See section 4.1 for an approach and some references to others: https://
arxiv.org/abs/2109.09774
MACHINE LEARNING
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Section 4.1 approach and references on arxiv paper
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NVIDIA Partnership Accelerates AI Production and Data Science
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Exciting news for AI/ML leaders! Our collaboration with NVIDIA is moving AI into production faster and more cost-effectively to unleash data science. Check out this recent blog on our partnership's impact https://
blogs.nvidia.com/blog/2023/03/2
2/mlops-ai-platform-partners-move-ai-into-production/
… #AI #MLOps #NVIDIA #DominoDataLab #Innovation -
Efficient Differentially Private Training for Large-Scale Image Classification
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Today on the blog, read all about efficient differentially private (DP) training for large-scale models, focusing on image classification. Dive into our findings, state-of-the-art results, and grab the source code ↓ #DifferentialPrivacy https://
goo.gle/3Kf2FDr -
Ideas for Improving Language Models Without New Developments
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reply if you have other ideas about how language models could currently be improved without any new developments and I'll add them to the thread
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Language Models with Long-Term Memory via Embeddings and Search
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language models with long-term memories using embeddings and semantic search language models will be able to access some short convo you had a month prior
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Unlocking Language Models’ Potential Without New Development
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gpt-5 is not needed to 100x the potential these models have we could stop all language model development today and we still haven’t scratched the surface of their capabilities here are a few non-obvious ways language models can be improved without creating any new models:
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Parallel Language Models Orchestrated by Conductor LLM
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running language models in parallel with each one focused on a sub-task, all orchestrated by a conductor language model picture something like a massive tree of GPT models working on answering a single complex prompt
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Language Models with Dynamic Memory and Reflection Capabilities
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language models equipped with dynamic memory and the ability to reflect (e.g. Reflexion paper)
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Hectare: Type-Directed Program Synthesizer 100x Faster
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Want to get a program from just its type? Meet new type-directed synthesizer Hectare. Over 100x faster than state of the art on some problems — all with 10x less code. All thanks to a new kind of solver, ECTAs, solving a decades-old problem in synthesis: https://
bit.ly/3z2D5Ly -
SAP partnership makes machine learning accessible for business solutions
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ICYMI: We are thrilled about our strategic partnership with @SAPbtp
. This collaboration makes #ML accessible to users who need to create business-centric ML solutions that can significantly enhance business processes and drive differentiation. Read more: https://
bit.ly/40EkHUW