NEWS IN BRIEF: AI/ML FRESH UPDATES

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Unleashing the Power of Dynamic Execution

Dynamic execution in AI tasks can optimize performance by distinguishing between Hard and Easy problems. By identifying and addressing the complexity of data points, accuracy can be maintained while saving computational resources.

Streamlining AI Models

AI models, like LLaMA 3.1, require large GPU memory, hindering accessibility on consumer devices. Research on quantization offers a solution to reduce model size and enable local AI model running.

AI Translating Dutch Books to English

VBK, the largest Dutch publisher, to trial AI for translating books into English, acquired by Simon & Schuster. AI to assist in translation of commercial fiction, Vanessa van Hofwegen confirms.

Revolutionizing Contracts with GraphRAG

Summary: Introducing a new GraphRAG approach for efficient commercial contract data extraction and Q&A agent building. Focus on targeted information extraction and knowledge graph organization enhances accuracy and performance, making it suitable for handling complex legal questions.

Revealing Humanity: Insights from AI Images

Generative AI is reshaping the art world, with humans still playing a crucial role in creating AI art. Rachel Ossip explores the intersection of technology and creativity in the evolving art landscape.

Cracking the Code: AI Chatbot Trustworthiness

AI-generated summaries by Google and Microsoft are being used in search engines, but the accuracy and authority of the information are questioned. The debate over the carcinogenic properties of aspartame highlights the potential pitfalls of relying on AI for contentious topics.

Securing Large Language Models

LLMs can now be run locally for enhanced privacy and control over model settings, with various sizes available. Quantization reduces memory usage, while local implementations prove cost-effective compared to cloud-based solutions.

Revolutionizing LLM Assessments

Evaluating AI-generated outputs of large language models is crucial for building robust applications, with supervised and unsupervised methods available. Self-evaluation and iterative self-reflection can improve the quality of generative models, reducing the need for human involvement in evaluations.

Greening Clinical Trials with AWS

Decentralized clinical trials reduce costs and environmental impact, using technologies like wearable devices and telemedicine. AWS enables fast implementation, supporting virtual trials and personalized patient engagement for more sustainable clinical research practices.