3 Reasons You Should Not Rely on Robots for Data Science

3 Reasons You Should Not Rely on Robots for Data ScienceIn terms of technological advancement, we have developed a lot. Even digital marketing has also transformed. Artificial intelligence is one of the factors that is taking over almost everything a human being can do. Though it can be good sometimes, but not every time as it limits people’s knowledge and learning.

Ans when you are working in the world of data science, deep learning is highly essential. You cannot completely depend on technology and robots for data science. If you give more importance to robots than people for data science, you should stop it immediately. Here are the reasons why.

  • We Distribute the Work

Deep learning is one of the most advanced forms of machine learning and can easily realize in data complex patterns with different statistical and algorithmic techniques. This can also recognize features of images and data sets at times. Though it is really good and advanced, still deep learning and much of data science are limited to limited tasks. Along with numerous features, deep learning has some drawbacks also like these can be easily fooled. So to get maximum benefits from the technology, you should know the right ratio between technological work and manual work.

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  • We Offer Context

Cars are really comfortable and convenient for long journeys, but can these work without your direction? Same is the case with deep learning and data science. You need to provide context, frame the problem and create the hypothesis to ensure right application of data science. The only human can define the problem, understand important tasks and affirm that the system is functioning as per the expectations. No matter how advanced is the system, they don’t know when to turn themselves off. So, you need to monitor, improve and control the systems.

  • Buying Deep Learning is not Possible

Though there are companies trying hard to buy deep learning to automate the tasks. Toyota is one of them, investing a billion dollars in finding out how deep learning can help them design driver-less cars, but it is still going to take time. Even if you will be able to buy deep learning, you can stand out of the crowd only when you know where to draw a line between these automatic tools and manual work by understanding their limits.

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Data science and machine learning have bright future with huge potential. Nevertheless, you must understand that machine learning still needs human guidance and supervision to get desired success.

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