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  • Ravindra Patel
Ravindra Patel
Campbellsville University

Corresponding Author:[email protected]

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There has been a lot of hype about machine learning. The advent of big data, cloud computing, and the Internet of Things, together with powerful ML tools, have unleashed a wave of excitement around the benefits of learning. Unfortunately, the results have not always been that impressive. We need better algorithms, better data, more effective models, more powerful computing, and faster and smarter data access to get the most from these techniques. Here we present five challenges to the future of ML. As the AI and big data industries grow, we are witnessing an increasing demand for ML engineers. Machine Learning is fast becoming a core skill for every digital leader and will remain so for the foreseeable future. ML can transform data into knowledge. This is something organizations need to create insights from data. In this paper, we discuss the potential of using machine learning and big data to predict the future of intelligence. We take inspiration from the many projects in the field that are already making excellent progress and explore the possibility of creating a large repository of knowledge which could be used to build an AI machine capable of self-learning and thus be forever evolving. We explore the question of what the future might hold for artificial intelligence and machine learning, and whether it could one day replace us.