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Analytics Insight

Google's Data Science Agent: Reshaping Data Analysis or Threat to Jobs?

Google's push into artificial intelligence keeps on growing, and 2025 brought a notable entry in the Data Science Agent. Introduced as part of Google's overall ...

Challenging negative data helps AI models better identify effective antibodies

Imagine you are developing antibodies—drugs precisely aimed at a target, for example a viral protein or onco-marker. You test a series of antibodies and find that some work, while others do not.

Cai Cai's Machine Learning Sklearn Class: Understandable from Scratch, from Model Invocation to Practical Implementation

The design of sklearn follows the "Swiss Army Knife" principle, integrating six core modules: Data Preprocessing: Similar to ...

A key type of AI training data is running out. Googlers have a bold new idea to fix that.

Google DeepMind researchers have a new way to take toxic data and clean it for AI training. It could prove to be a powerful ...
Nature

Synthetic data can benefit medical research — but risks must be recognized

Artificially generated data can help to train AI models when real data are scant, but more focus is needed on validating the ...

OpenAI foresees millions of AI agents 'somewhere in the cloud' in just a few years — with human supervision

OpenAI launches GPT-5 Codex, an AI agent that the company said could run for hours on complex software projects.
EMBO Press

Pre-analytical drivers of bias in bead-enriched plasma proteomics

Bead-based plasma proteomics workflows boost proteome coverage but are vulnerable to cellular contamination, highlighting the ...

A Must-Read for AI Product Managers: The Complete Process of Building RAG Datasets to Support High-Quality AI Applications

With the rapid development of artificial intelligencetechnology, RAG (Retrieval-Augmented Generation) architecture is becoming the core technology that connects external knowledge with large models. A ...