Archetype AI - Physical AI for the Real World

Archetype AI Physical AI for the Real World

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#physical AI
#LBM
#sensor data
#time series data
#multimodal
#Real Time

What is Archetype AI?

Archetype AI is a physical AI company focused on developing solutions that help humanity understand and interact with the physical world. The core of their technology is Newton, a proprietary foundation model referred to as a Large Behavior Model (LBM). Unlike AI models that primarily process text and images, Newton perceives and reasons about the physical world in real time by fusing multimodal sensor data with natural language.

Newton utilizes time-series data from a variety of sensors, including radars, cameras, accelerometers, and gyroscopes. This data is encoded into a universal embedding space, creating a single, compressed mathematical representation of the physical world that is constantly updated. This allows Newton to compute the meaning of sensor data and present it in various helpful formats, such as text, visualizations, or code, to assist humans. Anyone can interact with the system using natural language, speech, or gestures to obtain insights and predictions.

Features

API for the Physical World: Integrate Newton into applications to summarize, search, predict, and monitor sensor data.
Sensor Agnostic: Compatible with cameras, microphones, radars, and time-series sensors like thermometers and accelerometers.
Data Privacy: Run Newton with proprietary data on Archetype AI's infrastructure or your own, ensuring data privacy.
Fine-Tuning: Customize Newton with proprietary data for specialized use cases, creating a unique physical AI model.
Edge Deployment: Deploy Newton at the edge in devices, vehicles, or machinery, with optimized models for specific tasks.

Use Cases

Improve safety on construction sites by monitoring for potential hazards.
Enhance smart homes with spatial computing, automating tasks based on presence and preferences.
Adapt vehicles to people through spatial interactions, improving safety and convenience.
Reduce factory downtime by detecting anomalies in automation and robotic systems.
Monitor energy infrastructure to predict maintenance needs and optimize energy production.
Generate comprehensive health reports from wearable sensors and other devices.