The idea is that this approach will make the tech available to companies that don't have large amounts of data but do have significant human expertise. The goal is to build AI solutions that need less data and energy to learn. This approach to AI combines various techniques to expand the level of knowledge representations and solve more business problems more efficiently. Here is a brief description of each type of AI, based on Gartner's hype cycle report. AI orchestration and automation platform.These six technologies are all in the 'innovation trigger' phase of the hype cycle and are expected to hit the plateau of productivity (the end of the hype cycle) within two to five years: Vashisth and Sicular also see an increased focus on minimum viable products and accelerated AI development cycles, which they see as an important best practice. Small and wide data approaches enable more robust analytics and AI, reduce organizations' dependency on big data and deliver more complete situational awareness.

Responsible AI includes explainable AI, risk management and AI ethics for increased trust, transparency, fairness and auditability of AI initiatives.

Multi-experience AI, composite AI, generative AI and transformers are examples of this trend. Innovation in AI means efficient use of all resources, including data, models and compute.AI orchestration and automation platforms (AIOAPs) and model operationalization (ModelOps) reflect this trend. Companies are looking to operationalize AI platforms to enable reusability, scalability and governance and speed up AI adoption and growth.Gartner analysts Shubhangi Vashisth and Svetlana Sicular wrote the report and identified these four AI mega trends: SEE: Salesforce rolls out AI-powered workflows, contact center updates in Service Cloud

More familiar technologies, such as edge AI, decision intelligence and knowledge graphs, are at the peak of inflated expectations phase of the hype cycle, while chatbots, autonomous vehicles and computer vision are all in the trough of disillusionment. Synthetic data, orchestration platforms, composite AI, governance, human-centered AI and generative AI are all in this early phase. That means that end users are looking for specific technology capabilities that current AI tools can't quite deliver yet. Gartner analysts found more innovations in the innovation trigger phase of the hype cycle than usual. Gartner analysts described 34 types of AI technologies in the report and also noted that the AI hype cycle is more fast-paced, with an above-average number of innovations reaching mainstream adoption within two to five years. Users want more than artificial intelligence can provide at the moment but those capabilities are changing fast, according to Gartner's Hype Cycle for Artificial Intelligence 2021 report.

Gartner hype cycle 2021