Multi-experience, 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. 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. “Innovations such as AI orchestration and automation platforms (AIOAPs) and model operationalisation (ModelOps) are enabling reusability, scalability and governance, accelerating AI adoption and growth.” Efficient use of resources “Gartner research has found that 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. This means moving AI projects from concept to production, so that solutions can be relied upon to solve enterprise-wide problems. The urgency of leveraging AI for business transformation is driving the need for operationalisation of AI platforms. Read here Operationalisation of AI platforms We gauged the perspectives of experts in data science, asking them about the biggest emerging trends in data science. “Together, these approaches enable more robust analytics and help attain a more 360-degree view of business problems.” Hot topics and emerging trends in data science “Small data is about the application of analytical techniques that require less data but still offer useful insights, while wide data enables the analysis and synergy of a variety of data sources,” said Sicular. With data forming the foundation of successful AI initiatives, small and wide data approaches enable more robust analytics and AI, reduce dependency on big data, and deliver richer, more complete situational awareness.Īccording to Gartner, 70% of organisations will be compelled to shift their focus from big to small and wide data by 2025, providing more context for analytics and making AI less data-hungry. The global research and advisory body expects that by 2023, all personnel hired for AI development and training work will have to demonstrate expertise in responsible AI. “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,” explained Svetlana Sicular, research vice-president at Gartner. Increased trust, transparency, fairness and auditability of AI is believed to be of growing importance to stakeholders, and responsible AI is helping to achieve this. The four trends driving AI innovation today, according to Gartner, are as follows: Responsible AI “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.” Source: Gartner (September 2021) “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 Shubhangi Vashisth, senior principal research analyst at Gartner. Meanwhile, smart robots, knowledge graphs, edge AI and digital ethics are among the trends at the ‘Peak of Inflated Expectations’. This finding indicates a market trend of end users seeking specific technology capabilities that are often beyond the capabilities of current AI tools.
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