Tag: Machine learning

AISoftwareStartups

Sumble Launches with $38.5M Funding to Revolutionize Sales Intelligence Through AI Context

Sumble, founded by Kaggle creators, has secured $38.5 million in funding to bring AI-powered context to sales intelligence. The startup already counts Snowflake and Figma among its enterprise clients and has seen rapid internal adoption within organizations.

AI-Powered Sales Intelligence Startup Emerges with Major Funding

Sumble, a San Francisco-based sales intelligence startup, has reportedly emerged from stealth mode with $38.5 million in funding, according to TechCrunch reports. Founded by Anthony Goldbloom and Ben Hamner, the creators of data science community Kaggle, the company aims to transform how sales teams gather and utilize prospect information through AI-powered context.

by Desai Priyanka
ChemistryInnovationSoftware

New Open-Source Platform Aims to Standardize AI-Driven Polymer Discovery

Researchers have launched an open-source platform designed to overcome fundamental barriers in polymer informatics. The ecosystem reportedly addresses dataset incompatibility and featurization inconsistencies that have hampered AI-driven polymer discovery efforts.

Addressing the Polymer Informatics Crisis

Scientific reports indicate a growing crisis in polymer informatics where machine learning models trained on different datasets produce wildly varying results. According to researchers behind a new open-source platform called PolyMetriX, cross-testing existing models revealed mean absolute errors ranging from 13.79 to 214.75 Kelvin when predicting glass transition temperatures – a critical polymer property. This substantial variation reportedly stems from incompatible datasets and inconsistent featurization methods across the research community.

by Desai Priyanka
AIResearch

AI Framework Predicts Plant Invasion Risks Before Species Spread

Researchers have created an artificial intelligence system that can forecast which plant species might become invasive before they arrive in new territories. The interdisciplinary approach adapts astrophysics algorithms to ecological prediction, offering a powerful complement to traditional risk assessment methods.

Breaking New Ground in Ecological Forecasting

As global connectivity increases the movement of plant species across regions, scientists are developing advanced methods to predict which introductions might threaten native ecosystems. According to reports from the University of Connecticut, an interdisciplinary team has created a machine learning framework that can identify potentially problematic plants before they establish in new environments.

by Alvarez Jordan