Small Data, Real-time ML/AI Training, On-Platform/On-Edge/On-Cloud, Actionable Predictive Accuracy
Arguably the biggest obstacle to the widespread implementation of “Big Data” machine learning (ML) and artificial intelligence (AI) is the massive amounts of data and associated infrastructure these methods require to be predictive. This makes the implementation of “Big Data” ML/AI complicated, costly, unwieldy, and relegated to server based or on cloud, where data storage, management and computational power is sufficient to manage it.
PredictiveIQ™ is developing a new class of data-driven and physics informed ML/AI that utilizes advanced mathematics and/or physics priors to reduce by orders of magnitude (1,000X) the amount of data required to train ML/AI algorithms (i.e. “Small Data”). This in turn enables real-time training, which can be done on-platform, on-edge or on-cloud and significantly improves the predictive accuracy of the ML/AI algorithm, thereby making them actionable.
Predictive Engineering, Predictive Maintenance, Predictive Performance
Our solutions are valuable for a broad range of problems and are particularly relevant in industries with highly engineered products where data transmission, management, and storage is either difficult, impossible, or undesired. Industries with these characteristics include ground combat vehicles, mining, rail, trucks, construction equipment, agriculture, automotive, aerospace, marine, energy, and life sciences. Our solutions speed engineering product development, ensure equipment uptime by predicting and preventing maintenance events, and optimize operational performance. We enable digital twins and facilitate new business models such as Product as a Service (PaaS).
We are organized around three solution segments as described below:
Hyper-Fast Multi-Disciplinary Machine Learning Models
Convert computationally intensive engineering simulations into advanced ML/AI surrogates that enable Design Exploration & Optimization, Product Validation, and System Simulation.
Diagnostics, Prognostics & Prescriptive Maintenance
Predict and prevent machine maintenance events. Ensure equipment uptime and materiel availability using advanced ML/AI algorithms.
Objective Specific Performance Optimization
Optimize machine performance for given objectives using advanced ML/AI and optimal control frameworks. Enable Autonomy, Electrification, and System Performance Optimization.
Traditional digital transformation efforts focus around connecting assets, adding sensors, and managing large amounts of data. This data needs to be collected, stored, aggregated, labeled, and then transmitted to some cloud environment for “Big Data” ML/AI algorithms to then make sense of this massive ocean of data.
In order for machines to be truly smart and autonomous, they must be able to predict in real-time on platform or on edge, machine performance under very complex environments. This is simply not practical under traditional cloud-centric “big data” data analytics paradigms.
At PredictiveIQ, our vision is making truly smart and autonomous a reality through our groundbreaking predictive ML/AI technologies that reduce data requirement for orders of magnitude and our business-centric solutions that are aligned to key business processes.