Active Physical Intelligence: The Future of AI in the Real World
As part of the AI Innovation Summit at Google, KavAI’s CTO, presented our work on curiosity-driven intelligence. She emphasized why curiosity is innately intertwined with intelligence in the physical world and how our Active Physical Intelligence™ (AΦI™) addresses this need. Enjoy this video summary:-)
Active Physical Intelligence for Awareness
Monitoring for a Better, Greener Future.
Technology and infrastructure have expanded to mega-scale, reshaping industries worldwide. Yet, awareness of their operational state has lagged. Companies rely on outdated, reactive, and inefficient information, leaving businesses, governments, and communities vulnerable to catastrophic failures.
Unplanned shutdowns cost Fortune 500 companies up to $1.5 trillion in operational losses.
Undetected faults lead to safety incidents, risking injury and loss of life.
Leaked emissions cause irreversible environmental damage, harming ecosystems and communities.
Passive Data and Fragmented Intelligence
Current monitoring and learning systems rely on passively collected data, limited sensors, and fragmented views of operations. As a result, massive shutdowns and large-scale accidents have become a frequent reality.
Challenges of Awareness at Scale
Difficulty is in precisely pinpointing problems (precision) in a timely manner (proactiveness). Existing solutions (e.g., scanning with specialized robots or ubiquitous yet generic IoT platforms) trade off the extremes in a zero-sum fashion.
What can Active Physical intelligence achieve?
Active Physical Intelligence eliminates blind spots and adapts in real-time. Unlike existing solutions and their passive AI models, active physical intelligence simultaneously ensures precision (focus on critical areas) and proactiveness (real-time response).
Active Physical Intelligence
“bends” the precision vs speed
trade-off curve for a given
fixed cost.
Monitoring for a Better World: Exponential Possibilities
Monitoring for a Better, Greener Future.
The environmental and industrial monitoring market is set for exponential growth, driven by the urgent need for efficiency, sustainability, and safety. Kav AI’s Active Phsyical Intelligence uniquely positions us to sustain the growth in this expanding market.
Heavy Industry
In heavy industry, the cost of unplanned shutdown doubled between 2019 and 2021, reaching $128 million per plant annually — contributing to a global loss of $276 billion.
Oil & Gas
One of the largest sectors is the oil and gas industry the cost of unplanned shutdowns rose by 76% over the same period, reaching $149 million per plant annually and contributing to an estimated global loss of $300 billion.
Automotive & General Manufacturing
In 2021, major automotive plants faced annual losses of up to $600 million per facility due to unexpected shutdowns, reducing production capacity by as much as 45% and contributing to a global loss of $459 billion. In manufacturing, the cost of a one-hour shutdown increased by 50% between 2019 and 2021.
Power Generation & Maritime
The maritime and power generation sectors are also experiencing significant losses, estimated at approximately $85 billion and $122 billion, respectively.
What is Active Physical Intelligence?
Our Active Physical Intelligence is an adaptive foundation model designed for real-time data collection and acquisition. Its adaptive architecture functions as a physical attention system, seamlessly processing data across multiple scales, resolutions, and modalities.
A foundation model built for active data collection and acquisition, with an adaptive architecture that enables seamless data processing across scales, resolutions, and modalities.
Our active physical intelligence consists of two components:
Active Data and Inference (ADI): Uses AI-enabled robots, sensors, and cameras to seek and collect multi-modal physical data across time, space, and modality.
Active Foundation Model (AFM): Adaptive foundation model training and fine-tuning for precise and timely prediction of physical world and its sensory data.