Key machine learning trends that will rock your 2018

If there’s one tech term that takes the crown for being on the lips of everyone associated with the industry, it has to be “machine learning.” Abbreviated ML, machine learning has impacted almost every industry in one way or another. Everything, from unmanned diagnostics equipment that detects tumors and cancers to your favorite music streaming apps, uses machine learning now. Venture capitalists have invested millions of dollars in startups with a focus on machine learning. That’s not all: ML is in the budget plans of all enterprises that haven’t already started their machine learning journeys. Tech giants with platform businesses are advancing their ML capabilities to the next level. It’s fascinating that machine learning (which is actually one approach to achieving artificial intelligence in computer programs) is already a multibillion-dollar industry. Trends and predictions made about the industry back in 2016-17 have turned into realities. Now is time for every IT leader to recognize and track the machine learning trends that will keep this technology hot throughout 2018.

Machine learning trends: Edge computing

Machine learning trends
Flickr / Ivan T

Edge computing mimics public cloud by providing compatible services and endpoints that applications can use. Machine learning applications depend a great deal on the ability of the program to execute complex data analytics operations quickly. Often, for low-latency applications such as in unmanned aerial vehicles, ML applications are prone to failure because of the lag caused by the need to carry out complex analytics in the cloud. Edge computing emerges as a solution. Particularly in infrastructures that can’t accommodate VMs and containers, edge computing offers a practical solution. The edge computing layer, here, connects developers with compute, store, and networking services. In 2018, this kind of serverless computing will deliver a lot of convenience to developers by reducing the overhead efforts of deploying code.

ML applications in IT operations improvements

machine learning trends

In most enterprises, IT system components are generating massive data (log files, status reports, error logs, and whatnot). Hardware components, software components, server applications, and operating system — there’s operational data being produced everywhere. By taking all this data into the purview of machine learning, enterprise IT can become proactive instead of reactive.

  • ML algorithms will assist IT operations teams in finding the root cause of issues.
  • IT disruptions will be significantly mitigated by using predictive analytics.
  • ML will be able to enhance the intrusion detection capabilities of IT systems.

Watch out for updates about Amazon Mackie, an AI-powered operations management platform for IT. Azure Log Analytics is another of the machine learning trends to remember from this space.

Emergence of personalized tech experiences

Though chatbots are already a business force, 2018 is going to be a crucial year in terms of how machine learning technologies become accessible and relevant for end users in business settings. Personal virtual assistant applications are next in order. These apps will be able to connect with a user’s application usage information stored across databases. It will then detect patterns to prepare personalized usage experiences, such as lists of favorite screens, the anticipation of next actions, and easy access to knowledge base documents. The availability of a personalized virtual assistant per employee will be a giant leap forward for enterprises in maximizing workplace productivity.

Transparency in ML decision making

The impact of deep learning models in industries such as media, health care, law, and engineering has been felt already. Increasingly, we are depending on the judgments made by machine learning algorithms. However, these models are not very easy for humans. This means that there is a clear need to enhance the ease of comprehension of ML algorithms for humans. Particularly in applications such as loan application evaluation and medical diagnosis, the importance of understanding the core decision-making logic used by the algorithms is important to evaluate the dependability of the outcomes. Also, in applications where models need to be aligned with laws and regulations (example, treating job applications equally without consideration of age, gender, religion, caste, creed, color, etc), this will become increasingly important.

The race to market for cloud AI

Even though machine learning-powered artificial intelligence is impacting everyday life, it’s not exactly affordably accessible for businesses. Cloud-based AI solutions will be a major enabler for these businesses and perhaps the most unstoppable of all the machine learning trends that will take off 2018. In this direction, the leading tech innovators (Google, Microsoft, IBM, and Amazon) will race each other to develop cloud-based AI solutions and make them available as platforms to businesses.

Shift toward meta-learning

Today, machine learning algorithms depend vastly on their exposure to massive learning datasets and expansive real-life exposure to data to improve their logics. DeepMind’s AlphaGo Zero is able to beat its human competitors, but it’s not yet played a human that’s played 5 million games (though the software would have been exposed to much larger data).

Meta-learning is a bit of an umbrella term. Few-shot learning is a more specific term, focusing on the idea of discovering machine learning algorithms from a manageably small number of examples. This is certainly an exciting area. IT leaders will need to keep an eye on developments in this space to be able to leverage opportunities that present themselves.

Marrying existing ML systems with GDPR

When General Data Protection Regulation (GDPR) becomes applicable in 2018, we could see an increasingly large number of compliance issues being unearthed, with ML systems at the core of the noncompliance. This is because these systems depend on several interconnected databases, each individually governed by GDPR’s strict data protection and privacy guidelines. Data dependencies are very costly, and complex data-driven models will require deeper analysis from a GDPR perspective.

The pace of research and development in machine learning is commendable. It has engendered major transformations in business and commercial applications already. Throughout 2018, we expect a lot more progress, primarily around the trends described in this guide. As an IT leader and decision maker, you’d do well to stay abreast of these machine learning trends and take timely actions to surf this mega-wave.

Photo credit: Shutterstock

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