The future of Artificial Intelligence (AI) and machine learning is intertwined with knowledge graphs. Knowledge graphs can connect with and contribute to machine learning for better AI.
The future of Artificial Intelligence (AI) and machine learning is intertwined with knowledge graphs. Knowledge graphs can connect with and contribute to machine learning for better AI.
Machine learning requires a lot of cycles to train, classify, and report. By connecting machine learning with a knowledge graph, the need to relearn all the time, and the overall computational costs can be reduced. Besides, knowledge graphs help leverage inferencing in many ways that are difficult to do in a relational data system.
Moreover, the heuristic itself is a graph, which can be analyzed, encoded, and referenced. Graphs, in general, provide a means of making recommendations self-documenting, thereby making it easier to trust the results of AI algorithms. Knowledge graphs can help visualize the causal chains that led to a classification. This can go a long way in overcoming the contextual problem - understanding why a given resource was classified- that has been plaguing both AI and machine learning.
Furthermore, knowledge graphs offer an alternative approach to describing neural networks. Using graphs, particularly property graphs, properties that are essentially parameterized over time or that may be passed as functional results from inbound edges can be created. This alternative approach has the effect of being able to make inferences based upon changing conditions over time.
AI is undoubtedly going to drive the next wave of competitive advantage by creating connected enterprises. Graphs, ultimately, are about connections and context. They underlie contracts and news, research and entertainment, history, and how the future is shaped. Furthermore, graphs promise a means of generating knowledge, creating new models, and even learning and it will ultimately drive the next wave of competitive advantage through AI.
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