AI and Machine Learning Trends to Spot in 2021

22 Jun 2021

Artificial Intelligence and Machine Learning are on everyone’s lips since last year. These technologies become slowly but surely integrated into everything, starting from cutting-edge medical diagnostic systems and quantum computing systems to user electronics and personal assistants. The return from AI technologies has reached 150  billion around the world. It is easy to get confused among all the trends in the use and development of Artificial Intelligence and Machine Learning. In this article, we will cover major ways of application of AI and ML, as well as their use and development.

The importance of AI and ML in Hyperautomation.

Market research firm Gartner identifies Hyperautomation as a megatrend; its main concept lies in total automation of everything within a company, such as various business processes, etc. This process, also known as the digital automation process, has developed even more due to the pandemic. ML and AI are important elements of Hyperautomation. For successful implementation, the static software package is not sufficient. These automated corporate processes should be able to adapt to changing situations and be responsive to unexpected circumstances. In this case, AI, ML, and deep learning technologies implement learning models and algorithms. This way, the system improves as time passes by affecting business processes. 

AI development through AI engineering

It might surprise you, but only 50% of AI ventures make it through the process of prototype to complete production. While implementing newly developed AI systems and ML models, companies often have issues with the system maintainability, governance, and scalability, as some promising AI projects often don’t meet the hoped-for expectations. Many companies believe that a vigorous AI engineering strategy will affect the scalability, performance, and reliability of AI models and yield the entire value of AI investments. Creating a strict AI engineering process is key. AI engineering includes elements of ModelOps, DataOps, and DevOps and makes Artificial Intelligence an integral part of the common DevOps process, rather than a combination of single specialized projects. 

Enhanced use of AI for cybersecurity applications

 AI and ML technology is finding new implementations into cybersecurity systems for both corporate systems and home security. There is a race among cybersecurity systems developers who strive to keep their technology apace constantly evolving threats from malicious software, DDS attacks, etc. AI and ML technology can be used to spot all kinds of threats. Cybersecurity tools powered by AI can gather data from corporate transactional systems, communications structures, online activity, and web resources, in addition to external public resources, and implement AI algorithms to detect threatening activity. The use of AI in home security systems is still quite limited; the main application integrates video cameras and intruder alert systems implemented with a voice assistant. However, experts expect an expansion of AI to create “smart homes” where the system learns the ways and preferences of residents – increasing its ability to detect intruders.
The confluence of Artificial Intelligence, Machine Learning, and IoT

The IoT is a constantly developing area of technology. Experts forecast that the IoT market will increase to more than 20 billion devices in 10 years while generating trillions of dollars in revenue. There is an intersection in use between AI, ML, and IoT; for instance, these technologies are implemented to make devices smarter and more secure. Also, when it comes to industrial automation, IoT networks can collect operational and performance data analyzed by AI systems to boost production system performance, increase efficiency and predict when machines will require maintenance. 

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