Foundational Models AI News & Updates

FieldAI Secures $405M to Develop Physics-Based Universal Robot Brains for Cross-Platform Embodied AI

FieldAI raised $405 million to develop "foundational embodied AI models" - universal robot brains that can work across different robot types from humanoids to self-driving cars. The company's approach integrates physics into AI models to help robots safely adapt to new environments while managing risk, addressing traditional robotics limitations in generalization and safety.

Latent Labs Releases State-of-the-Art Web-Based AI Model for Novel Protein Design

Latent Labs launched LatentX, a web-based AI model that enables users to design entirely new proteins using natural language, achieving state-of-the-art performance in lab testing. Unlike AlphaFold which predicts existing protein structures, LatentX creates novel molecular designs including nanobodies and antibodies with precise atomic structures. The company plans to license the technology to academic institutions, biotech startups, and pharmaceutical companies to accelerate therapeutic development.

RLWRLD Secures $14.8M to Develop Foundational AI Model for Advanced Robotics

South Korean startup RLWRLD has raised $14.8 million in seed funding to develop a foundational AI model specifically for robotics by combining large language models with traditional robotics software. The company aims to enable robots to perform precise tasks, handle delicate materials, and adapt to changing conditions with enhanced capabilities for agile movements and logical reasoning. RLWRLD has attracted strategic investors from major corporations and plans to demonstrate humanoid-based autonomous actions later this year.

Amazon Deploys AI Across All Operations, Dismisses Open Source Compute Efficiency

Amazon's VP of Artificial General Intelligence, Vishal Sharma, stated that AI is pervasive across all Amazon operations, from AWS cloud services to warehouse robotics and consumer products like Alexa. He emphasized Amazon's need for diverse AI models suited to specific applications, dismissed the notion that open source models might reduce compute demands, and predicted that computing resources will remain a crucial competitive factor for the foreseeable future.