Artificial intelligence (AI) is becoming a major driver of digital business, according to a new report from Gartner. The future Of AI is very bright for a new generation
This year some of the mainstream applications of AI have started to go mainstream. With the convergence of factors like big data, new machine learning techniques, and cloud computing, AI has been able to break through and make its way into many people’s lives.
Trend Future Of AI
Assessing the trends that will shape the future of AI in business, Gartner identifies five as the most important. Technology leaders must capitalize on these emerging AI trends to democratize and promote responsible AI use, adapt composite techniques, leverage AI for real-time analytics at the edge, and capitalize on its generative powers.
AI that has been democratized will be accessible to a broad range of users. From uncovering unexpected opportunities to augmenting and assisting workers in supplementing their knowledge, democratized AI will impact every aspect of our lives.
The ability to deploy a variety of AI techniques that are aligned with the appropriate use cases is referred to as composite AI. It extends the power of AI to a broader set of use cases and users, and it is gaining popularity because it improves AI’s versatility, efficiency, and adaptability.
Edge AI will enable digital moments by bringing AI closer to data sources for real-time analytics. Gartner predicts that by 2025, more than half of all deep neural network data analysis will take place at the edge, up from less than 10% in 2021.
Responsible AI is an umbrella term for making sound business and ethical decisions when implementing AI. It necessitates taking into account business and societal value; risk; trust; transparency; fairness; bias reduction; explainability; accountability; safety; privacy; and regulatory compliance. Responsible AI is becoming increasingly important as regulatory oversight, consumer expectations, and emerging sustainability goals increase.
Generative AI is the use of artificial intelligence to create novel artifacts and products. To date, generative AI efforts have concentrated on producing media content such as photorealistic images of people and objects, but it can also be used for code generation, the generation of synthetic tabular data, and the design of pharmaceuticals and materials with specific properties.