The message from Google’s engineer Jeff Dean was loud and clear during his keynote earlier this year at a conference dedicated to web search and data mining. If you are not using deep learning already, you should be. However, machine learning have been limited to people within the industry up until now, but looking forward – this might change. 

Over the past five years, machine learning have grown from being accessible to only a limited amount of people, to reaching out to the “nonexperts”, the “common man”, Harvard Business Review writes. This growth has broadened the reach of machine learning, enabling its benefits to be shared to the masses.

“For the first time in history it’s possible, for example, for a person with knowledge of programming but no machine learning experience to create in one afternoon a neural network that can read handwritten digits” Harvard Business Review.

When looking at it from a historical perspective, software developers have struggled with entering machine learning, as software libraries are designed for the more academic researchers rather than software engineers and there have been a lack of sufficient data, HBR writes.

“With massive increases in the data being generated and stored by many applications, though, the set of companies with data sets on which machine learning algorithms could be applied has significantly expanded”.

As a result to a surge in machine learning frameworks that are commercially usable, such as scikit-learn Python library and well-publicised releases of libraries like Tensorflow by Google and CNTK by Microsoft Research, anyone interested in machine learning is required to understand the science of deep learning algorithms.

“Tutorials and public code exist for applications as diverse as AI-driven art generation, language translation, and automated image captioning. The accessibility of this code creates a virtuous cycle”, HBR writes.

The effect of this is that the demand for systems that are easier to use increases due to the interest of the “nonexperts” too, which again uncovers new applications of machine learning, that leads to further research and development by experts.

This has also created a change in the employment patterns of people working in machine learning as well, as exceptional quantitative skills are still important, whereas direct education in machine learning itself have become less essential.

HRW states that: “These changes have not simply made software developers more efficient; they have allowed a much broader set of people to develop software and start software companies. Software bootcamps now train working engineers in a matter of months, and startups can turn ideas into products in a few development cycles”.

This means that by making the technology more accessible for everyone, it will not affect the experts, but it will introduce the “nonexperts”, which will contribute to an increase in the industry, and hopefully new and innovative information.

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