Throughout 2023, generative artificial intelligence (AI) has dominated discussions on AI. However, hype around this technology has reached a peak. Data and analytics leaders must stay on top of generative AI (GenAI) trends and track the trajectory of innovations to create credible cases for investment.
For example, generative AI has 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.
There are two sides to the generative AI movement on the path toward more powerful AI systems: innovations that will be fueled by GenAI, and 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.
- Cloud AI services provide AI model building tools, APIs for prebuilt services and associated middleware that enable the building/training, deploymentm 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.
- 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.
- 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.
- 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 are aggressively experimenting with generative AI.
Technology vendors are forming 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 this is expected 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.
- 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.
- 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.
Generative AI has the potential to impact organizations like no other technology in the past decade. Data and analytics leaders must leverage these trends to prepare their AI strategy for the future and utilize technologies that offer high impact in the present.
Afraz Jaffri is a Director Analyst at Gartner where he advises Data and Analytics leaders on making the most from their investments in machine learning analytics platforms. Gartner analysts will provide additional analysis on emerging technologies such as AI, as well as share advice for data and analytics leaders at Gartner IT Symposium/Xpo, taking place October 16-19, in Orlando, FL.