ATi continues to be on the forefront of innovation in predictive analytics and artificial intelligence. Our pioneering work in delivering predictive analytics as software as a service demonstrates ATi's commitment to use the power of science and technology to solve today's business challenges.

The ATi Research and Development team is comprised of world-renowned physicists, software engineers and sophisticated data analysts. Our team's scientific approach and research driven expertise is a true competitive advantage. Clients choose ATi based on our abilities to provide scientific insight and profound expertise in the areas of Artificial Intelligence, Data Mining, Predictive Analytics and Business Intelligence . This is the ATi difference .

Our scientific team is led by Dr. Chris Stephens, who has over 100 publications in refereed journals and conference proceedings, and who has collaborated with other distinguished scientists, such as advisory board members, Dr. Gerard t'Hooft, the 1999 Nobel Prize Winner in physics, and Dr. Itamar Procaccia, one of the world's leading experts in complex systems and chaos theory. Further insight into ATi's science and technology can be found in our white papers and related research publications.

ATi's proprietary Predictive Analytics platform is a multi-agent system where large numbers of Intelligent, Self-Monitoring Artificial Agents are created in an evolutionary environment - an "agent factory" - where only the "fittest" survive. Intuitively, an agent is a mathematical model or algorithm that provides an opinion, perspective, profile, prediction or strategy based on input data.

Both artificial intelligence and more standard statistical models can serve as embodiments of particular agents. An important agent type is that of a classifier, which relates a set of predictive drivers to membership of a class, such as the most profitable customers. As there are vast numbers of potential agents, an intelligent search must be carried out to determine the most appropriate ones for a given problem.

As real-world business problems are "multi-perspective", involving the simultaneous solution of many potentially antagonistic sub-problems and the subsequent combination of these partial solutions into a global one, so ATi's platform combines the outputs of different agents associated with different sub-problems into global solutions, thereby obtaining multi-perspective models. This type of modeling leads to a much more robust kind of data mining. Importantly,