and have been hot buzzwords in 2020. As we approach 2021, it’s a good time to take a look at five “big-picture” trends and issues around the growing use of and technologies.
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Artificial Intelligence and have been hot topics in 2020 as and technologies increasingly find their way into everything from advanced quantum computing systems and leading-edge medical diagnostic systems to consumer electronics and “smart” personal assistants.
Revenue generated by hardware, software and services is expected to reach $156.5 billion worldwide this year, according to market researcher IDC, up 12.3 percent from 2019.
But it can be easy to lose sight of the forest for the trees when it comes to trends in the development and use of and technologies. As we approach the end of a turbulent 2020, here’s a big-picture look at five key and trends– not just in the types of applications they are finding their way into, but also in how they are being developed and the ways they are being used.
The Growing Role Of And Machine Learning In Hyperautomation
Hyperautomation, an IT mega-trend identified by market research firm Gartner, is the idea that most anything within an organization that can be automated – such as legacy business processes – should be automated. The pandemic has accelerated adoption of the concept, which is also known as “digital process automation” and “intelligent process automation.”
and are key components – and major drivers – of hyperautomation (along with other technologies like process automation tools). To be successful hyperautomation initiatives cannot rely on static packaged software. Automated business processes must be able to adapt to changing circumstances and respond to unexpected situations.
That’s where , models and technology come in, using “learning” algorithms and models, along with data generated by the automated system, to allow the system to automatically improve over time and respond to changing business processes and requirements. (Deep learning is a subset of that utilizes neural network algorithms to learn from large volumes of data.) […]
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