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Meet TinyLlama: An Open-Source Small-Scale Language Model that Pretrain a 1.1B Llama Model on 3 Trillion Tokens
Language models are crucial in natural language processing, trending towards larger, intricate models to process human-like text. A challenge is balancing computational demand and performance. The introduction of TinyLlama, a compact language model with 1.1 billion parameters, addresses this by efficiently using resources while maintaining high performance. It sets a new precedent for inclusive NLP…
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Mobile ALOHA: Low-cost bimanual mobile robot housekeeper
Stanford University researchers unveiled Mobile ALOHA, a low-cost, bimanual mobile robot capable of performing household tasks. The robot, an improved version of static ALOHA, uses an imitation learning process and Action Chunk with Transformers algorithm to learn new skills. Mobile ALOHA is affordable, open-source, and run by off-the-shelf hardware, making it a promising advancement in…
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Generative AI is a Gamble Enterprises Should Take in 2024
The article emphasizes the challenges and benefits of adopting generative AI in enterprises. It warns about the inaccuracies and potential risks associated with large language models (LLMs) due to hallucinations, but also highlights the necessity and transformative potential of leveraging generative AI for productivity and strategic advantage. The recommendations include prioritizing data foundation, building an…
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Merge Large Language Models with mergekit
The text discusses different methods of merging large language models using mergekit and how to use them to create new combined models without requiring a GPU. It provides examples of configurations for four merging methods: SLERP, TIES, DARE, and Passthrough, and details the steps for implementing each method. The tutorial also explains how to use…
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Machine Learning in Business: 5 things a Data Science course won’t teach you
The author highlights key aspects of Applied Machine Learning often overlooked in formal Data Science education. These include thoughtful target selection, dealing with imbalanced data, using real-life testing, meaningful performance metrics, and reconsidering the importance of scores. The insights are aimed at helping junior and mid-level data scientists enhance their career. [50 words]
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Researchers map the oceans to uncover ‘dark vessels’ and offshore structures
Researchers used neural networks to analyze satellite and radar images and found that a large portion of the world’s fishing and energy vessels operate as “dark vessels,” not publicly sharing their location. They developed deep learning models to classify vessels and offshore structures, revealing insights into global maritime activities and concerns about illegal fishing.
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Two influential journalists file lawsuit against OpenAI and Microsoft
Journalists Nicholas Gage and Nicholas Basbanes have filed a copyright lawsuit against OpenAI and Microsoft, claiming their literary works were used without authorization to train ChatGPT. The lawsuit follows a similar case by The New York Times. It alleges that OpenAI used pirated e-book datasets and that its ChatGPT-4 model reproduced copyrighted text. This aligns…
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The upcoming World Conference on Data Science & Statistics 2024
The World Conference on Data Science & Statistics 2024, taking place from June 17th to 19th in Amsterdam, is a diverse event uniting industry leaders, academics, and innovators in data science, AI, and related technologies. With 60+ sessions covering key topics like AI’s impact on data science and public policy, the conference promises valuable insights…
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What is AI Hallucination? Is It Always a Bad Thing?
AI hallucinations, seen in generative AI like ChatGPT and Google Bard, occur when large language models deviate from accurate information due to flawed training data or generation methods. The consequences include misinformation, bias amplification, and privacy issues. However, with responsible development, AI hallucinations can offer benefits like creative potential, improved data interpretation, and enhanced digital…
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Researchers from UT Austin and Meta Developed SteinDreamer: A Breakthrough in Text-to-3D Asset Synthesis Using Stein Score Distillation for Superior Visual Quality and Accelerated Convergence
Recent advancements in text-to-3D generation, led by diffusion models, have spurred interest in automating 3D asset creation for virtual reality, movies, and gaming. Challenges in 3D synthesis are being addressed through the development of SteinDreamer, which integrates Stein Score Distillation to improve visual quality and convergence speed. This breakthrough represents a significant advancement in text-to-3D…