By 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 📊 Find out more: GartnerSYM
Gartner expects that by 2025, 70% of organizations will have operationalized AI architectures due to the rapid maturity of AI orchestration initiatives.
For 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. The result of combining those techniques is a composite AI system that solves a wider range of business problems in a more efficient manner.
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. For example, a data scientist might overlook that a number of clicks on the website can be discriminatory against age. AI can perfectly classify a stereotypical Western wedding but be blind to the weddings in India and Africa.
Disruptions such as the COVID-19 pandemic are causing historical data that reflects past conditions to quickly become obsolete, breaking many production AI and ML models.
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