NEWS IN BRIEF: AI/ML FRESH UPDATES

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Eric Evans Honored with DOD Medal

Eric Evans receives DoD Medal for Distinguished Public Service for his leadership at MIT Lincoln Laboratory and Defense Science Board. Evans' impact on national security and defense innovation recognized by U.S. Department of Defense.

Unleashing the Power of NVIDIA AI Workbench

NVIDIA AI Workbench simplifies generative AI development with RAG applications, enabling customized models and seamless collaboration. The free tool streamlines setup and offers ease of collaboration across platforms for data scientists and developers.

Master Regression with LightGBM

The article "Regression Using LightGBM" in Microsoft Visual Studio Magazine explores using LightGBM for regression tasks. LightGBM, an open-source tree-based system introduced in 2017, can handle multi-class classification, binary classification, regression, and ranking.

Streamline Amazon Bedrock with a Slack Gateway

Unlock the power of generative AI in Slack with Amazon Bedrock, streamlining collaboration and boosting productivity. Create a seamless workflow with leading AI models from companies like AI21 Labs and Meta, enhancing efficiency and creativity.

The Atomic Human: A Terminator Returns

AI expert Neil Lawrence's new book, The Atomic Human, delves into AI myths, including Terminator references. The study challenges techbro arrogance, contrasting biblical creation with AI consciousness.

AI Pioneer Sutskever Aims for Superintelligence

Former OpenAI Chief Scientist Ilya Sutskever launches Safe Superintelligence, Inc. (SSI) to develop advanced AI surpassing human intelligence. Sutskever aims for revolutionary breakthroughs with a small team including former OpenAI members and an AI investor from Apple.

Mastering EDA: 11 Essential Steps

Summary: Communicate with stakeholders to set analysis goals, align expectations, and develop research questions for effective exploratory data analysis. Identify knowns and unknowns to build a strong foundation for your analysis process.

Enhancing Reliability: Evaluating Retrieval Augmented Generation with Amazon Bedrock

Retrieval Augmented Generation (RAG) enhances large language models by incorporating external knowledge sources, improving performance across various applications. Challenges include retrieving relevant knowledge, avoiding hallucinations, and integrating retrieval and generation components efficiently. Evaluation of RAG applications is crucial for trustworthiness, performance improvement, and r...