2022 Artificial Intelligence Technology Hype Cycle: Compound artificial intelligence and decision-making intelligence will become mainstream technologies in less than 5 years

According to Gartner's "2022 Artificial Intelligence Technology Hype Cycle" report, the early adoption of AI technologies such as compound artificial intelligence (AI) and decision intelligence will bring significant competition to enterprise organizations. Advantages, alleviate problems caused by the fragility of AI models, help capture business background information, and promote value realization.

The technology has entered the mature stage of production, and its actual benefits have been proven and recognized. As more and more organizations believe that risks have dropped to an acceptable level, the adoption of AI technology has begun to enter a stage of rapid growth (see Figure 1).

AI has always been a hot topic of discussion among enterprises, governments and society. It is difficult for enterprise organizations to distinguish which AI technologies have real business value. Data and analytics (D&A) leaders need to develop a forward-looking AI strategy and take advantage of current technologies that can have a significant impact.

Figure 1. Artificial intelligence technology maturity curve in 2022

Figure 1. Artificial intelligence technology maturity curve in 2022

Source : Gartner (July 2022)

Synthetic Data

One of the main issues facing the development of AI today is that obtaining and labeling real data in order to effectively train AI models will cause problems for enterprises. Institutions carry a heavy burden. This is time-consuming and expensive, but this problem can be remedied by synthetic data. In addition, synthetic data also plays a vital role in removing personally identifiable information (PII).

causal artificial intelligence

The ultimate value of artificial intelligence is to improve the level of human action. Machine learning (ML) methods make predictions based on statistical relationships (correlations), whether or not these relationships constitute causation. Causal AI can play a crucial role when a more formalized determination of which best actions contribute to a specific outcome is needed. This method can improve the autonomy, explainability, robustness and efficiency of artificial intelligence technology.

Decision Intelligence

Decision Intelligence is a practical technology designed to improve decision-making by accurately understanding the decision-making process and how to evaluate, manage and improve the results based on feedback. Currently, as artificial intelligence technology is increasingly used in decision-making, automatic decision-making and augmented intelligence are hotly discussed. This trend is pushing decision-making intelligence into a period of inflated expectations. Recent crises have revealed the fragility of business processes, and decision intelligence methods and technologies will play an important role as organizations restructure their business processes and increase resilience, adaptability and flexibility. The decision-making intelligence market relying on a variety of software technologies is rapidly emerging and has begun to provide solutions for decision-makers.

Compound artificial intelligence

The premise of compound artificial intelligence is that no artificial intelligence method can solve all problems. Currently, composite artificial intelligence combines methods from the "connectionist" school (such as machine learning) with methods from the "symbolism" school (such as rule-based reasoning, graph analysis, agent-based modeling and optimization techniques, etc.) , aiming to reduce the data and energy required when artificial intelligence solutions learn, so that abstraction mechanisms can play a greater role. Compound artificial intelligence is the core factor driving the rise of the decision-making intelligence market.

Generative Artificial Intelligence

The exploration of generative artificial intelligence methods is currently heating up, and has begun to be used in life sciences, medical care, manufacturing, materials science, media, entertainment, automobiles, aerospace, The defense and energy industries have proven their worth. Generative AI has given rise to creative work in marketing, design, architecture, and content. Synthetic data generated by the technology can improve the accuracy and speed of AI delivery. The use of generative artificial intelligence is becoming increasingly common, and the types of products on the market are becoming more and more diverse. This technology has recently been actively used in the metaverse field.

Basic Model

The basic model comes with a large number of pre-trained data sets and can be applied to a wide range of use cases. It is a major advancement in the development of the field of artificial intelligence. The base model delivers more advanced natural language processing capabilities more efficiently than previous models. The base model has become the architecture of choice in natural language processing, which also supports computer vision, audio processing, software engineering, biochemistry, finance, and legal use cases.

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