AI and ML: 5 Emerging Trends for 2021
In 2020 some of the hottest topics have been AI and ML. The technologies have increasingly found their way into everything from leading-edge medical diagnostic systems, to “smart” personal assistants and advanced quantum computing systems.
According to market researchers IDC, revenue brought in by AI software, hardware and services is expected to reach £117 billion globally this year. This is a 12.3 percent growth from 2019.
Nearing the end of what can only be classed as an unexpected and turbulent 2020 we take a big-picture look at 5 key Artificial Intelligence and Machine Learning trends.
Cybersecurity Applications: AI Increasingly Used
Cybersecurity systems for both home and corporate security systems is seeing an increase in artificial intelligence and machine learning technology.
The developers that create cybersecurity systems are constantly embroiled in a never-ending scramble to update their technology to keep up with the constantly evolving threats from ransomware, DDS attacks, malware and more. In order to identify threats Artificial Intelligence and Machine Learning can be employed.
Cybersecurity tools that are AI-powered can also collect data from a company’s own communication networks, digital activity, websites, transactional systems, as well as external public sources. They can then utilise AI algorithms that identify threatening activity, like detecting suspicious IP addresses and potential data breaches.
Artificial Intelligence use in home security systems today however is largely limited to intruder alarm systems integrated with a voice assistant according to IHS Markit. However, the research firm also says that AI use will expand to create “smart homes” where the system learns the ways, preferences and habits of its occupants. This will improve its ability to identify intruders.
IoT and AI/ML Intersecting
Even though the Internet of Things has been a fast-growing area in recent years, Transforma Insights forecasts that the worldwide IoT market will reach 24.1 billion devices by 2030, generating revenue of £1.13 trillion.
IoT is increasingly intertwined with AI and ML. For example AI, machine learning, and deep learning are already being used to make IoT services and devices smarter and more secure. However, it’s a two-way street in terms of benefits. Artificial Intelligence and Machine Learning need large volumes of data to successfully operate and that’s exactly what IoT sensor and device networks provide.
Take an industrial setting for example, where IoT networks throughout a manufacturing plant can collect performance and operational data. AI systems them analyse this in order to improve production system performance, predict when machines will need maintenance and boost efficiency.
“Artificial Intelligence of Things” (AIoT) could ultimately redefine industrial automation.
AI Engineering: Discipline in Development
According to Gartner, only around 53 percent of projects make it from prototype to full production successfully. Businesses and organisations struggle with maintainability, scalability, and governance when trying to deploy newly developed AI systems and machine learning models. Because of this AI initiatives often fail to generate projected returns.
A robust AI engineering strategy is now understood by business to be essential in improving “the performance, scalability, interpretability and reliability of AI models” and delivering “the full value of AI investments”, according to Gartner’s list of Top Strategic Technology Trends for 2021.
The development of a disciplined AI engineering process is absolutely pivotal. Elements of ModelOps, DevOps, and DataOps are incorporated in AI engineering, which makes AI a part of the mainstream DevOps process, rather than keeping it as a set of isolated and highly-specialised projects.
As identified by Gartner, an IT mega-trend is Hyperautomation. The trend encompasses the idea that anything in an organisation that could be automated, should be. The adoption of this concept has been accelerated by the pandemic and its also known as “intelligent process automation” and “digital process automation”.
Two major drivers of hyperautomation are Artificial Intelligence and Machine Learning, alongside other technologies such as robot process automation tools. In order for a hyperautomation initiative to be successful it cannot rely simply on static packaged software, but instead must be able to adapt to changing circumstances and respond to unanticipated situations.
This where Artificial Intelligence and Machine Learning models come in, joined by deep learning technology. By using “learning” models and algorithms, next to data generated by the automated system, it allows the system to automatically improve over time and react to changing business requirements and processes.
According to a Washington Post story many leading IT vendors such as IBM, Amazon and Microsoft announced their would limit the use of their proprietary AI-based facial recognition technology by police departments until there were federal laws that regulated their use. This came as protests against racial injustice were at their peak earlier this year.
The move has put a range of ethical questions around the use of AI technology into the spotlight. One of the clearer areas include the obvious misuse of AI for creating “deepfake” misinformation campaigns as well as cyberattacks. Some greyer areas include the use of AI by law enforcement and governments for surveillance and related activities and the use of AI by business for customer relationship and marketing purposes.
This is all before even going into the even deeper questions about the potential future in which AI systems replace human workers completely.
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