The latest developments in artificial intelligence (AI) research always gets plenty of attention, but is it too much? This interesting topic came to us from Imperial Valley News in their article, “AI Researcher Offers Insight on Promise, Pitfalls of Machine Learning.”
Ranjeev Mittu heads NRL’s Information Management and Decision Architectures Branch and has been working in the AI field for more than two decades. He recently voiced concern about the near-obsession people have in the area of machine learning and deep learning. “The biggest limitation of deep networks is that a complete understanding of how these networks arrive at a solution is still far from reality,” said Mittu.
Machine learning and deep learning are two subsets of AI. Machine learning is a subset of AI involved with the creation of an algorithm which can modify itself without human intervention to produce desired output – by feeding itself through structured data. Deep learning is a subset of machine learning where algorithms are created and function similar to those in machine learning, but there are numerous layers of these algorithms – each providing a different interpretation to the data they feed on.
While the field of AI offers almost limitless potential for innovative solutions to today’s problems, they need to be taken with some critique and analysis, instead of at face value.
Melody K. Smith
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