Artificial Intelligence & Machine Learning are the buzzwords today. Technologies are constantly evolving with advancements in Machine Learning. For instance, facial recognition in mobile phones, automated cars, language translation, etc. is all possible due to the massive developments in computer vision and Natural Language Processing (NLP). In this blog, we discuss the future of Machine Learning.
What we watch in sci-fi movies is a reality today and the future only seems more interesting. The advancements we have accomplished with artificial intelligence and machine learning are something no one could predict years ago.
Computer Vision
Machine Learning is predicted to grow exponentially in the coming times. This means computer vision will also have massive developments. Computers are anyway far more advanced than humans in carrying out tasks and analyzing data (texts, numerics, or images).
Also, the error rate in computer vision has gone from 26% in the year 2011 to 3% in the year 2016. This shows huge improvements. This has been possible only because of machine learning. For instance, a computer will take only minutes to analyze a picture and give out meaningful insights whereas it would take much longer for humans to perform the same task.
Natural Language Processing (NLP)
NLP has algorithms that help machines to understand texts, analyze them, and translate them. Huge progress has also been made in natural language processing models. NLP helps computers to understand texts better than they could ever before. However, you still can’t expect a computer to read a book and gather the same meaning as humans do.
Machine Learning developments like NLP has made computers understand languages and make accurate translations. For instance, Google uses the NLP model BERT for various search algorithms. This has improved search results for various search inputs that were not possible earlier. The Google search engine is now capable of giving out more accurate results to different search queries by users.
Challenges in Machine Learning
Although, Machine Learning developers use various atomic models, yet these models mostly remain inefficient. Moreover, the computation is expensive and requires major work to achieve the desired results. Studies suggest, that Machine Learning developers have to select the accurate datasets to train the model. Only then the model will have the ability to carry out tasks.
Machine Learning in Coming Times
Machine Learning is a great technology that is capable of doing multiple functions. In the future, AI and ML developers need to concentrate on Machine Learning implementation that does not require specific models for specific tasks. There is a need for one large model with the capability to perform various tasks. The focus should be directed towards the structure of the ML models and the scalability of Machine Learning platforms.
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