This year has seen some pretty interesting technology trends. We have a quick look at some of them and what they may mean…..
Machine Learning or Deep Learning (ML or DL) Technology
Machine learning and Deep Learning technology is already on the edge of a revolution. They are widely adopted in datacenters (Amazon making graphical processing units [GPUs] available for DL, Google running DL on tensor processing units [TPUs], Microsoft using field programmable gate arrays [FPGAs], etc.), and DL is being explored at the edge of the network to reduce the amount of data propagated back to datacenters. Applications such as image, video, and audio recognition are already being deployed for a variety of verticals. DL is used for a variety of assistive functions such as Autonomous Vehicles and Cancer Detection.
Digital Currencies and Blockchain Technology
These two are almost inseparable. Although digital currencies have their high an low points, mostly due to speculative trading, they are a viable alternative to hard currency. Bitcoin, Ethereum, and newcomers Litecoin, Dash, and Ripple have become commonly traded currencies. They will continue to become a more widely adopted means of trading. The use of Bitcoin and the revitalization of peer-to-peer computing have been essential for the adoption of blockchain technology in a broader sense. There will most likely be an increased expansion of companies delivering blockchain products and even IT heavyweights entering the market and consolidating the products.
IoT (Internet of Things) and Robotics
Empowered by Dep Learning at the edge, IoT continues to be the most widely adopted use case for edge computing. It is driven by real needs and requirements. It will most likely continue to be adopted with a broader set of technical offerings enabled by DL, as well as other uses of IoT, with an increasing rate of home use adoption as connectivity to the home improves and data prices drop. The past few years have seen an increased market availability of consumer robots, as well as more sophisticated military and industrial robots. This could trigger a wider adoption of robotics in the medical space for caregiving and other healthcare uses.
Assisted transportation / Autonomous Vehicles
While development of fully autonomous vehicles has slowed down due to numerous obstacles, a limited use of automated assistance has continued to grow, such as parking assistance, video recognition, and alerts for leaving the lane or identifying sudden obstacles. This vehicle assistance will develop further as automation and ML/DL are deployed in the automotive industry with the main goal of increasing safety.
Cybersecurity using Machine / Deep Learning and AI
Cybersecurity is becoming essential to everyday life and business, yet it is increasingly hard to manage. Exploits have become extremely sophisticated and it is hard for IT to keep up. Pure automation no longer suffices and AI is required to enhance data analytics and automated scripts. It is expected that humans will still be in the loop for taking actions. But AI itself is not immune to cyber attacks. We will need to make AI/DL techniques more robust in the presence of malicious actions in any application area.