According to a report issued by the research firm Gartner, these trends include:
Issues such as increasing the degree of trust, transparency, fairness, and auditability of AI technologies continue to receive increasing interest from a wide range of stakeholders.
Therefore, adopting responsible AI helps achieve fairness, despite the presence of bias in the approved data, gain trust despite continued developments in transparency and accountability methods, and ensure regulatory compliance despite the probability-based nature of AI.
Rather, Gartner predicts that by 2023, teams overseeing AI development and training will have to demonstrate expertise in responsible AI.
Extensive and limited data:
Data is the foundation of successful AI initiatives. Limited and broad data methodologies can provide more sophisticated analytics and AI, reduce organizations’ reliance on big data, and provide a more complete and informed awareness of the current state.
According to Gartner, by 2025, nearly 70 percent of organizations will be convinced to shift their focus from big data to limited, broad data, allowing more contexts for analysis and making AI less data-intensive.
Activate artificial intelligence platforms:
The urgent and urgent need to benefit from artificial intelligence for the digital transformation of business reinforces the need to activate artificial intelligence platforms, and this requires moving artificial intelligence projects from the concept stage to the production stage, allowing reliance on artificial intelligence solutions to solve problems at the enterprise level.
A Gartner study showed that only half of AI projects make it past the experimental stage to the production stage, and that those that do make it take up to nine months to do so.
Innovations such as AIOAP automation and coordination platforms and ModelOps enable reusability, scalability, and governance, contributing to the growth and acceleration of AI adoption.
Effective use of resources:
Given the size and complexity of data, models, and computing resources used in the implementation of artificial intelligence projects, innovations in the field of artificial intelligence require optimal exploitation of these available resources with the highest levels of efficiency.
Also, some concepts related to artificial intelligence, such as multiplicity of experiences, synthetic intelligence, artificial intelligence generators and converters, are receiving increasing interest in artificial intelligence markets, due to their capabilities in finding solutions to a wide list of problems facing business, and in more effective ways.
Innovations such as edge AI, computer insights, intelligent decision-making, and machine learning are set to transform markets in the coming years.”