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What Will Shape the Future of Machine Learning in 2018?

Guest blog post by Chirag Tumar.

Any new technology is not successful until it is embraced and used to its potential. Machine learning is no exception to this rule and its success or to say its ability can be gauged by the trends that exist. Machine learning is already a hot technology at the moments and it seems to have a promising future. At present, it seems to be an evolutionary phase where remarkable developments are expected.

This brings us to the thought of what the machine learning of future will be like. Don't worry anything like a Hollywood flick is happening here, where machines will outsmart humans and capture Earth. This blog will delve deeper into the aspects that will shape the future of machine learning.

Investments and Developments

A quick research on the investments made by top world companies in machine learning will make you believe that the future holds a lot of innovation in the field of machine learning. Global pioneers like Google, Amazon, etc. have placed their bets on machine learning and are working on creating future-ready solutions that will be more efficient and provide the better experience.

These developments can mark the beginning of a new era of machine learning which will simplify the most complex tasks when dealing with big data. It can also mean replacing humans with bots which have been something that we have been looking forward to for a long time and it just seems closer to reality. If media reports are to be believed, self-driven cars are being tested and that would be a leap forward towards machine learning of the future.

Dependency and Expectations

Another vital aspect that will shape the future of machine learning is the dependency that businesses will have on it. A considerable number of businesses are already using machine learning and enjoying the benefits of enhanced productivity, better revenues, informed decisions, etc. These advantages make companies rely on machine learning and look forward to the advances that are being made.

With passing time, more and more organizations are going to embrace machine learning and the expectations will rise. The expectations are not limited to progress in terms of algorithms but also compatibility with other technological advances like cloud, web, etc. It is the blend of compatibility and advances that will shape the true future along with the efforts of investment and development firms.

Interaction and Comfort

Any solution that is complex for humans to operate does not really last long and is soon replaced by other technologies are easier to use. After all, that is what machines are made for. Isn’t it? This is another important aspect that plays a crucial role in how the future will be for a technology. Machine learning gives that comfort to humans and makes their life simple. The future will also have many competing technologies, therefore, machine learning has to evolve at a faster pace and establish itself as a trusted choice.

Whether you are using supervised or unsupervised machine learning which does not matter. The point is that solutions that are offered should minimize your effort maybe by reducing the interaction time or requirement. This calls for a better technology. However, human involvement cannot be completely ruled out because of the vulnerability of the machine learning solutions. The dependency on data and insights provided might require a human validation before commencing actions based on results.

In all consensus, whatever we have read about machine learning points toward a bright future that is to be shaped by the market expectations which are catered to by companies that develop and promote machine learning. While these factors contribute to the growth and shape the future, the interaction between machine and human and the comfort that it brings will make it stay. 

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Comment by Andres Urrego on November 21, 2017 at 1:39pm

Your example about comfort fits enough with what happen or is going thru with map reduce jobs. The idea is create approaches enough easy and highly performed then a new broad of tools that already include map reduce were created. Nice article 

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