Intelligent Data Understanding for AI
This presentation will discuss benefits and applications of “smart data” and intelligent data understanding for operationalizing AI. Smart data are labeled, tagged, and annotated data. The tags, labels, and annotations include content, context, uses, sources, and characterizations (patterns, features) associated with a whole data set or with individual data granules. Labels can be learned through machine learning, or applied by human experts, or proposed by non-experts when those labels represent cognitive human-discovered patterns and features in the data. Labels can be learned and applied in existing data lakes, in massive streaming data, and in sensor data (collected in devices at the “edge”). Consequently, intelligent data understanding thrives at the convergence of AI and IoT (Internet of Things). Labels are curated and stored with the data, thus enabling search, delivery, orchestration, and use of the data in AI applications, including data-driven decision-making and autonomous operations. Techniques that enable and benefit from smart data are data discovery, machine learning, knowledge graphs, semantic linked data, knowledge discovery, and knowledge management. Intelligent data understanding thus meets the needs for AI operations, which must devour streams of data – not just any data, but smart data – the right data at the right time in the right context.
Session ID: LT0915 Presentation Type: Live Session (Replay Available)
Date / Time: [Day 2] Tue. Sep. 15, 2020 @ 09:00 ET (US)
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