Useful Websites
Dora-rs
In 2023, as AI continues to boom, the robotic framework has seen little innovation—until dora-rs. dora-rs is a groundbreaking robotic framework designed to modernize robotic applications, making them faster and simpler. It demonstrates impressive performance improvements by leveraging a shared memory server and Apache Arrow for zero-copy operations. This significant enhancement is especially beneficial for beginners, AI practitioners, and weekend hobbyists who have faced challenges due to the lack of Python support in robotics. Dora-rs stands out with its innovative features, marking a new era in robotics.
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Record Added: 08 April 2024
The Bitter Lesson
Rich Sutton's "The Bitter Lesson" emphasizes the profound impact of computational scalability over human-designed methods in AI progress. Highlighting historical instances in AI research, it advocates for leveraging computation to unlock AI's future potential, challenging the reliance on human knowledge. This reflective piece underscores a pivotal shift towards computation-driven AI methods, urging the field to embrace general-purpose approaches for groundbreaking advancements.
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Record Added: 01 April 2024
Prompt Engineering Guide
The Prompt Engineering Guide offers comprehensive insights into prompt engineering—a new field essential for optimizing the use of large language models (LLMs). It covers everything from the development of prompts to enhance LLM capabilities for various tasks, to understanding their limitations. This guide is a valuable resource for researchers and developers alike, providing advanced prompting techniques, model-specific guides, and tools for safer, more effective LLM interactions.
Visit website Record Added: 01 April 2024
Sebastian Raschka
Sebastian Raschka, a prominent figure in machine learning & AI research, has a rich history of contributing to open-source software and authoring several books on the subject. Previously an Assistant Professor at the University of Wisconsin-Madison, he resigned in 2023 to focus on his role at Lightning AI. His work explores the intersection of AI research, software development, and large language models. Raschka also runs the Ahead of AI magazine, sharing insights on deep learning and AI research.
Read More in Ahead of AI Visit Website Record Added: 01 April 2024
Nathan Lambert
Nathan Lambert is a dedicated machine learning researcher aiming to enhance the safety and efficacy of autonomous systems. His work spans the Allen Institute for AI, Hugging Face, DeepMind, and Facebook AI. Lambert completed his Ph.D. at UC Berkeley, contributing significantly to the field of AI under the guidance of Professor Kristofer Pister and Roberto Calandra. His journey in AI research is marked by a strong commitment to understanding and developing beneficial autonomous technologies.
Explore More Record Added: 01 April 2024
Papers With Code: The Latest in Machine Learning
Papers With Code is a groundbreaking resource dedicated to machine learning, offering access to papers, code, datasets, methods, and evaluation tables. It's a collaborative platform, enhanced by NLP and ML technologies, encouraging community contributions with its open CC-BY-SA license. Apart from its core domain, Papers With Code extends its reach into specialized portals for astronomy, physics, computer sciences, mathematics, and statistics, making it a central hub for academic and practical ML resources.
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Record Added: 01 April 2024
LMSYS Chatbot Arena Leaderboard
The LMSYS Chatbot Arena Leaderboard showcases the performance of large language models (LLMs) in a unique, competitive format. It utilizes a crowdsourced, anonymous battle system where models are evaluated based on their responses to user queries, with rankings determined by the Elo rating system. This initiative encourages community participation by allowing contributors to submit models and vote for the best answers, fostering a collaborative environment for advancing chatbot technology.
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Record Added: 01 April 2024
Hugging Face's Blog
Hugging Face's blog offers in-depth articles and discussions on the latest AI and machine learning developments. It includes contributions from industry experts, covering innovative technologies and research. Highlights include discussions on model merging techniques and efficient language model fine-tuning methods.
Special Mentions: - Merge Models with MLabonne - PEFT: Exploring Efficient Fine-Tuning
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Record Added: 01 April 2024
fast.ai—Making Neural Nets Uncool Again
fast.ai is on a mission to democratize deep learning. Through free courses, a robust software library, cutting-edge research, and a vibrant community, fast.ai aims to make deep learning accessible to everyone. Their slogan underscores a commitment to inclusivity, welcoming individuals with diverse backgrounds and expertise, even those using "uncool" languages or operating systems. fast.ai believes in breaking down barriers to entry in AI, ensuring that deep learning technology can benefit a wide array of users and applications.
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Record Added: 01 April 2024