TechnoSphere’s powerful Learning Evolutionary COE is equipped to begin the journey by generating—and then comparing—diverse candidate agents (genes) to distinguish which ones are better suited to solve a particular problem. A “fitness score” is assigned to each candidate based on how well it performs relative to its peers.
Through masses of generations, distributed over millions of CPUs, the system gradually converges on solutions, resulting in code that works to solve the complex problems across all industries. This breakthrough technology allows businesses to scale evolutionary AI techniques to solve the most complex business problems.
Evolving deep network architectures means the system can automatically design AI solutions that are better than the alternatives designed using many Ph.D.-days of human labor.
Evolving and iteratively improving a decision prescription system with a goal to impact outcomes important to your industry is a unique feature in the system.
To AI-enable a business, it must go through many experiments, trying out different approaches, measuring success and learning from each iteration. Our approach can augment or even replace this process using a principled AI-based approach.
TechnoSphere’s Evolutionary AI Model Optimization, evolutionary AutoML, creates models with high performance and accuracy. These models reduce the need for expert in-house talent and extend to a wide range of applications, including those where little data exists and when only limited computing and memory is available.
Tens of thousands or even millions of potential outcomes can be tested by the model to identify the best outcomes to implement in the real world. AI and model building is all about finding the right architectures and meta-level parameters, and now this can be done automatically.
TechnoSphere’s Evolutionary AI Business Optimization consulting improves decision-making in a very principled data-driven manner. It builds a predictive engine that helps business managers maximize business outcomes by recommending optimal decisions that apply directly to their goals.
It runs a continuous learning and optimization life cycle loop on a surrogate model of the real world, saving both time and money. Each evolution, or loop, identifies and selects better outcomes. Our optimization engine is ideal in situations where the need to evaluate multiple complex variables is beyond the capability of humans. It doesn’t replace humans, it expands their ability to identify the—sometimes very complicated—relationships within their data and pinpoint best actions.