What’s New in Artificial Intelligence from the 2022 Gartner Hype Cycle
AI innovations fall into four categories
The wide range of AI innovations is expected to impact people and processes within and outside an enterprise context, making them important to understand for many stakeholders, from business leaders to the enterprise engineering teams tasked with deploying and operationalizing AI systems.
Data and analytics (D&A) leaders have the most to gain, however, from using the Hype Cycle outlook to craft their AI strategies for the future and use technologies that offer high impact in the present.
The AI innovations on the Hype Cycle reflect complementary and sometimes conflicting priorities across four main categories:
Data-centric AI
The AI community has traditionally focused on improving outcomes from AI solutions by tweaking the AI models themselves, but data-centric AI shifts the focus toward enhancing and enriching the data used to train the algorithms.
In addressing AI-specific data considerations, data-centric AI disrupts traditional data management, but organizations that invest in AI at scale will evolve to preserve evergreen classical data-management ideas and extend them to AI in two ways:Add capabilities necessary for convenient AI development by an AI-focused audience that is not familiar with data management.
Use AI to improve and augment evergreen classics of data governance, persistence, integration and data quality.
Innovations in data-centric AI include synthetic data, knowledge graphs, data labeling and annotation.
Synthetic data , for example, is a class of data that is artificially generated rather than obtained from direct observations of the real world. Data can be generated using different methods, such as statistically rigorous sampling from real data, semantic approaches and generative adversarial networks or by creating simulation scenarios where models and processes interact to create completely new datasets of events.
Adoption is increasing across various industries, along with use in computer vision and natural language applications, but Gartner predicts a massive increase in adoption as synthetic data:
Avoids using personally identifiable information when training machine learning (ML) models via synthetic variations of original data or synthetic replacement of parts of data
Reduces cost and saves time in ML development as it is cheaper and faster to obtain
Improves ML performance as more training data leads to better training outcomes
Model-centric AI
Despite the shift to a data-centric approach, AI models still need attention to ensure the outputs continue to help us to take better actions. Innovations here include physics-informed AI, composite AI, causal AI, generative AI, foundation models and deep learning.
Composite AI refers to the fusion of different AI techniques to improve the efficiency of learning and broaden the level of knowledge representations. Since no single AI technique is a silver bullet, composite AI ultimately provides a platform to solve a wider range of business problems in a more effective manner.
Expected to reach mainstream adoption in two to five years, the business benefits of composite AI are likely to be transformational, enabling new ways of doing business across industries that will result in major shifts in industry dynamics. For example, composite AI:
Brings the power of AI to a broader group of organizations that do not have access to large amounts of historical or labeled data but have significant human expertise
Helps to expand the scope and quality of AI applications (that is, more types of reasoning challenges can be embedded)
Causal AI includes different techniques, like causal graphs and simulation, that help uncover causal relationships to improve decision making.
