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New CA Technologies Research Explores How AI Can Improve Human Decision Making in IoT Applications

Singapore: CA Technologies announced its participation in a scientific research to discover how Internet of Things (IoT) applications can use a type of artificial intelligence (AI) known as ‘deep learning’ to imitate human decisions. The research will also explore how to prevent that AI-based decisions are not producing biased results.

This three-year research project is named ALOHA (adaptive and secure deep learning on heterogeneous architectures). It is funded by the European Union as part of the Horizon 2020 research and innovation programme, and coordinated by the University of Cagliari in Italy.

“The future of all technologies will include AI and deep learning in some way,” said Otto Berkes, chief technology officer, CA Technologies. “The expansion of complex, multi-layered IoT systems bring both security and software development challenges that AI and autonomous computing are uniquely positioned to address,” he added.

Until now, deep learning AI algorithmic processing has largely been limited to expensive, high performance servers. ALOHA will study the use of these deep learning algorithms on small, low-power consumption devices such as video cameras, sensors and mobile devices. This will enable them to learn, recognize and classify images, videos, sounds and sequences quickly and with high precision.

The ability of small devices to make smart decisions thanks to deep learning applications will be extremely useful in situations where human expertise is not available. For example, an IoT application could automatically provide a diagnosis for a medical CT scan image in a remote location.

The outcome of ALOHA to prevent data poisoning could be applied to help solve AI bias issues in IoT applications and also in other application contexts, to avoid situations as chatbot communicating a racist remark or a translation application advocating sexism. CA Technologies supports the development and security of deep learning applications.

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