What's new in artificial intelligence from the 2022 Gartner Hype Cycle (2023)

The 2022 Gartner Hype Cycle™ for Artificial Intelligence (AI) identifies must-know innovations in AI technology and techniques that go beyond everyday AI already being used to make previously static business applications, devices, and productivity tools smarter .

"It is worth noting that the AI ​​hype cycle is full of innovations that are expected to deliver high or even transformative benefits," said Afraz Jaffri, director analyst at Gartner. “Pay special attention to innovations that are projected to enter the mainstream in two to five years, including compound AI, decision intelligence and edge AI. Adopting these innovations early can create significant competitive advantage and business value, and solve issues related to the fragility of AI models.”

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The wide range of AI innovations is expected to impact people and processes both inside and outside of a corporate context, making it important for many stakeholders to understand, from business leaders to the engineering teams of companies that are tasked with the provision and operationalization of AI systems.

data and analyticsHowever, (D&A) leaders can benefit the most by using the hype cycle perspective to develop their AI strategies for the future and deploy technologies that are making a big impact in the present.

The AI ​​innovations in the Hype Cycle reflect complementary and sometimes conflicting priorities in four main categories:

1. Data-centric AI

2. Model-centric AI

3. Application-centric AI

4. Human-centric AI

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ThatKI-Communityhas traditionally focused on improving the outcomes of AI solutions by optimizing the AI ​​models themselves, butdata-centricAI shifts the focus to improving and enriching the data used to train the algorithms.

When dealing with AI-specific data considerations, data-centric AI is disrupting traditional data management, but organizations investing in AI at scale will evolve to preserve evergreen classic data management ideas and extend them to AI in two ways:

• Add features required for convenient AI development by an AI-centric audience unfamiliar with data management.

• Use AI to improve and extend the evergreen classics of data governance, persistence, integration and data quality.

Innovations in data-centric AI include synthetic data, knowledge graphs, data labeling, and annotation.

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synthetic data, for example, is a class of data that is artificially generated rather than obtained from direct real-world observations. Data can be generated using various methods such as B. statistically rigorous sampling from real data, semantic approaches and generative adversarial networks or by creating simulation scenarios in which models and processes interact to create entirely new datasets of events.

Adoption is growing across multiple industries, along with use in computer vision and natural language applications, but Gartner predicts a massive increase in adoption as synthetic data:

• Avoids using personally identifiable informationwhen training machine learning (ML) models by synthetic variations of original data or synthetic replacement of parts of data

• Reduces costs and saves time in ML developmentsince it is cheaper and quicker to get

• Improves ML performancesince more training data leads to better training results

Despite shifting to a data-centric approach, AI models still need attention to ensure the results continue to help us take better action. Innovations here include physically informed AI, composite AI, causal AI, generative AI, fundamental models, and deep learning.

Composite AIrefers to the merging of different AI techniques to improve learning efficiency and expand the level of knowledge representation. Because no single AI technique is a silver bullet, composite AI ultimately provides a platform to more effectively solve a broader range of business problems.

It is expected to reach widespread adoption in two to five years, and the business benefits of composite AI are likely to be transformative, enabling new ways of doing business across industries that will lead to major shifts in industry dynamics. For example composite AI:

• Brings the power of AI to a broader set of organizations that don't have access to large amounts of historical or labeled data but have significant human expertise

• Helps expand the scope and quality of AI applications (i.e. more types of brain teasers can be embedded)

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Kausale KIincludes various techniques such as causal graphs and simulations that help uncover causal relationships to improve decision-making. Although it will take 5 to 10 years for causal AI to reach widespread adoption, the business benefits are expected to be high and new modes of performance are expected to enable horizontal or vertical processes that result in significant revenue increases or cost savings for an organization. Benefits of causal AI include:

• efficienciesfrom adding domain knowledge to bootstrap causal AI models with smaller datasets

• Greater decision expansionand autonomy in AI systems

• Better explainabilityby capturing easy-to-interpret cause-effect relationships

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• More robustness and adaptabilitythrough the use of causal relationships that remain valid in changing environments

• Reduced distortion in AI systemsby making causal relationships more explicit

Innovations here include AI engineering, decision intelligence, operational AI systems, ModelOps, AI cloud services, intelligent robots, natural language processing (NLP), autonomous vehicles, intelligent applications and computer vision.

Decision intelligence and edge AI are expected to reach widespread adoption and deliver transformative business benefits in two to five years.

decision intelligenceis used to a practical disciplineimprove decision makingthrough explicit understanding and engineering of how decisions are made and how outcomes are evaluated, managed and improved through feedback

Decision intelligence helps with:

• Reduce technical debt and increase visibility, and improve the impact of business processes by significantly improving the sustainability of organizations' decision-making models based on the power of their relevance and the quality of their transparency, making decisions more transparent and auditable

• Reduce the unpredictability of decision outcomesby correctly capturing and considering the uncertainty factors in the business context and making decision models more resilient

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Edge-KIrefers to the use of AI techniques embedded in Internet of Things (IoT) endpoints, gateways, and edge servers in applications ranging from autonomous vehicles to streaming analytics. Business benefits include:

• Improved operational efficiency, e.g. B. in the production of visual inspection systems

• Improved customer experience

• Reduced latency in decision making by using local analytics

• Reducing connectivity costs by reducing data traffic between edge and cloud

• Constant availability of the solution, regardless of network connectivity

This group of innovations includes AI Trust, Risk and Safety Management (TRiSM), responsible AI, digital ethics, and AI makers and teaching kits.

When AI replaces human decisions, it amplifies both good and bad outcomes.Responsible AIenables the right outcomes by solving dilemmas based on delivering value and tolerating risk. Responsible AI is an umbrella term for aspects of reasonable business conduct andethical decisions when introducing AI, including business and societal value, risk, trust, transparency, fairness, prejudice reduction, explainability, accountability, security, privacy and regulatory compliance. Responsible AI will take 5 to 10 years to reach mainstream adoption, but will ultimately have a transformative impact on business.

Digital ethics is a short-term trend (two to five years) that is likely to have a major impact on business. Digital ethics encompasses the value systems and moral principles governing the behavior of electronic interactions between people, organizations and things. These issues, particularly those related to privacy and bias, remain a concern for many. People, increasingly aware that their information is valuable, are frustrated by a lack of transparency, abuse and violations. Organizations are acting to mitigate risk in managing and securing personal information, while governments are introducing tougher laws.

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Many organizations still ignore digital ethics because they think they don't apply to their industry or field, but Gartner predicts that by 2024, 30% of large organizations will use a new "Voice of Society" metric to measure up respond to societal issues and assess the impact on their business success. Organizations need to integrate digital ethics into their AI strategies to increase their impact and reputation with customers, employees, partners and society.

In summary:

• The 2022 Gartner Hype Cycle™ for Artificial Intelligence features "must-know" innovations that are expected to bring significant benefits to any organization.

• These innovations go beyond everyday AI techniques already being used to add intelligence to previously static business applications, devices, and productivity tools.

• Watch early for innovations that are projected to enter the mainstream in two to five years, including compound AI, decision intelligence, and edge AI.

Afraz Jaffriis Research Director at Gartner, where Mr. Jaffri focuses on analytics, data science and AI. He advises data and analytics leaders on making the most of their investments in modern data science, machine learning, and analytics platforms.