AI Agents: Proactive Assistance for R&D and QC
AI Agents of assistance for R&D and Quality Assurance, reshaping labs: analyzing, deciding, suggesting — to speed up innovation and quality. What happens when they power R&D and QA? Find out.
The EU Green Deal and Digitalization
To become climate-neutral by 2050, Europe must rethink how materials are developed. AI agents powered by Material Intelligence help unite innovation speed, sustainability, and compliance.
Machine Learning – How it works and what it can do
What if your material data could tell you exactly which formulation works best — before you even run a test?
This article shows how R&D and QA teams are using machine learning to predict performance, reduce trial-and-error, and accelerate development with a clear five-step approach.
AI in the plastics industry
In an interview, Charles Jouanique discusses the utility of artificial intelligence in the polymer processing industry – from its potential, and the challenges associated with its implementation.
How AI Unlocks Hidden Knowledge in Documents
Engineers and researchers spend hours searching through PDFs and reports. AI-powered analysis retrieves insights instantly, boosting efficiency and enabling data-driven decisions.
LIMS-Forum Review
What does the future of laboratory digitalization look like? How does the current excitement surrounding ChatGPT and artificial intelligence influence it? And when will it finally be possible to connect all devices cost-effectively? For those eager to get up to speed on these digital topics, the LIMS Forum 2024 was the place to be.
AI in the Laboratory – Part IV
Effective communication with suppliers is essential for maintaining high-quality standards and resolving issues promptly. This scenario demonstrates how AI can present data in a clear and impactful manner.
AI in the Laboratory – Part III
Ensuring the quality of raw materials from suppliers is critical for maintaining product standards. This scenario highlights how AI can assist in comparing different material suppliers, providing manufacturers with the insights needed to make informed decisions.
AI in the Laboratory – Part II
In this scenario, we show how an AI-powered digital assistent is able to handle large data sets. AI has the potential to support laboratories by conducting complex data analyses and finds correlation that would otherwise remain hidden.
AI in the Laboratory – Part I
This is the first part in a series of blog posts on the practical use of Artificial Intelligence in the testing laboratory – using polymer testing as an example. Here we explain the issue of fragmented data and show how this can be resolved.
