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Joaquim Cardoso MSc.
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September 12, 2023
What is the message?
The 2023 Gartner Hype Cycle for Artificial Intelligence (AI) highlights the significance of Generative AI innovations, which are transforming productivity for developers and knowledge workers.
Early adoption of these innovations can provide a competitive advantage and address challenges in AI utilization within business processes.
Additionally, the article emphasizes the critical technologies both fueled by and fueling Generative AI, underscoring the dynamic nature of AI advancements in the current landscape.
Key takeaways:
What is the central theme of the 2023 Gartner Hype Cycle for Artificial Intelligence (AI)?
- The 2023 Gartner Hype Cycle for AI focuses on identifying innovations and techniques within AI that offer significant benefits while also addressing limitations and risks. It emphasizes the importance of early adoption of these innovations to gain a competitive advantage and improve AI utilization within business processes.
What key area dominates discussions in the AI field, and why is it significant?
- Generative AI dominates discussions due to its ability to increase productivity for developers and knowledge workers.
- It has real-world applications, such as ChatGPT, which have prompted organizations to rethink their business processes and the value of human resources. This has pushed Generative AI to the “Peak of Inflated Expectations” on the Hype Cycle.
What are the critical technologies fueled by Generative AI?
- The critical technologies fueled by Generative AI include Artificial General Intelligence (AGI), AI engineering for enterprise-scale AI solutions, autonomic systems with autonomy, learning, and agency, cloud AI services for model building and deployment, composite AI for improved learning efficiency, computer vision for analyzing real-world images and videos, data-centric AI for enhanced data quality, privacy, and scalability, edge AI for embedding AI in non-IT products, intelligent applications with autonomous responses, model operationalization (ModelOps), prompt engineering for specifying AI model responses, smart robots for autonomously executing physical tasks, and synthetic data generation.
What innovations will fuel the advancement of Generative AI?
- Innovations that will fuel Generative AI advancement include AI simulation for developing AI agents in simulated environments, AI trust, risk, and security management to ensure model governance, fairness, reliability, and data protection, causal AI for identifying cause-and-effect relationships, data labeling and annotation to enrich data, first-principles AI (FPAI) that incorporates physical principles into AI models, foundation models trained on diverse datasets, knowledge graphs representing physical and digital worlds, multiagent systems composed of interactive agents, neurosymbolic AI combining machine learning and symbolic systems, and responsible AI for ethical AI development and operation. These innovations are driving the exploration of Generative AI.
What are some notable trends in the AI landscape mentioned in the article?
- Notable trends in the AI landscape include the rapid acceleration of Generative AI exploration, the emergence of technology vendors forming Generative AI groups, the growth of startups focusing on Generative AI innovation, and the evaluation of the impacts of Generative AI by some governments with plans to introduce regulations. These trends indicate the increasing importance and adoption of Generative AI in various industries.
DEEP DIVE
What’s New in Artificial Intelligence from the 2023 Gartner Hype Cycle
Innovations in and around generative AI dominate and have transformative impact
Gartner
Afraz Jaffri and Svetlana Sicular. Contributor: Lori Perri
August 17, 2023
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.
About the Authors
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.
Originally published at https://www.gartner.com
