Machine Learning Predicted to Take the Stage Again in 2018

Machine learning and artificial intelligence will continue to improve and make waves in the big data industry. Autonomous machines that can learn without explicit programming is the future, many say, and experts are predicting that machine learning will continue to take center stage in a variety of ways in 2018.

1. Graph technology will link up with machine learning: We expect graph technology to become tightly linked with machine learning models and applications in any industry in 2018, given how much richer the potential training features can be. Graph techniques make it easier to generate lists of features from diverse data that can be used to train models which identify and qualify the most valuable signals from data. - Sean Martin, CTO and co-founder of Cambridge Semantics
2. In 2018, machine learning will be the ultimate weapon in the cloud wars: We are one major announcement away from the technology industry being pushed into a machine learning renaissance. 2018 will be a year of search and exploration of machine learning to determine how best to use it, and what things can be automated that you never thought possible before. Cloud will make machine learning pervasive and soon enough, it will be built into every application - used by everyone either directly or indirectly. - Ashley Stirrup, CMO, Talend
3. An increased focus on real-time analytics and machine learning will be seen in all areas of technology: If you don’t have real-time access to your analytics, it’s not too late, but you need to start now. Understanding security risks on the mainframe or IBM is movement toward access to information quicker. There’s also an aspect of machine learning when it comes to not only analytics, but to everything: people are increasingly understanding that AI can be applied to everything in their lives, from making their jobs easier to helping inform decisions. So Syncsort predicts that machine learning and AI will be much more prevalent in 2018 across all kinds of technology, from products to analytics to data quality and governance. It applies to everything. - Keith Kohl, VP of product management, Syncsort 
4. Machine learning is here but its impacts remain to be seen: There have been repeated predictions over the last couple of years touting a potential breakthrough in enterprise use of Artificial Intelligence and Machine Learning (ML). While there are no shortage of startups - CBInsights published an AI 100 selected from over 2000+ startups - the reality is that most enterprises have yet to see quantifiable benefits from their investments, and the hype has been rightly labelled as overblown. In fact, many are still reluctant to even start, with a combination of skepticism, lack of expertise, and most of all lack of confidence in the reliability of their data sets. - Ramon Chen, chief product officer, Reltio