​[[{“value”:” Data curation is critical in large-scale pretraining, significantly impacting language, vision, and multimodal modeling performance. Well-curated datasets can achieve strong performance with less data, but current pipelines often rely on manual curation, which is costly and hard to scale. Model-based data curation, leveraging training… Read More »Google DeepMind Introduces JEST: A New AI Training Method 13x Faster and 10X More Power Efficient Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Advances in hardware and software have enabled AI integration into low-power IoT devices, such as ultra-low-power microcontrollers. However, deploying complex ANNs on these devices requires techniques like quantization and pruning to meet their constraints. Additionally, edge AI models can face errors due to shifts… Read More »Efficient Continual Learning for Spiking Neural Networks with Time-Domain Compression Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Artificial Intelligence (AI) projects require powerful hardware to function efficiently, especially when dealing with large models and complex tasks. Traditional hardware often needs help to meet these demands, leading to high costs and slow processing times. This presents a challenge for developers and businesses… Read More »Google Cloud TPUs Now Available for HuggingFace users Niharika Singh Artificial Intelligence Category – MarkTechPost

​[[{“value”:” When given an unsafe prompt, like “Tell me how to build a bomb,” a well-trained large language model (LLM) should refuse to answer. This is usually achieved through Reinforcement Learning from Human Feedback (RLHF) and is crucial to make sure models are safe to… Read More »The Hidden Danger in AI Models: A Space Character’s Impact on Safety Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:”This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP-III) Workshop at NeurIPS. Large, pre-trained models are problematic to use in resource constrained applications. Fortunately, task-aware structured pruning methods offer a solution. These approaches reduce model size by dropping structural units like… Read More »Transfer Learning for Structured Pruning under Limited Task Data Apple Machine Learning Research

​We propose utilizing n-best reranking to enhance Sequence-Level Knowledge Distillation (Kim and Rush, 2016) where we extract pseudo-labels for student model’s training data from top n-best hypotheses and leverage a diverse set of models with different inductive biases, objective functions or architectures, including some publicly-available… Read More »Accurate Knowledge Distillation via N-best Reranking Apple Machine Learning Research

​[[{“value”:” As generative artificial intelligence (AI) inference becomes increasingly critical for businesses, customers are seeking ways to scale their generative AI operations or integrate generative AI models into existing workflows. Model optimization has emerged as a crucial step, allowing organizations to balance cost-effectiveness and responsiveness,… Read More »Achieve up to ~2x higher throughput while reducing costs by up to ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 2 James Wu AWS Machine Learning Blog

​[[{“value”:” Today, Amazon SageMaker announced a new inference optimization toolkit that helps you reduce the time it takes to optimize generative artificial intelligence (AI) models from months to hours, to achieve best-in-class performance for your use case. With this new capability, you can choose from… Read More »Achieve up to ~2x higher throughput while reducing costs by ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 1 Raghu Ramesha AWS Machine Learning Blog

​[[{“value”:” Anthropic Claude 3.5 Sonnet currently ranks at the top of S&P AI Benchmarks by Kensho, which assesses large language models (LLMs) for finance and business. Kensho is the AI Innovation Hub for S&P Global. Using Amazon Bedrock, Kensho was able to quickly run Anthropic… Read More »Anthropic Claude 3.5 Sonnet ranks number 1 for business and finance in S&P AI Benchmarks by Kensho Qingwei Li AWS Machine Learning Blog

​[[{“value”:” Retrieval-augmented generation (RAG) has emerged as a crucial technique for enhancing large language models (LLMs) to handle specialized knowledge, provide current information, and adapt to specific domains without altering model weights. However, the current RAG pipeline faces significant challenges. LLMs struggle with processing numerous… Read More »NVIDIA Introduces RankRAG: A Novel RAG Framework that Instruction-Tunes a Single LLM for the Dual Purposes of Top-k Context Ranking and Answer Generation in RAG Mohammad Asjad Artificial Intelligence Category – MarkTechPost