Research firm Gartner has identified four trends that are driving artificial intelligence innovation in the near term. These technologies and approaches will be key to scaling AI initiatives, the company emphasized in a news announcement:
1) Responsible AI. Stakeholders are demanding increased trust, transparency, fairness and auditability of AI technologies, according to Svetlana Sicular, research vice president at Gartner. Responsible AI provides a governance framework for meeting those requirements: “Responsible AI helps achieve fairness, even though biases are baked into the data; gain trust, although transparency and explainability methods are evolving; and ensure regulatory compliance, while grappling with AI’s probabilistic nature,” Sicular said.
2) Small and wide data. Gartner contends that AI models based on large amounts of historical data have become less relevant as organizations have undergone sweeping changes during the COVID-19 pandemic. Today, small data — which Gartner defines as “the application of analytical techniques that require less data but still offer useful insights” — and wide data — “data that enables the analysis and synergy of a variety of small and large, unstructured and structured data sources” — enable more robust analytics for decision-making. “By 2025, 70 percent of organizations will be compelled to shift their focus from big to small and wide data, providing more context for analytics and making AI less data hungry,” Gartner predicted.
3) Operationalization of AI platforms. Operationalization means “moving AI projects from concept to production, so that AI solutions can be relied upon to solve enterprise-wide problems,” Gartner said, pointing out that it’s a critical step toward leveraging AI for business transformation. “Only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so,” said Sicular. Innovations in AI operationalization are “enabling reusability, scalability and governance, accelerating AI adoption and growth,” she added.
4) Efficient use of resources. “Given the complexity and scale of the data, models and compute resources involved in AI deployments, AI innovation requires such resources to be used at maximum efficiency,” Gartner noted. A few areas gaining traction in this area include multiexperience, composite AI, generative AI and transformers.
Source: campustechnology.com
