Block Sparse Matrices for Smaller and Faster Language Models
Hugging Face details block sparse matrix techniques to reduce LLM size and accelerate inference, potentially lowering operational costs.
Hugging Face Blog·10 Sept 2020
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Hugging Face details block sparse matrix techniques to reduce LLM size and accelerate inference, potentially lowering operational costs.
OpenAI's Frontier Lab is researching generative language models for automated theorem proving, indicating a long-term AI capability focus.
Eugene Yan outlines testing methodologies for machine learning systems, covering implementation, learned behavior, and performance validation.
OpenAI applied reinforcement learning from human feedback (RLHF) to train language models for improved summarization tasks, claiming better performance.
Article discusses trade-offs between regex and ML for PDF field parsing, focusing on accuracy, maintenance, and data volume.
AWS details architectural patterns for building secure, compliant self-service cloud environments for highly regulated financial institutions.
OpenAI Scholars presented their final projects, concluding a five-month research program focused on various AI topics.
Spark+AI Summit 2020 notes detail production applications and frameworks of Spark across various enterprises.
Hugging Face blog post discusses 'Reformer' architecture for efficient, long-context language modeling using LSH attention and reversible layers.
Eugene Yan's 2020 Spark+AI Summit notes cover application-agnostic talks, providing insights into general AI/ML best practices from four years ago.
OpenAI co-organized NeurIPS 2020 competitions using Procgen Benchmark and MineRL to advance reinforcement learning research.
OpenAI research paper highlights large language models' ability to learn new tasks from few examples, reducing the need for extensive fine-tuning.
Eugene Yan provides practical tips for maintaining machine learning models in production, covering monitoring, retraining, and incident response.
The article outlines common operational challenges encountered after machine learning models are deployed to production environments.
OpenAI analysis claims algorithmic progress has reduced compute needed for ImageNet classification by a factor of 2 every 16 months since 2012.
OpenAI co-authored a multi-stakeholder report outlining 10 mechanisms to improve the verifiability of claims about AI systems' safety, security, and fairness.
OpenAI launched Microscope, a tool visualizing layers and neurons of eight vision models to aid interpretability research.
Hugging Face blog details process for training a new language model from scratch using Transformers and Tokenizers libraries.
OpenAI announced a standardization of its deep learning framework on PyTorch, consolidating away from other frameworks.
OpenAI published 'Scaling Laws for Neural Language Models' in 2020, demonstrating predictable performance gains with increased compute, data, and parameters.
OpenAI released the largest 1.5B parameter version of GPT-2 with code and model weights, completing its staged release plan.
OpenAI trained neural networks via reinforcement learning and Automatic Domain Randomization (ADR) to solve a Rubik's Cube with a robot hand in simulation.
OpenAI opened applications for its 2020 Scholars program, a 6-month, full-time research scholarship for underrepresented groups in AI.
A meetup titled "Applying ML to Healthcare" featured in-depth sharing on putting machine learning systems into production.
OpenAI fine-tuned GPT-2 with human feedback, observing labeler preferences for summarization favored copying input text verbatim, even if unintended.
OpenAI developed a new metric, UAR, to assess neural network classifier robustness against adversarial attacks not seen during training.
OpenAI fully released the 774M parameter GPT-2 model after staged releases and research into misuse potential, alongside a model-sharing legal agreement.
Microsoft invested $1 billion in OpenAI to develop AGI, partnering on Azure AI supercomputing and making Azure OpenAI's exclusive cloud provider.
OpenAI proposes four strategies for industry cooperation on AI safety norms, citing competitive pressures as a potential collective action problem.
OpenAI hosted its first Robotics Symposium in April 2019, bringing together researchers to discuss frontier robotics development.
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