By Rick Whiting
Artificial Intelligence and machine learning have been hot topics in 2020 as AI and ML 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 AI hardware, software and services is expected to reach US$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 five 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.
The growing role of AI 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.”
AI and machine learning are key components – and major drivers – of hyperautomation (along with other technologies like robot 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 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. (Deep learning is a subset of machine learning that utilizes neural network algorithms to learn from large volumes of data.)
Bringing discipline to AI development 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.
Businesses and organizations are coming to understand that a robust AI engineering strategy will improve “the performance, scalability, interpretability and reliability of AI models” and deliver “the full value of AI investments,” according to Gartner’s list of Top Strategic Technology Trends for 2021.
Developing a disciplined AI engineering process is key. AI engineering incorporates elements of DataOps, ModelOps and DevOps and makes AI a part of the mainstream DevOps process, rather than a set of specialized and isolated projects, according to Gartner.
Increased use of AI for cyber security applications
Artificial intelligence and machine learning technology is increasingly finding its way into cybersecurity systems for both corporate systems and home security.
Developers of cybersecurity 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 cybersecurity tools also can 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, according to research firm IHS Markit. But IHS says 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 US$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 analyzed 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.
Persistent ethical questions around AI technology
Earlier this year as protests against racial injustice were at their peak, several leading IT vendors, including Microsoft, IBM and Amazon, announced that they would limit the use of their AI-based facial recognition technology by police departments until there are federal laws regulating the technology’s use, according to a Washington Post story.
That has put the spotlight on a range of ethical questions around the increasing use of artificial intelligence technology. That includes the obvious misuse of AI for “deepfake” misinformation efforts and for cyberattacks. But it also includes grayer areas such as the use of AI by governments and law enforcement organizations for surveillance and related activities and the use of AI by businesses for marketing and customer relationship applications.
That’s all before delving into the even deeper questions about the potential use of AI in systems that could replace human workers altogether.
A December 2019 Forbes article said the first step here is asking the necessary questions – and we’ve begun to do that. In some applications federal regulation and legislation may be needed, as with the use of AI technology for law enforcement.
In business, Gartner recommends the creation of external AI ethics boards to prevent AI dangers that could jeopardize a company’s brand, draw regulatory actions or “lead to boycotts or destroy business value.” Such a board, including representatives of a company’s customers, can provide guidance about the potential impact of AI development projects and improve transparency and accountability around AI projects.