What’s New in Artificial Intelligence from the 2023 Gartner Hype Cycle

August 17, 2023

Contributor: Lori Perri

Innovations in and around generative AI dominate and have transformative impact.

The 2023 Gartner Hype Cycle™ for Artificial Intelligence (AI) identifies innovations and techniques that offer significant and even transformational benefits while also addressing the limitations and risks of fallible systems. AI strategies should consider which offer the most credible cases for investment. 

“The AI Hype Cycle has many innovations that deserve particular attention within the two-to-five-year period to mainstream adoption that include generative AI and decision intelligence,” says Gartner Director Analyst Afraz Jaffri. “Early adoption of these innovations will lead to significant competitive advantage and ease the problems associated with utilizing AI models within business processes.”

Two types of GenAI innovations dominate

Generative AI is dominating discussions on AI, having increased productivity for developers and knowledge workers in very real ways, using systems like ChatGPT. This has caused organizations and industries to rethink their business processes and the value of human resources, pushing GenAI to the Peak of Inflated Expectations on the Hype Cycle.

Gartner now sees two sides to the generative AI movement on the path toward more powerful AI systems:

  • Innovations that will be fueled by GenAI.

  • Innovations that will fuel advances in GenAI.

Innovations that will be fueled by generative AI

Generative AI impacts business as it relates to content discovery, creation, authenticity and regulations. It also has the ability to automate human work, as well as customer and employee experiences. 

The critical technologies that fall into this category include the following: 

  • Artificial general intelligence (AGI) is the (currently hypothetical) intelligence of a machine that can accomplish any intellectual task that a human can perform.

  • AI engineering is foundational for enterprise delivery of AI solutions at scale. The discipline creates coherent enterprise development, delivery, and operational AI-based systems.

  • Autonomic systems are self-managing physical or software systems performing domain-bounded tasks that exhibit three fundamental characteristics: autonomy, learning and agency. 

  • Cloud AI services provide AI model building tools, APIs for prebuilt services and associated middleware that enable the building/training, deployment and consumption of machine learning (ML) models running on prebuilt infrastructure as cloud services.

  • Composite AI refers to the combined application (or fusion) of different AI techniques to improve the efficiency of learning to broaden the level of knowledge representations. It solves a wider range of business problems in a more effective manner.

  • Computer vision is a set of technologies that involves capturing, processing and analyzing real-world images and videos to extract meaningful, contextual information from the physical world.

  • Data-centric AI is an approach that focuses on enhancing and enriching training data to drive better AI outcomes. Data-centric AI also addresses data quality, privacy and scalability.

  • Edge AI refers to the use of AI techniques embedded in non-IT products, IoT endpoints, gateways and edge servers. It spans use cases for consumer, commercial and industrial applications, such as autonomous vehicles, enhanced capabilities of medical diagnostics and streaming video analytics.

  • Intelligent applications utilize learned adaptation to respond autonomously to people and machines.

  • Model operationalization (ModelOps) is primarily focused on the end-to-end governance and life cycle management of advanced analytics, AI and decision models. 

  • Operational AI systems (OAISys) enable orchestration, automation and scaling of production-ready and enterprise-grade AI, comprising ML, DNNs and Generative AI.

  • Prompt engineering is the discipline of providing inputs, in the form of text or images, to generative AI models to specify and confine the set of responses the model can produce. 

  • Smart robots are AI-powered, often mobile, machines designed to autonomously execute one or more physical tasks.

  • Synthetic data is a class of data that is artificially generated rather than obtained from direct observations of the real world.

Innovations that will fuel generative AI advancement

“Generative AI exploration is accelerating, thanks to the popularity of stable diffusion, midjourney, ChatGPT and large language models. End-user organizations in most industries aggressively experiment with generative AI,“ says Gartner VP Analyst Svetlana Sicular, . 

“Technology vendors form generative AI groups to prioritize delivery of generative-AI-enabled applications and tools. Numerous startups have emerged in 2023 to innovate with generative AI, and we expect this to grow. Some governments are evaluating the impacts of generative AI and preparing to introduce regulations.”

The critical technologies that fall into this category include the following: 

  • AI simulation is the combined application of AI and simulation technologies to jointly develop AI agents and the simulated environments in which they can be trained, tested and sometimes deployed.

  • AI trust, risk and security management (AI TRiSM) ensures AI model governance, trustworthiness, fairness, reliability, robustness, efficacy and data protection.

  • Causal AI identifies and utilizes cause-and-effect relationships to go beyond correlation-based predictive models and toward AI systems that can prescribe actions more effectively and act more autonomously.

  • Data labeling and annotation (DL&A) is a process where data assets are further classified, segmented, annotated and augmented to enrich data for better analytics and AI projects.

  • First-principles AI (FPAI) (aka physics-informed AI) incorporates physical and analog principles, governing laws and domain knowledge into AI models. FPAI extends AI engineering to complex system engineering and model-based systems

  • Foundation models are large-parameter models trained on a broad gamut of datasets in a self-supervised manner.

  • Knowledge graphs are machine-readable representations of the physical and digital worlds. They include entities (people, companies, digital assets) and their relationships, which adhere to a graph data model.

  • Multiagent systems (MAS) is a type of AI system composed of multiple, independent (but interactive) agents, each capable of perceiving their environment and taking actions. Agents can be AI models, software programs, robots and other computational entities.

  • Neurosymbolic AI is a form of composite AI that combines machine learning methods and symbolic systems to create more robust and trustworthy AI models. It provides a reasoning infrastructure for solving a wider range of business problems more effectively.

  • Responsible AI is an umbrella term for aspects of making appropriate business and ethical choices when adopting AI. It encompasses organizational responsibilities and practices that ensure positive, accountable, and ethical AI development and operation.

Afraz Jaffri is Director Analyst at Gartner and focuses on Analytics, Data Science and AI. He advises Data and Analytics leaders on making the most from their investments in modern data science, machine learning and analytics platforms.

Svetlana Sicular is VP Analyst at Gartner and focuses on the intersection of data and AI. She is convinced that a human plus AI is smarter than either by themselves. Ms. Sicular really cares about helping organizations achieve digital transformation by using AI to implement breakthrough business ideas.

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