Two dominant themes emerge from the combination of 30 diverse AI technologies in this year’s Hype Cycle. These and many other new insights are from the Gartner Hype Cycle for Artificial Intelligence, 2020, published on July 27 th of this year and provided in the recent article, 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020. Five new technology categories are included in this year’s Hype Cycle for AI, including small data, generative AI, composite AI, responsible AI and things as customers.AI projects continue to accelerate this year in healthcare, bioscience, manufacturing, financial services and supply chain sectors despite greater economic & social uncertainty.30% of CEOs own AI initiatives in their organizations and regularly redefine resources, reporting structures and systems to ensure success.47% of artificial intelligence (AI) investments were unchanged since the start of the pandemic and 30% of organizations plan to increase their AI investments, according to a recent Gartner poll.IT leaders are now turning to new analytics techniques known as “small data” and “wide data.” Taken together, they are capable of using available data more effectively, either by working with low volumes of data or by extracting more value from unstructured, diverse data sources.īy 2025, Gartner expects that 70% of organizations will be compelled to shift their focus from big to small and wide data, providing more context for analytics and making AI less data-hungry. AI can perfectly classify a stereotypical Western wedding but be blind to the weddings in India and Africa. While algorithms can deduce race and gender from proxy parameters, such as typical female names or postal codes with the dominant racial demographics, more implicit bias is difficult to spot.įor example, a data scientist might overlook that a number of clicks on the website can be discriminatory against age.
Left unchecked, AI-based approaches can perpetuate bias leading to issues, loss of productivity and revenue. The more AI replaces human decisions at scale, the more it amplifies the positive and negative impacts of such decisions. The result of combining those techniques (among others) is a composite AI system that solves a wider range of business problems in a more efficient manner. Efficient use of data, models and computeĪs organizations continue to innovate in AI, they also need to efficiently use all resources - data, models and compute.įor example, composite AI is currently about combining "connectionist" AI approaches like deep learning, with "symbolic" AI approaches like rule-based reasoning, graph analysis, agent-based modeling or optimization techniques. It also offers a system for governance and lifecycle management of all AI (graphs, linguistic, rule-based systems and others) and decision models.
ModelOps reduces the time it takes to move AI models from pilot to production with a principled approach that can help ensure a high degree of success. Organizations should consider model operationalization (ModelOps) for operationalizing AI solutions. Gartner expects that by 2025, 70% of organizations will have operationalized AI architectures due to the rapid maturity of AI orchestration initiatives.