Interesting Engineering on MSN

New adaptive system lets robots replicate human touch with far less training data

Japanese researchers develop an adaptive robot motion system that enables human-like grasping using minimal training data.
Tech Xplore on MSN

Adaptive motion system helps robots achieve human-like dexterity with minimal data

Despite rapid robotic automation advancements, most systems struggle to adapt their pre-trained movements to dynamic ...
AlphaGalileo

Bringing Human Dexterity to Robots by Combining Human Motion and Tactile Sensation

Researchers develop an adaptive motion system that allows robots to generate human-like movements with minimal data ...
Open Access GovernmentOpinion

Navigating uncertainty: How advanced forecasting builds resilient institutions

Dr Yves R. Sagaert emphasizes the importance of organizations shifting away from traditional static budgeting, which relies ...
Deep Learning with Yacine on MSN

Linear regression from scratch in C++

Learn how to implement linear regression from scratch in C++. A beginner-friendly guide to machine learning basics.
Deep Learning with Yacine on MSN

Building multivariate linear regression in C++ — no libraries, just code

Want to understand how multivariate linear regression really works under the hood? In this video, we build it from scratch in C++—no machine learning libraries, just raw code and linear algebra. Ideal ...

First-time use of AI for genetic circuit design demonstrated in a human cell line

There are hundreds of cell types in the human body, each with a specific role spelled out in their DNA. In theory, all it ...
Tech Xplore on MSN

First-ever dataset to improve English-to-Malayalam machine translation fills critical gap for low-resource languages

The world's first dataset aimed at improving the quality of English-to-Malayalam machine translation—a long-overlooked ...
Scientific Research Publishing

Cross-Population Transfer Learning for Antidepressant Treatment Response Prediction: A SHAP-Based Explainability Approach Using Synthetic Multi-Ethnic Data ()

de Filippis, R. and Al Foysal, A. (2026) Cross-Population Transfer Learning for Antidepressant Treatment Response Prediction: A SHAP-Based Explainability Approach Using Synthetic Multi-Ethnic Data.