Gartner: The main development directions of machine learning in 2023
At the recent Gartner Data & Analytics Summit in Sydney, Australia, analysts from the research and advisory firm highlighted some of the top trends in data science and machine learning
Generative artificial intelligence, as a breakthrough technology in the field of machine learning, has aroused widespread discussion. It is expected to impact various industries in some way, tied to some of the trends identified by Gartner and the advancement and popularity of generative AI tools. Peter Krensky, principal analyst at Gartner, noted in a report: "With the As the use of machine learning across industries continues to grow rapidly, data science and machine learning are moving away from focusing solely on predictive models to more democratized, dynamic, and data-centric disciplines. Despite some potential risks, data scientists and their Organizations are constantly emerging with many new capabilities and use cases."
Here are five trends that Gartner believes are shaping the future of data science and machine learning:
1. Cloud data ecosystem
Over the past decade, organizations have often developed cloud data ecosystems in a way that moves them from point A to point B, rather than deploying them as a cohesive cloud data unit. According to Gartner, by 2024, half of deployments will be cohesive ecosystems rather than manually integrated point solutions, which has been the norm for most deployments over the past decade
2 , Edge artificial intelligence
According to Gartner, the next technology that may move to the edge is artificial intelligence. Demand for edge AI is growing as enterprises look to process data closer to the point of data generation to provide real-time, actionable insights. The ability to run AI software at the edge is also beneficial for operators in industries with strict data privacy requirements that do not allow data to be transferred to data centers or out of the country
3. Responsible Artificial Intelligence
More and more organizations are adopting artificial intelligence when considering ethical choices, which is called "responsible artificial intelligence". This concept focuses on various aspects of how models are trained and used, and ensures compliance with other risk and compliance measures. According to Gartner’s predictions, with the popularity of pre-trained models, more and more developers will regard responsible artificial intelligence as a social concern
4. Data-centric artificial intelligence
The focus of artificial intelligence development is changing from a code-centric approach to a data-centric approach. Data management, synthetic data and data labeling have become key factors in the successful development of artificial intelligence. According to Gartner, by 2024, 60% of artificial intelligence data will be comprehensively created to stimulate reality, up from 1% in 2021
5. Accelerate artificial intelligence investment
Investment in AI has reached high levels in many industries and is expected to continue to increase in the coming years as more businesses seek to implement AI solutions. Investment in AI startups relying on underlying models is expected to reach $10 billion by the end of 2026
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