Absolutely everyone in the tech industry is buzzing about artificial intelligence (AI) and machine learning (ML). AI and ML have become key areas of focus for global conglomerates and early stage start-ups alike, and the past few years have seen venture capitalists pour hundreds of millions of dollars into its development and refinement. There has been a 14-times increase in the number of active AI startups since the year 2000. According to surveys by Statista, 84% of companies believe investment in artificial intelligence and ML will lead them to greater competitive advantages.
Everything from your favorite search engine- Google, to your favorite video aggregator now uses machine learning to bring you customized and personal results like never before. Not just that, global revenues from AI for enterprise software is expected to reach $ 31.2 billion in 2025 at a CAGR 52.59% during the forecast period.
It is fair to say, every IT leader today is racing to recognize and keep up with the machine learning trends that will keep them ahead of the competition. With that in mind, here we list 3 trends that we think will rule 2018 in the machine learning and AI space.
1) Machines Teaching Themselves
Intelligent systems, powered by the latest AI software, are getting smarter faster than ever before. Previously it took manual data mining and feeding to the robots (or, bots) to be able to perform functions. Now that AI itself is capable of foraging the data, it makes sense that companies are investing in machine learning algorithms that are capable of teaching themselves and other connected systems.
If we consider data to be the gasoline that fuels machine learning, even for machines, some kinds of data are hard to come by due to cost/ security concerns. In these sort of cases, if machines can share experiences for each other to augment data, machine learning can become self-amplifying, and grow exponentially. We are already seeing versions of this on the way with Facebook building AI software that made up its own language, or Google’s own AI that can build another AI.
2) Growth of Edge Computing
Edge computing in IT refers to a method of optimizing cloud computing systems by reducing the distance of data processing to be near the source of the data. Edge computing effectively mimics a cloud by providing compatible services and endpoints that cloud-based applications can use.
Since, machine learning software are very dependent of the agility of a program to execute complex operations quickly, low-latency applications often struggled and lagged. Edge computing is expected to fill this gap as a solution, by providing developers with an easier way to deploying code.
3) Machine Learning for IT Operations Management (ITOM)
Most organizations struggle with various IT components producing massive piles of operational data viz. log files, errors, status reports, etc. While all this data can be helpful if utilized properly, sorting through and making sense of it all is complicated and time/ resource intensive manually.
Tech giants like enterprise software company ServiceNow have invested millions into perfecting their ‘intelligent automation engine’ that leverages powerful ML techniques. Machine learning and artificial intelligence helps IT operations teams in finding the root cause of issues with increased visibility and predictive analysis, and turn enterprise IT proactive, from a reactive state and greatly reduce the risk of service outages. ServiceNow can greatly improve your “availability” by keeping your service health optimized, and increase your “agility” by automating IT processes.
This has seen Gartner name ServiceNow a leader in the Magic Quadrant (MQ) for enterprise high-productivity application “Platform-as-a-Service (PaaS)” market. ServiceNow is a powerful product and can do many things. It can streamline processes, increase accountability, and reduce costs. However, simply buying a ServiceNow license is not enough.
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