It comes as no surprise that 2020 has witnessed an explosion in the deployment of Artificial Intelligence and Machine Learning technologies; increasingly finding 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 AI 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 AI and ML technologies. As we approach the end of a turbulent 2020, here’s a big picture look at some key AI and machine learning 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.
AI and ML in Hyperautomation
Hyperautomation (also known as ‘digital process automation’ or ‘intelligent process automation’) is the idea that almost anything within an organization that can be automated - such as legacy business processes - should be automated. The pandemic has of course accelerated adoption of the concept.
To be successful hyperautomation initiatives can’t rely on static packaged software. Automated business processes must be able to adapt to changing circumstances and respond to unexpected situations. Which is where AI, machine learning models and deep learning 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.
Bringing discipline through AI Engineering
Only about 53 percent of AI projects successfully make it from prototype to full production, according to Gartner research. When trying to deploy newly developed AI systems and machine learning models, businesses and organizations often struggle with system maintainability, scalability and governance, and AI initiatives often fail to generate the hoped-for returns.
A robust AI engineering strategy will facilitate the performance, scalability, interpretability and reliability of AI models while delivering the full value of AI investments. AI projects often face issues with maintainability, scalability and governance, which makes them a challenge for most organizations.
AI engineering offers a pathway, making AI a part of the mainstream DevOps process rather than a set of specialized and isolated projects. It brings together various disciplines to tame the AI hype while providing a clearer path to value when operationalizing the combination of multiple AI techniques. Due to the governance aspect of AI engineering, responsible AI is emerging to deal with trust, transparency, ethics, fairness, interpretability and compliance issues. It is the operationalization of AI accountability.
AI in Cyber Security Applications
Artificial intelligence and machine learning technology is increasingly finding its way into cyber security systems for both corporate systems and home security. Developers of cyber security systems are in a never-ending race to update their technology to keep pace with constantly evolving threats from malware, ransomware, DDS attacks and more. AI and machine learning technology can be employed to help identify threats, including variants of earlier threats.
AI-powered cyber security tools can also collect data from a company’s own transactional systems, communications networks, digital activity and websites, as well as from external public sources, and utilize AI algorithms to recognize patterns and identify threatening activity - such as detecting suspicious IP addresses and potential data breaches.
AI use in home security systems today is largely limited to systems integrated with consumer video cameras and intruder alarm systems integrated with a voice assistant. But AI use will expand to create ‘smart homes’ where the system learns the ways, habits and preferences of its occupants - improving its ability to identify intruders.
The Intersection of AI/ML and IoT
The Internet of Things has been a fast growing area in recent years with market researcher Transforma Insights forecasting that the global IoT market will grow to 24.1 billion devices in 2030, generating $1.5 trillion in revenue.
The use of AI/ML is increasingly intertwined with IoT. AI, machine learning and deep learning, for example, are already being employed to make IoT devices and services smarter and more secure. But the benefits flow both ways given that AI and ML require large volumes of data to operate successfully - exactly what networks of IoT sensors and devices provide.
In an industrial setting, for example, IoT networks throughout a manufacturing plant can collect operational and performance data which is then analysed by AI systems to improve production system performance, boost efficiency and predict when machines will require maintenance. What some are calling “Artificial Intelligence of Things (AIoT) could redefine industrial automation.
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