Top 10 Machine Learning Resources and Updates 06/21/2024
The fastest way to get up to speed on ML fundamentals, Meta releases new models, NVIDIA releases open models, Google generates audio for video, and more
Below are the machine learning resources and updates from the past few days you don't want to miss. If you want all the ML updates follow me there. If you want more than just the top 10 resources, you can get all the updates and resources each week supporting Society's Backend for just $1/mo.
The Fastest Way to Get Up to Speed on Machine Learning Fundamentals for Free
Understanding Kolmogorov–Arnold Networks: Possible Successors to MLPs? [Breakdowns]
NVIDIA Releases Open Synthetic Data Generation Pipeline for Training Large Language Models
OpenAI appoints Retired U.S. Army General Paul M. Nakasone to Board of Directors
Made With ML
Made With ML by madewithml.com helps over 30,000 developers learn to responsibly develop, deploy, and maintain machine learning (ML) applications. It offers foundational ML training with clear explanations, clean code, and visualizations. The MLOps course teaches combining ML with software engineering for production applications. The instructor, with 7 years of experience at Apple, Ciitizen, and his own startup, shares knowledge from working with various industries. The aim is to make ML accessible and responsible, accelerating progress in the field. The community's positive feedback highlights its impact.
🥇Top ML Papers of the Week
The article highlights the top machine learning papers of the week, focusing on advancements in language models and optimization algorithms. Key papers include Nemotron-4 340B, which competes with GPT-4 and releases preference data, and a study on LLM-driven preference optimization without human intervention. Other notable works include frameworks like SelfGoal for breaking down goals into subgoals and Mixture-of-Agents for leveraging multiple LLM strengths. Additionally, innovations like Self-Tuning for knowledge acquisition and Sketching as a Visual Chain of Thought for multimodal reasoning are discussed. These papers are important as they push the boundaries of AI capabilities and performance.
The Fastest Way to Get Up to Speed on Machine Learning Fundamentals for Free
Logan Thorneloe emphasizes the importance of understanding machine learning (ML) as it impacts everyone. He created the ML Road Map-Turbo, a streamlined guide to quickly learn ML fundamentals for free. The guide includes necessary prerequisites, a fundamental course, advanced resources, community learning sources, and free compute options. It aims to make ML education accessible and engaging. Feedback and support for the road map are encouraged. This resource is crucial for anyone looking to quickly get up to speed with machine learning.
Meta just released 4 models today
Meta released four new models: Meta Chameleon (7B & 34B language models), Meta Multi-Token Prediction LLM, Meta JASCO (text-to-music models), and Meta AudioSeal (audio watermarking model). These innovations are based on a groundbreaking paper from April 2024, which proposes training language models to predict multiple future words at once. This new method makes models more efficient and faster, solving more problems and speeding up inference. Multi-token prediction uses a shared transformer trunk and multiple output heads, reducing memory usage and enhancing performance. The new approach excels in tasks like code generation and natural language processing. It offers better results in both generative and standard benchmarks.
Understanding Kolmogorov–Arnold Networks: Possible Successors to MLPs? [Breakdowns]
The article discusses Kolmogorov-Arnold Networks (KANs) as potential successors to Multi-Layer Perceptrons (MLPs) in deep learning. Unlike MLPs, KANs use learnable activation functions on edges, improving accuracy and interpretability, especially for functions with sparse structures common in scientific applications. KANs utilize splines for modeling complex relationships while maintaining local control. Despite their advantages, such as reduced catastrophic forgetting and better scaling, KANs face practical limitations that could hinder their broader adoption. The article emphasizes KANs' potential for accuracy and interpretability, suggesting they could be foundational models for AI in science. Overall, KANs offer a new approach to overcoming MLPs' limitations, potentially providing valuable insights.
Open Interpreter’s Local III is out today
Open Interpreter’s Local III is released today. It allows computer-controlling agents to work offline. The new version sets up fast, local language models. A free inference endpoint is available. They're also training their own model. This update marks their biggest progress yet.
Data Is Better Together: A Look Back and Forward
The "Data Is Better Together" initiative by Hugging Face and Argilla aims to empower the open-source community to create impactful datasets. The project has seen significant community involvement, particularly in the prompt ranking and Multilingual Prompt Evaluation Project (MPEP), which translates high-quality prompts into multiple languages. Community efforts also include building datasets and tools, with successful translations in Dutch, Russian, and Spanish. The initiative highlights the need for diverse and inclusive benchmarks beyond English. The community is encouraged to contribute through various guides and tools provided. Join the Hugging Face Discord to participate and share your contributions.
NVIDIA Releases Open Synthetic Data Generation Pipeline for Training Large Language Models
NVIDIA has released Nemotron-4 340B, a family of open models for generating synthetic data to train large language models (LLMs). These models help create high-quality training data, which is crucial for improving LLM performance and accuracy. Nemotron-4 340B includes base, instruct, and reward models, optimized for use with NVIDIA NeMo and TensorRT-LLM libraries. Developers can download these models from Hugging Face and soon from NVIDIA's AI platform. This release is important because it provides a scalable, cost-effective way to generate synthetic data across various industries, enhancing AI development and customization.
Generating audio for video
Google DeepMind's blog introduces their new video-to-audio (V2A) technology. V2A generates realistic soundtracks for silent videos using video pixels and text prompts. It can create various soundscapes, like music, sound effects, and dialogue, for different types of footage. The system uses a diffusion model to synchronize audio with video, producing high-quality results. This technology aims to enhance creative control and streamline the process of adding sound to generated movies. Further research and safety assessments are ongoing to ensure its responsible use.
OpenAI appoints Retired U.S. Army General Paul M. Nakasone to Board of Directors
OpenAI has appointed Retired U.S. Army General Paul M. Nakasone to its Board of Directors. Nakasone is a cybersecurity expert and will join the Board's Safety and Security Committee. His appointment highlights OpenAI's focus on security as AI technology advances. Nakasone's experience will help protect OpenAI's systems and enhance their cybersecurity measures. He will also help explore how AI can improve cybersecurity for various institutions. His expertise aligns with OpenAI's mission to ensure artificial general intelligence benefits everyone.
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