Edge AI and responsible AI top trends impacting data science and machine learning

Edge AI and responsible AI are some of the top trends that will impact the future of data science and machine learning (DSML) as organisations continue their shift toward more AI applications.

According to Gartner, these trends highlight the way the industry will rapidly grow and evolve to meet the increasing significance of data in artificial intelligence (AI), particularly as the focus shifts towards generative AI investments.

Other growing trends include cloud data ecosystems, data centric AI and accelerated AI investments.

At the Gartner Data and Analytics Summit in Sydney, Peter Krensky, director analyst at Gartner said as machine learning adoption continues to grow rapidly across industries, DSML is evolving from just focusing on predictive models, toward a more democratised, dynamic and data-centric discipline.

“This is now also fuelled by the fervour around generative AI. While potential risks are emerging, so too are the many new capabilities and use cases for data scientists and their organisations,” he said.

Demand for edge AI is growing to enable the processing of data at the point of creation at the edge, helping organisations to gain real-time insights, detect new patterns and meet stringent data privacy requirements.

Edge AI also helps organisations improve the development, orchestration, integration and deployment of AI.

Responsible AI

Responsible AI makes AI a positive force, rather than a threat to society and to itself, Gartner noted.

It covers many aspects of making the right business and ethical choices when adopting AI that organisations often address independently, such as business and societal value, risk, trust, transparency and accountability.

Gartner predicts the concentration of pre-trained AI models among 1 percent of AI vendors by 2025 will make responsible AI a societal concern.

Data ecosystems

Data ecosystems are moving from self-contained software or blended deployments to full cloud-native solutions. By 2024, Gartner expects 50 percent of new system deployments in the cloud will be based on a cohesive cloud data ecosystem rather than on manually integrated point solutions.

Gartner recommends organisations evaluate data ecosystems based on their ability to resolve distributed data challenges, as well as to access and integrate with data sources outside of their immediate environment.

Data-centric AI

Data-centric AI represents a shift from a model and code-centric approach to being more data-focused to build better AI systems. Solutions such as AI-specific data management, synthetic data and data labelling technologies, aim to solve many data challenges, including accessibility, volume, privacy, security, complexity and scope.

The use of generative AI to create synthetic data is one area that is rapidly growing, relieving the burden of obtaining real-world data so machine learning models can be trained effectively.

By 2024, Gartner predicts 60 percent of data for AI will be synthetic to simulate reality, and future scenarios and de-risk AI, up from 1 percent in 2021.

AI investment

Investment in AI will continue to accelerate by organisations implementing solutions, as well as by industries looking to grow through AI technologies and AI-based businesses. By the end of 2026, Gartner predicts that more than $10 billion will have been invested in AI startups that rely on foundation models – large AI models trained on huge amounts of data.

A recent Gartner poll of more than 2,500 executive leaders found that 45 percent reported that recent hype around ChatGPT prompted them to increase AI investments. Seventy percent said their organization is in investigation and exploration mode with generative AI, while 19 percent are in pilot or production mode.