Machine learning is a concept which allows the machine to learn from examples and experience. The machines don’t write the codes. The data is fed in the generic algorithm. The algorithm that machine builds is the logic based on the given data. This interesting news came to us from Grand Canyon University in their article, “AI interns hit the gas on machine learning.”
Students at one university are embracing the emerging technology in the form of technology-powered scaled race cars. The 1/18th-scale mobiles teach reinforcement learning, a type of machine learning in which developers train machines to make a sequence of decisions. In this case, the artificial intelligence (AI)/machine learning interns are essentially teaching the cars how to drive autonomously, then racing them on a cloud-based 3D racing simulator before moving on to racing on a real-live track.
This is encouraging on many levels. Most organizations have little knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. If they learn how this works now, when they enter the workplace they can bring new knowledge to the organization. Knowledge that explains AI.
Melody K. Smith
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