NEWS IN BRIEF: AI/ML FRESH UPDATES

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Power Up: The ABCs of Transformation

Meta and Waymo introduce Transfusion model combining transformer and diffusion for multi-modal prediction. Transfusion model uses bi-directional transformer attention for image tokens and pre-training tasks for text and...

Building a Smart QA Model with HuggingFace

HuggingFace offers a vast library of pretrained language and image models for natural language tasks. Despite some errors, the QA system showcases the simplicity and effectiveness of using the pipeline...

Unlocking Self-Attention: A Code Breakdown

Large language models like GPT and BERT rely on the Transformer architecture and self-attention mechanism to create contextually rich embeddings, revolutionizing NLP. Static embeddings like word2vec fall short in capturing contextual information, highlighting the importance of dynamic embeddings in language...

Optimizing Small Transformers for Text Classification

Microsoft’s Phi-3 creates smaller, optimized text classification models, outperforming larger models like GPT-3. Synthetic data generation with Phi-3 via Ollama improves AI workflows for specific use cases, offering insights into clickbait versus factual content...

BERT Demystified: A Complete Guide with Code

BERT, developed by Google AI Language, is a groundbreaking Large Language Model for Natural Language Processing. Its architecture and focus on Natural Language Understanding have reshaped the NLP landscape, inspiring models like RoBERTa and...

Unleashing the Power of Language Models: Automatic Summarization Techniques

Summarization is essential in our data-driven world, saving time and improving decision-making. It has various applications, including news aggregation, legal document summarization, and financial analysis. With advancements in NLP and AI, techniques like extractive and abstractive summarization are becoming more accessible and...