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TransAgents: A Recent Approach to Machine Translation for Literary Works

Translating literary classics like into other languages often ends in losing the creator's unique style and cultural nuances. Addressing this longstanding challenge in literary translation is crucial to preserving the essence of works...

Seoul National University Holds ‘AI x Art Hackathon’… Competition to Create Works Using Generative AI

Seoul National University to carry 'AI x Art Hackathon'... Competition to create works using generative AI Seoul National University's Artificial Intelligence (AI) Art Research Center announced on the ninth that it'll be accepting applications...

How Text-to-3D AI Generation Works: Meta 3D Gen, OpenAI Shap-E and more

The power to generate 3D digital assets from text prompts represents one of the exciting recent developments in AI and computer graphics. Because the 3D digital asset market is projected to grow from $28.3...

Ronald T. Kneusel, Creator of “How AI Works: From Sorcery to Science” – Interview Series

We recently received a sophisticated copy of the book “How AI Work: From Sorcery to Science” by Ronald T. Kneusel. I've to this point read over 60 books on AI, and while a few...

When computer vision works more like a brain, it sees more like people do

From cameras to self-driving cars, lots of today’s technologies depend upon artificial...

How GPT works: A Metaphoric Explanation of Key, Value, Query in Attention, using a Tale of Potion

Side note: the language “decoder” is a vestige from the unique paper, as Transformer was first used for machine translation tasks. You “encode” the source language into embeddings, and “decode” from the embeddings to...

How GPT works: A Metaphoric Explanation of Key, Value, Query in Attention, using a Tale of Potion

Side note: the language “decoder” is a vestige from the unique paper, as Transformer was first used for machine translation tasks. You “encode” the source language into embeddings, and “decode” from the embeddings to...

Generating Song Recommendations Introduction Goal Data Pipeline Summarization Model Description Comparison Musical Feature Comparison User Flow Results Conclusion and Future Works

Jaykumar Patel, Janvi Patel, Aniketh Devarasetty, Malvika Vaidya, Seann Robbins, Tanner HudnallIn this text, we'll showcase our initial attempts at making a song suggestion model. We'll give an outline of our dataset and the...

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