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OpenAI Cofounder: "Learn these 30 research papers and you will know 90% of what matters today."

Please like and bookmark this if you find it useful. I have acess to the top AI researchers and ML PhDs working on the cutting edge in India and the US, will post some good content if this crosses 100 likes. Ilya Sutskever, OpenAI cofounder, gave John Carmack this reading list of approximately 30 research papers and said, ‘If you really learn all of these, you’ll know 90% of what matters today.’ FYI: John Carmack is also considered to be the greatest programmer of all time.

https://arc.net/folder/D0472A20-9C20-4D3F-B145-D2865C0A9FEE

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by Gooner7

Goldman Sachs

The Gods of AI made "Generative Adversarial Networks"

Please give this post +1 like, I am getting demoralized when I see bad posts get 100s of likes but a good post like this gets no likes whatsoever. If I don't get more than 100 likes then I will stop posting research papers here. Ian Goodfellow and his collaborators published a groundbreaking paper that revolutionized the field of artificial intelligence, especially in generative modeling. This is the first time the world was introduced to Generative Adversarial Networks (GANs), a revolutionary framework that pits two neural networks against each other in a game-theoretic scenario. Unlike traditional generative models, GANs consist of a generator and a discriminator. The generator creates data samples, while the discriminator evaluates them, distinguishing between real and generated data. This novel approach not only significantly enhanced the quality and realism of generated data but also provided a robust framework for training generative models in an unsupervised manner. Ian Goodfellow, along with his colleagues, not only pushed the boundaries of generative modeling but also set the stage for a plethora of applications across various domains, from image synthesis and enhancement to data augmentation and beyond. Their innovative work demonstrated the potential of adversarial training in neural networks, opening new avenues in both theoretical research and practical applications in AI.

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a gener...

https://arxiv.org/pdf/1406.2661

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