According to Gartner, generative artificial intelligence has taken discussions about Artificial Intelligence (AI) to new levels, arousing interest among boards of directors and heads of state. Data and analytics leaders must keep abreast of trends and follow the trajectory of innovations to create credible investment cases.
However, there is a lot of euphoria surrounding AI. With so many emerging AI models and products on the market, it can be difficult to filter out all the noise and determine how to use AI safely, effectively, and strategically.
The Gartner® Hype Cycle™ for Artificial Intelligence, 2023 shows how it's possible:
- Gain a comprehensive understanding of the current AI landscape and make data-driven decisions when adopting AI.
- Assess the maturity of various AI technologies to mitigate risks.
- Use AI to support business growth and gain a competitive advantage.
Gartner identifies two sides of the generative AI movement on the road to more powerful AI systems:
The innovations that generative AI will power:
- Autonomic systems
- AI engineering
- Data-centric AI
- Composite AI
- Operational AI systems
- AGI
- Prompt Engineering
- Smart Robots
- ModelOps
- Edge AI
- Synthetic data
- Intelligent applications
- Cloud AI services
- Computer vision
The innovations that will fuel the advance of generative AI:
- First-principles IA
- Neuro-symbolic AI
- Multiagent systems
- Causal AI
- AI simulation
- AI TRiSM
- Responsible AI
- Foundation models
- Knowledge graphs
- Data labeling and annotation
These innovations are found in different parts of Gartner's Hype Cycle, a graphical representation of the phases of a technology's life cycle from initial development to commercial availability and adoption, as well as its eventual decline and obsolescence.
Compared to other Hype Cycles, the AI Hype Cycle has more innovations rated as having benefits in the high to transformational categories, with no innovation having a low or moderate benefit rating.
In this article, we'll show you some of the AI technologies that are on the rise and those that have already reached the peak of Gartner's Hype Cycle.
Rising technologies in the Hype Cycle
Autonomous systems are self-managing physical or software systems that perform tasks limited by a domain and their own decisions and tasks autonomously, without external assistance. Autonomous systems are increasingly important because they enable levels of adaptability, flexibility, and business agility that cannot be achieved with traditional AI techniques alone.
A multi-agent system (MAS) is made up of multiple, interactive agents capable of perceiving the environment and carrying out actions. Agents can be AI models, software programs, robots, and other computational entities. Multiple agents can work towards a common goal that exceeds the capacity of the individual agents, with greater adaptability and robustness. The combined application of multiple autonomous agents can solve complex tasks that individual agents cannot while creating more adaptable, scalable, and robust solutions.
Neuro-symbolic AI is a form of composite AI that combines machine learning methods and symbolic systems to create more robust and reliable AI models. This combination makes it possible to combine statistical patterns with explicitly defined rules and knowledge to give AI systems the ability to better represent, reason, and generalize concepts. This leads to more powerful, versatile, and interpretable AI solutions and allows AI systems to handle more complex tasks with human-like reasoning.
AI engineering is fundamental to the enterprise delivery of AI solutions at scale. The discipline unifies DataOps, MLOps, and DevOps pipelines to create coherent AI-based business development, delivery, and operational systems. Establishing consistent AI pipelines allows companies to develop, implement, adapt, and maintain AI models consistently, regardless of the environment.
AI simulation combines AI and simulation technologies to develop AI agents and simulated environments. It includes both the use of AI to make simulations more efficient and useful and the use of a wide range of simulation models to develop more versatile and adaptable AI systems. Simulation is used to make AI more robust and compensate for the lack of training data, and AI is used to make simulations more efficient and realistic.
Artificial general intelligence (AGI) is the intelligence of a machine that can perform any intellectual task that a human can perform. AGI is a characteristic attributed to future autonomous AI agents that can achieve goals in a wide range of real or virtual environments, at least as effectively as humans. Understanding the concept of AGI is crucial to guiding and regulating the future evolution of AI, although it is essential to manage realistic expectations.
AI operating systems enable the orchestration, automation, and scaling of production-ready, enterprise-grade AI. These systems integrate DataOps, ModelOps, MLOps, and deployment services to provide enterprise governance, eliminating integration friction and incompatibility between different platforms.
Technologies at the Peak of the Hype Cycle
Responsible artificial intelligence is a generic term that covers aspects related to making appropriate business and ethical decisions when adopting AI. Responsible AI enables the right outcomes, ensuring business value and mitigating risks. This requires a set of tools and approaches, including sector-specific methods adopted by suppliers and companies.
An intelligent robot is an AI-powered, often mobile machine designed to autonomously perform one or more physical tasks. These tasks can be based on machine learning, which can be incorporated into future activities or support unprecedented conditions. Interest in intelligent robots has been growing as companies seek to further improve logistics operations, support automation and augment human capacity in various jobs.
Base models are trained autonomously on large data sets, based on deep neural network architectures. They are called base models because of their critical importance and applicability to a wide variety of downstream use cases. This broad applicability is due to the pre-training and versatility of the models. Base models are an important step forward for AI due to their massive pre-training and broad applicability to use cases. They can deliver state-of-the-art capabilities more effectively than their predecessors.
Generative AI technologies can generate new derivative versions of content, strategies, designs, and methods by learning from large repositories of original content. Generative AI has a profound impact on companies, namely in the discovery, creation, authenticity, and regulation of content; in the automation of human work; and customer and employee experiences.
Technologies reaching the top of the Hype Cycle
Computer vision is a set of technologies that involve capturing, processing, and analyzing real-world images and videos to extract meaningful and contextual information from the physical world. Computer vision is driving innovation in many industries and use cases and is creating unprecedented applications and business opportunities.
Cloud AI services provide AI model creation tools, APIs for pre-built services, and associated middleware that enable the creation, deployment, and consumption of machine learning (ML) models running on pre-built infrastructures as cloud services. These services include vision and language services and automated ML to create models and customize pre-built models. Cloud AI services allow advanced machine learning models to be incorporated into applications that are used to run day-to-day business operations.
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