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Use Cases for Intelligent Agent Systems
Kence Anderson's book teaches how to combine early AI with deep learning for real-world decision-making. It provides a practical framework for designing autonomous AI without complex neural network manipulation, making it useful for engineers, data scientists, and students.
Machine Teaching is engineering for decision-making AI. Just like other engineering disciplines apply pure science to real world problems, Machine Teaching creates engineered intelligence that uses tried-and-true principles to solve tough problems and outpace performance benchmarks for high-value enterprise processes.
Machine teaching is a way to create powerful intelligent agents that can solve real problems and outperform the current methods of getting things done.Using the machine teaching framework and the Composabl platform, engineers can design and build agents, train them on a simulation of the real world task, and compare their performance to choose the best option.
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