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The Coming Era of AI-Enhanced Superhumans
We’re on the edge of a radical transformation where, very soon, we won’t be able to tell where the human ends and technology begins. This intermeshing of mind, body, and technology will become so seamless and invisible that we essentially “become one with the tech.”
On the surface this sounds like George Orwell’s worst nightmare, a totally frightening proposition where our first instinct is fear, fear of the machines taking over like the Borg on Star Trek. In the back of our minds we have a deep fear of loosing control or having something hijacking our minds and assuming control. After all, isn’t this what Hollywood has been warning us about?
Yes, there’s always a potential for things to go wrong, but adding intelligent agents to our life could have an enormously positive impact.
The AI-enhanced customer experience
Artificial intelligence (AI) is unleashing a new approach for customer experience (CX) strategy, design and development. We haven’t seen change on this scale since the internet transformed print professionals into digital pioneers. But the timeline for evolution is far shorter than it was twenty years ago. Fifty percent of companies surveyed are already taking action to deploy AI, using it to quickly access insights and automate campaigns and processes. They can also embed it directly into new customer touchpoints. However, many companies have important capability gaps, and lack the strategy and skills needed to meet their AI aspirations. As a result, executives may be overestimating the ability of their organizations to make the shift.
AI-Enhanced Offloading in Edge Computing: When Machine Learning Meets Industrial IoT
The Industrial Internet of Things (IIoT) enables intelligent industrial operations by incorporating artificial intelligence (AI) and big data technologies. An AI-enabled framework typically requires prompt and private cloud-based service to process and aggregate manufacturing data. Thus, integrating intelligence into edge computing is without doubt a promising development trend. Nevertheless, edge intelligence brings heterogeneity to the edge servers, in terms of not only computing capability, but also service accuracy. Most works on offloading in edge computing focus on finding the power-delay trade-off, ignoring service accuracy provided by edge servers as well as the accuracy required by IIoT devices. In this vein, in this article we introduce an intelligent computing architecture with cooperative edge and cloud computing for IIoT. Based on the computing architecture, an AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud. A case study is performed on transfer learning to show the performance gain of the proposed framework.