ML will completely change the face of various industries, as well as job profiles.
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While it offers a promising future, there are some inherent problems at the heart of ML and AI advancements that put these technologies at a disadvantage.
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1. Reasoning Power
One area where ML has not mastered successfully is reasoning power, a distinctly human trait.
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2. Contextual Limitation
If we consider the area of natural language processing (NLP), text and speech information are the means to understand languages by NLP algorithms.
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3. Scalability
Although we see ML implementations being deployed on a significant basis, it all depends on data as well as its scalability.
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4. Regulatory Restriction For Data In ML
Most tech companies have privatised data and these data are the ones which are actually useful for ML applications.
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5. Internal Working Of Deep Learning
This sub-field of ML is actually responsible for today’s AI growth. What was once just a theory has appeared to be the most powerful aspect of ML.