[Tree] Exploration-focused training for robotics AI
Version 0.12 (2024-05-10 19:22:01.669000)
updates: Algorithm enables robots to learn new tasks more efficiently
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Version 0.11 (2024-05-02 09:48:47.382000)
updates: The article highlights the potential of the AI-powered NoodleBot, which utilizes the MaxDiff RL algorithm developed by Northwestern University engineers, to outperform cutting-edge robotic systems.
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Version 0.1 (2024-05-02 09:43:28.700000)
updates: New AI algorithm enhances reliability and performance of robots
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Version 0.09 (2024-04-24 10:47:17.631000)
updates: Added information about the role of AI in game development based on the perspective of Candy Crush developers
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Version 0.08 (2024-04-18 01:22:18.788000)
updates: The report from Stanford University's Institute for Human-Centered Artificial Intelligence reveals that artificial intelligence has surpassed human performance in several basic tasks.
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Version 0.07 (2024-01-10 14:00:51.169000)
updates: The article provides a broader perspective on gaming achievements and the dedication of gamers, showcasing various examples from the gaming world. It emphasizes the significance of Willis Gibson's accomplishment in beating the original Nintendo version of Tetris, a milestone previously only reached by AI. The article also highlights the dedication of speedrunners, the popularity of Minecraft, and the community efforts in games like Halo 3. The mention of gamification and gameful design further explores the motivation and experiences that gamers seek. Overall, the article adds depth and context to the story of Willis Gibson's achievement and the broader gaming community.
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Version 0.06 (2024-01-04 00:20:48.809000)
updates: Updated information on training time and shortcuts discovered
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Version 0.05 (2023-12-21 15:52:34.551000)
updates: Updated information on training time and shortcuts discovered
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Version 0.04 (2023-12-21 15:52:12.420000)
updates: New record set by AI robot CyberRunner in maze puzzle game
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Version 0.03 (2023-12-21 15:48:14.485000)
updates: AI robot CyberRunner beats human record in maze puzzle game
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Version 0.02 (2023-11-27 11:33:09.879000)
updates: Introduces the HuGE method for training AI agents using crowdsourced feedback
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Version 0.01 (2023-11-25 15:38:19.475000)
updates: The article explores the emerging trends and future potential of human-machine interaction with AI and cognitive computing. It discusses current interaction methods such as voice and text commands and explores future possibilities like gesture and body movement control, virtual reality and augmented reality interaction, and neural implant interaction. The article also highlights the improvement in machines' ability to understand and interpret human emotions and the challenges related to privacy and data security. It concludes by emphasizing the need to address these challenges and ensure a fair transition for those affected by automation and AI.
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