Identify Four Trends Driving Near-Term Artificial Intelligence Innovation

Identify Four Trends Driving Near-Term Artificial Intelligence Innovation
Responsible AI, Small and Wide Data, Operationalization and Efficient Resource Use Will Be Key to Scaling AI Initiatives

Responsible AI, Small and Wide Data, Operationalization and Efficient Resource Use Will Be Key to Scaling AI Initiatives
Four trends on the Gartner, Inc. Hype Cycle for Artificial Intelligence, 2021 are driving near-term artificial intelligence (AI) innovation. These trends include responsible AI; small and wide data approaches; operationalization of AI platforms; and efficient use of data, model and compute resources.
“AI innovation is happening at a rapid pace, with an above-average number of technologies on the Hype Cycle reaching mainstream adoption within two to five years,” said, senior principal research analyst at Gartner. “Innovations including edge AI, computer vision, decision intelligence and machine learning are all poised to have a transformational impact on the market in coming years.”
The AI market remains in an evolutionary state, with a high percentage of AI innovations appearing on the upward-sloping Innovation Trigger . This indicates a market trend of end users seeking specific technology capabilities that are often beyond the capabilities of current AI tools.
Here are the four trends that are driving AI innovation, according to Gartner:
Responsible AI
“Increased trust, transparency, fairness and auditability of AI technologies continues to be of growing importance to a wide range of stakeholders,” said, research vice president at Gartner. “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.”
Small and Wide Data
Data forms the foundation of successful AI initiatives. Small and wide data approaches enable more robust analytics and AI, reduce organizations’ dependency on big data, and deliver richer, more complete situational awareness.
Operationalization of AI Platforms
The urgency and criticality of leveraging AI for business transformation is driving the need for operationalization of AI platforms. This means moving AI projects from concept to production, so that AI solutions can be relied upon to solve enterprise-wide problems.
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. Multiexperience, composite AI, generative AI and transformers are gaining visibility in the AI market for their ability to solve a wide range of business problems in a more efficient manner.

Sep 11, 2021 13:05

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