NEWS IN BRIEF: AI/ML FRESH UPDATES

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Accelerating ML with Amazon SageMaker: Axfood's Success Story

Axfood AB, Sweden's second largest food retailer, partnered with AWS to prototype a new MLOps best practice for efficient ML models. They improved scalability and efficiency by collaborating with AWS experts and using Amazon SageMaker, focusing on forecasting sales for fruits and vegetables to optimize in-store stock levels and minimize food waste.

'AI Streamlining Robotic Warehouse Operations'

MIT researchers developed a deep-learning model to decongest robotic warehouses, improving efficiency by nearly four times. Their innovative approach could revolutionize complex planning tasks beyond warehouse operations.

Mastering Prompting for LLMs

Exciting developments in Large Language Models (LLMs) have revolutionized communication, prompting is key to harnessing their in-context learning abilities. Companies like Prompting Llama and GPT-3.5 are leading the way in innovative prompting strategies for LLMs.

Decoding Machine Learning Failures

Machine learning pitfalls: overfitting, misleading data, hidden variables. Examples include failed Covid prediction models and water quality system. REFORMS checklist introduced to prevent errors in ML-based science.

AI Revolutionizing Image Text Editing

AI models like STEFANN, SRNet, TextDiffuser, and AnyText are revolutionizing Scene Text Editing, making it easier to modify text in images while preserving aesthetics. Companies like Alibaba and Baidu are actively researching and implementing STE for practical applications like enhancing text recognition systems.

ML Deployment: From Model to Cloud in Python

Article highlights deploying ML models in the cloud, combining CS and DS fields, and overcoming memory limitations in model deployment. Key technologies include Detectron2, Django, Docker, Celery, Heroku, and AWS S3.

Unlocking the Power of Direct Preference Optimization

The Direct Preference Optimization paper introduces a new way to fine-tune foundation models, leading to impressive performance gains with fewer parameters. The method replaces the need for a separate reward model, revolutionizing the way LLMs are optimized.