On-the-fly management, automated control and optimisation of data for machines
Artificial Intelligence On-a-Chip
Aftos:Robotics is an onboard integrated artificial intelligence system for next-generation highly-connected (autonomous) vehicles and robots, enabling on-the-fly management, automated control and optimisation of data from sensors and systems.
The newest innovation to be developed by Massive Analytic, this technology will provide the fundamental basis for network intelligence and macro intelligent control at city level and is based on Massive Analytic’s patented AI technology Artificial Precognition.
Artificial Precognition, Making Sense of Noisy Data
Artificial Precognition uses Adaptive Cognized Control (APACC) to increase signal to noise ratios on data and provide a new level of inferential thinking and real time communication for connected machines. It achieves this by blending together complex machine learning algorithms, including deep learning and possibilistic classifiers such as fuzzy logic and decision trees.
APACC goes far beyond what has until now been possible by mimicking the way humans use a combination of stored memories and sensory input to interpret and respond to events as they occur, and even anticipate likely scenarios.
Nearly Endless Applications for Controlling Machines of all Kinds
The potential applications for Aftos are huge as the AI-on-a-Chip technology can be applied to all manner of vehicles and machines. Whether that’s integrated within a smart city for seamless intelligent control of a transportation grid, or controlling smart robots in hazardous environments – enabling them to perform complex jobs without a human being at the controls or even providing precision docking for a space craft.
Case Study: Identifying Cancer Cells with Machine Learning Using Oscar:DataScience
The early diagnosis and prognosis of a cancer type has become a necessity in cancer treatment. Massive Analytic Limited developed new algorithms for use in precision medicine for personalised treatment of cancer victims.