Info-Tech has released its Artificial Intelligence (AI) Trends 2023 report detailing an overview of AI trends that will continue to drive innovation and new opportunities throughout the year for organizations.
The report, written by Irina Sedenko and Anuradha Ganesh, is broken down into eight trends: design for AI, event-based insights, synthetic data, edge AI, AI in science and engineering, AI reasoning, digital twin, combinatorial optimization
Trend highlights include:
Design for AI: Info-Tech reports that design for AI systems will change as the technology becomes more popular. Sustainable AI system design needs to consider several aspects such as the business application of the system, data, software and hardware, governance, privacy, as well as security. According to the report, an AI system design approach should cover all stages of AI lifecycle, from design to maintenance. It should also support and enable iterative development of an AI system.
To take advantage of different tools and technologies for AI system development, deployment, and monitoring, the design of an AI system should consider software and hardware needs.
AI in science and engineering:
The report details the impact AI has and will continue to have on the science and engineering fields. AI helps sequence genomes to identify variants in a person’s DNA that indicate genetic disorders. It allows researchers to model and calculate complicated physics processes, to forecast the genesis of the universe’s structure, and to understand the planet ecosystem to help advance the climate research. AI has been able to make advances in drug discovery.
“The role of AI in science will grow and allow scientists to innovate faster,” Info-Tech reports. It will continue to further contribute to science by assisting scientists with research to help find new insights, generalize scientific concepts, and transfer them between areas of scientific research.
Using synthetic data and combining physical and machine learning models and other advances of AI/ML will accelerate the use of AI in science and engineering, the report adds.
Synthetic data:
Synthetic data is artificially generated data that mimics the structure of real-life data. It’s used to train machine learning models when there is not enough real data or the existing data does not meet the specific needs. Synthetic data allows users to remove contextual bias from data sets containing personal data, prevent privacy concerns, and ensure compliance with privacy laws and regulations.
As of now, synthetic data is used in language systems, Info-Tech reports, in training self-driving cars, in improving fraud detection, and in clinical research. For the future, synthetic data has the ability to grow across all industries and applications of AI by allowing access to data for any scenario and technology and business needs.
Digital twins:
Digital twins (DT) are virtual replicas of physical objects, devices, people, places, processes, and systems. DT and AI technologies have enabled organizations to design and digitally test equipment like aircraft engines and wind turbines before actually manufacturing them, helping with cost and making processes overall more efficient.
Info-Tech says future for this tech includes enabling autonomous behaviour of a DT. An advanced DT can replicate itself as it moves into several devices, requiring it to be autonomous. “Such autonomous behaviour of the DT will in turn influence the growth and further advancement of AI,” the report adds.
Edge AI:
Edge AI integrates AI into edge computing devices for more seamless data processing and smart automation.
The main benefits of edge AI include real-time data processing capabilities to reduce latency and enable near real-time analytics and insights, and reduced cost and bandwidth requirements, since it’s unnecessary to transfer data to the cloud for computing. It also helps improve automation by training machines to perform automated tasks.
Challenges and solutions
Info-Tech’s report also explains the challenges that slowed the adoption of AI.
Some of these include data quality issues such as the lack of unified systems and unified data. The report noted that a lack of tools and technologies to operationalize models created by data scientists also slowed AI adoption. And in addition, a general lack of understanding of AI use cases such as how AI and machine learning (ML) can be applied to solve organizational problems.
Some solutions to speed up the adoption of AI include improving data management capabilities such as including data governance and data initiatives. Info-Tech also found that increasing the availability of cloud platforms will help grow machine learning operation capabilities.
