Create richer data models
Wrap existing data and models to expand features for analysis and to create richer models with behavioral, scientific, and physics phenomena. Solve for where the laws of physics and nature intersect with data science.
Solve Data Cold Cases
Wrap existing data and models to expand features for analysis and to create richer models with behavioral, scientific, and physics phenomena. Solve for where the laws of physics and nature intersect with data science. Context-Augmented Machine Learning helps you develop beyond traditional “dumb” ML and take problem-solving to the next level, tackling previously challenging advanced data science problems. Utilize Context-Augmented Machine Learning (CAML) to take problem-solving to the next level. With CAML, you can use your existing data and models to analyze AI “cold cases,” aka advanced data science problems. From here, you can create ML models that account for behavioral, scientific, and physics phenomena and solve these problems.
Create richer data models applicable to the real world
Smarter, Adaptable Models
Improve model accuracy and performance in new, real-world situations by enriching initial data features and model training. With CAML you can solve data problems including object detection, medical imaging, sensor fusion, predictive diagnostics, satellite and aerial imaging, thermography, anomaly detection and much more!
Software Development Services
Ready to work with Dynam.AI? Here’s how our process works
1. Data Preparation
We begin by analyzing and preparing your data. This includes cleanup, labeling, augmentation, embedding, and pre-filtering.
2. Feature Selection & Application
We then assess your raw data to determine its best application and select algorithms to achieve optimal results.
3. AI Model Development & Model Training
Our data scientists then create and train AI/machine learning models that replicate cognitive thought processes and methodologies to ensure your AI application performs as it should.
4. Model Deployment & Solution Integration
We then deploy the AI/machine learning models we have created into your business to test it, analyzing data via APIs, front-end interfaces, and packaging.
5. Launch & Service
Finally, we identify the proper hosting environment for your application, and develop a plan for continued support and maintenance.
Data Science for the Real World
Machine Learning is limited by the available data, and knows nothing outside of this. Dynam.AI solves that problem by adding needed context, such as laws of physics, nature, and behavior.
Research into areas such as physics-informed neural networks are showing benefits of adding understanding of first principles and other generalized intelligence, but developing these networks is challenging and often leads to one-off project-based development. Using the patented Context-Augmented Machine Learning (CAML) approach, you can apply a well-developed library from Dynam.AI, as well as incorporating your own computational models or those from outside organizations or academia, directly into the Machine Learning training process.
As a result, you can solve challenging “cold cases” and deliver flexible, powerful AI for the real world.