Rick Robinson is a writer and blogger, with a current'day job' focus on the tech industry and a particular interest in the interplay of tech-driven factors and business... Artificial intelligence (AI) is coming soon to a network near you. Limited forms of AI are already in use, and much more powerful applications are now in development. That means there's no better time to start thinking about the implications of AI on cybersecurity. Speculation about AI in the form of robots has been popular for generations, dating back well before pioneering digital computers to the giant electronic brains of the 1940s. From the very beginning, this speculation has included worries about the dangers that might be posed by malicious or mistaken robots.

I can't think of a reference to the intellect that would feel more unauthentic and fake. It's no wonder people turn to The Terminator or The Matrix to fathom what it's all about. Fortunately, after 60 years of AI rumors fueled by academia and movies, we're finally starting to see signs that it means more than just robots taking over. Working in the tech industry, it's ironic that the AI lightbulb clicked not through an understanding of machine learning or engineering -- but through the human challenges we face in the technology world. While at Yahoo! and Apple, it was amazing to be part of technologies that not only helped enable the modern cloud today but continue to support its more than doubling in performance every year.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billions of data bytes, generated at the network edge, put massive demands on data processing and structural optimization. Thus, there exists a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial Intelligence on Edge). The former focuses on providing more optimal solutions to key problems in Edge Computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge. This paper provides insights into this new inter-disciplinary field from a broader perspective. It discusses the core concepts and the research road-map, which should provide the necessary background for potential future research initiatives in Edge Intelligence.

Intelligence is a complex concept and can be difficult to define. How we define it will determine how we go about attempting to detect it. If we use a simplistic definition that only includes computation speed and accuracy and amount of information, we will use those factors to determine whether or not we have detected intelligence. This becomes important when we are attempting to detect intelligence or determine whether or not intelligence is present in unusual circumstances or environments. Detecting the presence of intelligence in an artificial source (AI) or an alien source are obvious examples.