Gartner: Top Machine Learning Trends for 2023

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Machine learning adoption continues to grow rapidly across industries. Here are the major trends Gartner believes will shape its future use.

At the Gartner Data & Analytics Summit in Sydney, Australia, analysts from the research and consultancy firm highlighted some of the top trends in data science and machine learning. 

Generative AI was the key talking point, as the breakout technology in the machine learning space. It is expected to affect every industry in some way, and some of the trends put forward by Gartner have links to the progress and proliferation of generative AI tools. 

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“As machine learning adoption continues to grow rapidly across industries, data science and machine learning is evolving from just focusing on predictive models, toward a more democratized, dynamic and data-centric discipline,” said Peter Krensky, director analyst at Gartner, in a statement when the report was released. “While potential risks are emerging, so too are the many new capabilities and use cases for data scientists and their organizations.”

Here are the five trends Gartner believes are shaping the future of data science and machine learning: 

Cloud Data Ecosystems

For the first decade, organizations have often went about developing their cloud data ecosystems in a point-A to point-B way, instead of deploying as a cohesive cloud data unit. According to Gartner, by 2024 half of all deployments will be cohesive ecosystems instead of manually integrated point solutions, which were the norm for most in the previous decade. 

Edge AI

The next technology to move to the edge could be AI, as Gartner suggests demand for edge AI is growing as businesses want to be able to process data closer to the point of creation, to deliver real-time, actionable insights. The ability to run AI software on the edge is also beneficial for operators in industries with tight data privacy requirements, which do not allow data to be transported to data centers or out of the country. 

Responsible AI

More organizations are taking into account ethical choices when adopting AI, and this is coming under the blanket term “responsible AI” which looks at various elements of how models are trained and used, alongside making sure other risk and compliance measures are being followed. Gartner predicts that due to the growing concentration of pre-trained models, more foundational developers will make responsible AI a societal concern. 

Data-Centric AI

This represents a shift in focus for artificial intelligence development, from the code-centric approach which has been the dominant focus to data-centric, which concentrates on data management, synthetic data, and data labeling as the key factors for successful AI development. According to Gartner, 60 percent of data for AI will be synthetically created to stimulate reality in 2024, up from one percent in 2021.  

Accelerated AI Investment

AI investment, already at a high level across many industries, is expected to increase in the next few years, as more businesses look to implement AI solutions. Investment in AI startups which rely on foundational models is expected to reach $10 billion by the end of 2026.

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David Curry

About David Curry

David is a technology writer with several years experience covering all aspects of IoT, from technology to networks to security.

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