(2/n) MediSwift capitalizes on our most recent innovations in sparsity, inducing up to 75% unstructured weight sparsity during in-domain pre-training on biomedical texts. This results in a 2-2.5x reduction in the required training FLOPs. Blog: https://
cerebras.net/blog/sparsity-
made-easy-introducing-the-cerebras-pytorch-sparsity-library
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MACHINE LEARNING
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MediSwift Achieves 75% Sparsity, Reduces Training FLOPs by 2.5x
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Dense Fine-Tuning Boosts MediSwift Biomedical Task Performance
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(3/n) Dense fine-tuning and soft prompting enhance the performance of sparsely pre-trained MediSwift models on biomedical tasks (e.g., PubMedQA). This ensures high accuracy, thereby improving the efficiency-accuracy Pareto frontier. Pubmed QA leaderboard: https://
pubmedqa.github.io -
MediSwift: Sparse Biomedical Language Models Reduce Computational Costs
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(1/n) Introducing MediSwift, the first suite of biomedical language models that employ sparse pre-training techniques to significantly reduce computational costs, while outperforming existing models up to 7B parameters on benchmark tasks such as PubMedQA. Paper:
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LLMs Outperform Human Experts in Neuroscience Prediction
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Large language models surpass human experts in predicting neuroscience results Luo et al.: https://
arxiv.org/abs/2403.03230 #ArtificialIntelligence #DeepLearning #MachineLearning -
Enterprise Machine Learning: Complete MLOps Platform Capabilities
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Enterprise #MachineLearning requires all this:
1. Data sources
2. Data pipelines
3. Feature stores
4. Model training
5. Model evaluation
6. Model deployment
7. Model monitoring
8. Predictions API
9. #MLOps Guess what?… @AbacusAI platform provides all this capability for you -
AI-Driven Supply Chain Sustainability Strategies for Short-Term Impact
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Short-Term Strategies For Making Your Supply Chain More Sustainable https://
forbes.com/sites/forbeste
chcouncil/2024/02/09/short-term-strategies-for-making-your-supply-chain-more-sustainable/?utm_source=dlvr.it&utm_medium=twitter
… #Innovation #ArtificialIntelligence #MachineLearning -
Choosing the Right Vector Embedding Model for Generative AI
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Vector embedding models are a critical component of many #generativeAI applications. In our latest blog, learn all the nuts and bolts of embedding models, and how to make the best choice for your next #AI use case. Nick Volynets, Senior Data Engineer https://
datarobot.com/blog/choosing-
the-right-vector-embedding-model-for-your-generative-ai-use-case/
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Wealthsimple reduces ML model deployment time to 15 minutes
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DYK – By using Triton Inference Server, @Wealthsimple has reduced their delivery time from months to 15 mins, allowing their data scientists to focus on critical projects. Find out > https://
nvda.ws/3v1ePLl By using Triton Inference Server, what results did Wealthsimple see? -
Alteryx AiDIN Launches in Saudi Arabia with AI and ML
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تم تدشين Alteryx AiDIN في المملكة العربية السعودية! نقوم بإحداث ثورة في عالم الأعمال من خلال تمكين أصحاب الأعمال بقوة الذكاء الاصطناعي والتعلم الآلي والذكاء الاصطناعي التوليدي. للمزيد من المعلومات: https://
alteryx.com/aidin #LEAP24 -
Large Multimodal Datasets: Unsung Heroes Behind AI Breakthroughs
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The unsung hero behind many breakthroughs (including Stable Diffusion, and GPT-4) is large multimodal datasets. Last October, Assistant Professor Ludwig Schmidt from the University of Washington introduced DataComp, an approach to enhance data quality.