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Why IoT Needs Machine Learning to Thrive
There’s an unceasing buzz around big data and AI, the opportunities and threats of these technologies and concerns about their future. Meanwhile, companies are installing more and more sensors hoping to improve efficiency and cut costs. However, machine learning consultants from InData Labs say that without proper data management and analysis strategy, these technologies are just creating more noise and filling up more servers without actually being used to their potential. Is there a way to convert simple sensor recordings into actionable industrial insights? The scope of ML is to mimic the way the human brain processes inputs to generate logical responses. If people rely on learning, training or experience, machines need an algorithm. Also, as each of us learns more, we adapt our reactions, become more skilled and start to apply our efforts selectively. Replicating this self-regulatory behavior in machines is the finish line of ML development.
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