learning works… This member-only story is on us. link] When we talk about artificial intelligence, or AI, we tend to mean deep learning. Let’s begin by imagining a group of computer scientists and a very large elephant in the same room… Read the full blog for free on Medium.

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On the Open Letter to Halt New AI Developments: 3 Turing Awardees Present 3 Different Postures

Towards AI

Picture created with Dall-E-2 Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, three computer scientists and artificial intelligence (AI) researchers, were jointly awarded the 2018 Turing Prize for their contributions to deep learning, a subfield of AI. Join thousands of data leaders on the AI newsletter.

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AI Everywhere, All at Once

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The EU’s draft AI Act touches on traceability and deepfakes, but it doesn’t specifically address generative AI–deep-learning models that can produce high-quality text, images, or other content based on its training data. Lanier views generative AI as a social collaboration that mashes up work done by humans.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

IBM computer scientist Arthur Samuel coined the phrase “machine learning” in 1952. In 1962, a checkers master played against the machine learning program on an IBM 7094 computer, and the computer won. Python is the most common programming language used in machine learning.