Opportunities and challenges in data-driven chemical engineering thermodynamics, statistical mechanics and molecular simulation are discussed, and new possibilities offered by machine learning in ...

Benefiting from the significant advances in machine learning (ML), ML-based data-driven methods allow structural design to be guided by existing data, rather than relying on the experience, intuition, ...

Constructing the truly modern data application, capable of driving data-informed decisions across the organization, requires a lot of effort. To dramatically improve productivity, operational ...

Programmable material systems are emerging architectural structures but the co-design of structure, material, and external stimuli present grand challenges. A team with Northwestern Engineering’s Wei ...

Design thinking is critical for developing data-driven business tools that surpass end-user expectations. Here's how to apply the five stages of design thinking in your data science projects. What is ...

A robust data strategy is paramount for harnessing generative AI (GenAI), which is poised to significantly elevate global GDP by $7 trillion and enhance productivity within the next decade. In my ...

Cloud-based data warehouse company Snowflake is adding more large language model capabilities and services related to generative AI, just months after releasing similar services in June. At its annual ...

AI talent is in high demand; AI reveals brain oscillations for memory and disease; AI may allow us to talk to whales; AI can help plan meals and other tasks; an AI platform for dentistry just raised ...

In the rapidly evolving landscape of the finance industry, the advent of synthetic data stands out as a groundbreaking development to revolutionize the way financial institutions harness data for ...

How organizations can do more with their data to realize business goals and achieve a sustainable competitive advantage. In partnership withWNS Triange In 2006, British mathematician Clive Humby said, ...