Report: 5 key trends for AI’s future

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Artificial intelligence (AI) is a fundamental enabler for digital businesses, according to a new report from Gartner. The past 10 years represent a watershed period in the development of AI, spurred by the convergence of factors such as the rise of big data, advances in computing infrastructure, new machine learning techniques, the emergence cloud computing and the vibrant open source ecosystem.

In assessing the trends that will shape the future of AI in business, there are five trends that Gartner considers most important. Technology leaders must harness these emerging AI trends to democratize and drive its responsible use, adapt composite techniques, leverage AI for real-time analytics at the edge, and harness its generative powers.

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Democratized AI will make AI accessible to a wide range of users. From uncovering unexpected opportunities by identifying hidden trends in large datasets, to augmenting and helping workers complete their knowledge, democratized AI will touch every aspect of our lives.

Composite AI is the ability to deploy a variety of AI techniques aligned with the right use cases. It brings the power of AI to a broader set of use cases and users, and it is growing in popularity as it improves the versatility, efficiency, and adaptability of AI.

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Edge AI will enable digital moments by harnessing AI for real-time analytics closer to data sources. Gartner predicts that by 2025, more than 50% of all data analysis by deep neural networks will occur at the edge, up from less than 10% in 2021.

Responsible AI is an umbrella term for making appropriate business and ethical choices when adopting AI. This requires consideration of business and societal value, risk, trust, transparency, fairness, reduction of bias, explainability, responsibility, safety, privacy and regulatory compliance. Responsible AI is increasingly important amid growing regulatory scrutiny, consumer expectations, and emerging sustainability goals.

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Generative AI is the use of AI to generate new artifacts and create breakthrough products. To date, generative AI efforts have focused on creating multimedia content such as photorealistic images of people and objects, but they can also be used for code generation, creating synthetic tabular data, and the design of pharmaceutical products and materials with specific properties.

Read the full report from Gartner.

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