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5 Machine Learning Trends in 2018

Posted by Sales

Jul 19, 2017 11:01:00 PM

Machine Learning Trends 2017

Machine Learning has been around for a long time empowering search engines and smartphones. Machine learning aims at developing powerful algorithms that can self-learn and improve when are exposed to data. Combined with artificial intelligence, machine learning can offer great potential for developments in the fields of Big Data and General Artificial Intelligence. In 2017, we have already seen a huge buzz around AI and ML. At the NGA (National Governors Association) Conference held this July, Elon Musk expressed his concerns about the potential threat these modern technologies possess on the human survival. Such is the hype about Machine Learning and Artificial Intelligence that from Google to Amazon to SpaceX all the major tech giants are focusing their entire R&D efforts to ensure the controlled development of these technologies.

Must Read: Best practices - Machine Learning models and applications

The 5 Machine Learning Trends in 2018:

1. Democratization of Machine Learning:

Tech giants like Google, Amazon, Microsoft and IBM are focusing on bringing machine learning technologies at the disposal of every developer in every country and company across the world.

  • In 2015, Google open-sourced TensorFlow (a set of machine learning libraries).
  • Under the Apache 2.0 license on GitHub, Amazon launched its Deep Scalable Sparse Tensor Network Engine (DSSTNE - pronounced ‘Destiny’)
  • Elon Musk has built OpenAI, a  non-profit AI research initiative to discover and enact the path to safe artificial general intelligence.
  • In 2010 IBM launched Watson Analytics (ML framework with cognitive computing abilities).

These number of open libraries for software developers are meant to ensure the contribution of every coder in Machine Learning. These resources coupled with cloud computing are enabling smaller companies and individuals to also learn and utilize these technologies. By scaling the number of users of ML, tech giants are doing their best to ensure the democratization of machine learning technologies.

2. Rise in Platform Wars:

Though we can count the number of competitive cloud solution providers on our fingers, how can we ensure that we choose the best solution? We have Amazon Web Services(AWS), Microsoft Azure, Google Cloud Platform, IBM Watson and maybe add Alibaba to the list as well. For beginners in the field of Data Science, it is very difficult to choose a platform that can satisfy their needs in the long run. The competitiveness in strategic marketing across multiple platforms will increase and so will the race to launch new innovations. We can already see the tech giants competing aggressively to show their strengths but it is certain that is there is more to come.

Must Read: Using IoT and Artificial Intelligence to improve customer satisfaction

3. Data Scientist Will Become the Hottest Job:

Data Scientist is already an in-demand job and with time this demand is going to skyrocket. There is a wide skill gap between the required skills and the market potential of job seekers. The geeky group of developers will find themselves at peace while the below average coders will have a hard time in finding a job. This ML Trend will also disrupt the education system as academicians will have to figure out courses to fill the ever widening demand and supply gap. Blog and forums like KDNuggets are taking educational initiatives but without a proper framework for education, hiring issues will remain to escalate.

4. Robotic Process Automation:

Machine Learning Trends in 2017 - RPA Creating an Impact

Robotic Process Automation is the automation of rule based tasks in software processes. These rule based tasks include CRM processes, data entry jobs etc. With the increase in cognitive abilities and more impactful machine learning algorithms and data sets, RPA will gain more and more users. Machine learning combined with artificial intelligence is slowly going to take over manual routine jobs across all industries.

Must Read: Machine Learning vs Predictive Analytics

5. Impact on Cyber-security:

Machine Learning Trends 2017 - Cyber Security

According to research by Accenture, an average organization faces 106 targeted cyber-attacks per year, with one in three of them resulting in a security breach. Cyber security is a major concern for all the companies across the world. The issue of increase in advanced cyber attacks is not going to resolve soon but if we can train our systems to learn from previous brute forces than we can surely attain better security. Machine Learning actually works both ways, if used by potential hackers than it can result in stronger attacks while if used by firms it can increase the level of security. Since these technologies are available for everybody it is best if we prepare our defenses in anticipation of the potential security threats.

As you have seen Machine Learning will gain a significant traction in 2018. Seeing the technologies evolving we can be sure that this trend will continue to rise with time. From small companies to MNCs all are able to channelize ML using cloud computing at a fraction of the cost that was actually incurred in building those systems. The access to business intelligence across the world will definitely disrupt the way businesses function.

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Topics: Machine Learning

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