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Together AI Unveils Revolutionary Inference Stack: Setting New Standards in Generative AI Performance

Marktechpost

Together AI has unveiled a groundbreaking advancement in AI inference with its new inference stack. This stack, which boasts a decoding throughput four times faster than the open-source vLLM, surpasses leading commercial solutions like Amazon Bedrock, Azure AI, Fireworks, and Octo AI by 1.3x

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Mistral AI Introduces Les Ministraux: Ministral 3B and Ministral 8B- Revolutionizing On-Device AI

Marktechpost

High-performance AI models that can run at the edge and on personal devices are needed to overcome the limitations of existing large-scale models. Introducing Ministral 3B and Ministral 8B Mistral AI recently unveiled two groundbreaking models aimed at transforming on-device and edge AI capabilities—Ministral 3B and Ministral 8B.

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Meta AI Releases Meta’s Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Marktechpost

While AI has emerged as a powerful tool for materials discovery, the lack of publicly available data and open, pre-trained models has become a major bottleneck. The introduction of the OMat24 dataset and the corresponding models represents a significant leap forward in AI-assisted materials science.

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MEGA-Bench: A Comprehensive AI Benchmark that Scales Multimodal Evaluation to Over 500 Real-World Tasks at a Manageable Inference Cost

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Don’t Forget to join our 50k+ ML SubReddit. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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This AI Paper from Meta AI Highlights the Risks of Using Synthetic Data to Train Large Language Models

Marktechpost

The results are particularly concerning given the increasing reliance on synthetic data in large-scale AI systems. Don’t Forget to join our 50k+ ML SubReddit. Although there are situations where increasing model size may slightly mitigate the collapse, it does not entirely prevent the problem.

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Stanford Researchers Propose LoLCATS: A Cutting Edge AI Method for Efficient LLM Linearization

Marktechpost

Researchers from Stanford University, Together AI, California Institute of Technology, and MIT introduced LoLCATS (Low-rank Linear Conversion via Attention Transfer). Don’t Forget to join our 50k+ ML SubReddit. If you like our work, you will love our newsletter.

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This AI Paper Propsoes an AI Framework to Prevent Adversarial Attacks on Mobile Vehicle-to-Microgrid Services

Marktechpost

AI is crucial in optimizing energy distribution, forecasting demand, and managing real-time interactions between vehicles and the microgrid. In conclusion, the proposed AI-based countermeasure utilizing GANs offers a promising approach to enhance the security of Mobile V2M services against adversarial attacks.