The technology is being developed for and deployed in practically every industry, but what does the term “machine learning” really mean? More importantly, what does this technology do, and how will it impact your business?
A revolution is coming to enterprises everywhere in the form of machine learning, and its beginnings are already visible in discrete areas from sales lead generation to supply chain analytics. As computing power soars while costs steadily decline, and as access to data proliferates, machine learning will serve an increasingly important function in tomorrow’s enterprise environments.
Leveraging the many benefits machine learning promises to bring will require that IT pros are able to properly understand how the technology works and what it needs to function optimally. And with plenty of confusion surrounding the term, it would seem that an explanation of what exactly machine learning is — and what it isn’t — is in order.
The terms “artificial intelligence,” “machine learning,” and “deep learning” are often misused, or even used interchangeably. And although their meanings overlap, they’re nonetheless fundamentally distinct processes and technologies. In short, deep learning is a particular type of machine learning, which is, in turn, a particular type of AI. Or in taxonomic terms: A.I. is the “family,” machine learning the “genus,” and deep learning the “species.”
As a relatively broad umbrella term, artificial intelligence can refer to any digital system designed to think like a human. Meanwhile, machine learning (ML) refers to a specific breed of predictive algorithm designed to learn over time as it consumes data. ML algorithms attempt to make sense of data inputs by referencing their base of preexisting knowledge — namely, previously crunched input-output data. Thus the more data it’s fed, the larger its base of knowledge and the more accurate its predictive capacity.
Finally, deep learning is a specific class of machine learning algorithms loosely based on human cognition. These algorithms use multilayered “artificial neural networks” to process and model data at multiple levels in order to make the kinds of complex, intelligent decisions you’d typically only expect as a result of human cognition.
Already, we can see a massive (and growing) number of serious, functional, profitable use cases for machine learning, from marketing automation in ad tech to supply chain analytics in retail. Youtube, for example, uses machine learning to produce an eerily prescient feed of content “recommended for you.” Critically, the outputs (in this case video recommendations) aren’t modeled by a pre-set, static, manmade algorithm, but by one that’s self-adjusting, dynamic, and machine-made — one that optimizes itself with each and every additional data point it crunches.
Machine learning is particularly effective when applied to repetitive, mundane tasks with large data sets like user recommendations (e.g. Netflix), spam filtering (e.g. Gmail), or user behavior analytics (cybersecurity).
Take user behavior analytics (UBA) as an example: the sheer amount of user-generated data available for analysis would render the task effectively impossible for any person or group of people. Therefore, UBA largely relies on machine learning algorithms to locate user anomalies and squash threats. Long an essential tool in the cybersecurity toolbelt, efficacious UBA would not be possible in the absence of machine learning.
The efficacy of machine learning will be driven in large part by three things: computing power, computing cost, and data. With Moore’s Law continuing apace, and IDC forecasting global annual data production to rise a whopping 29% each year over the next eight years, it seems inevitable that machine learning will grow in kind — both in scope and importance.
And as more and more devices become connected to the internet of things (IoT), these networks will funnel vast amounts of useful new data to machine learning algorithms, enabling a host of new technologies like automated grocery shopping and remote health monitoring. But companies that fail to adequately prepare for the change can expect a few drawbacks as well: first, a heavy strain on existing network infrastructures; second, more network security vulnerabilities resulting from millions of new and unsecured devices.
Readying enterprise networks for machine learning will entail bulking up your infrastructure to support the connectivity demands that come with it, as well as securing any new attack vectors. Companies looking to optimize their networks for the inevitable proliferation of data-intensive machine learning technologies should consider partnering with a company like Turn-key Technologies (TTI). With over two decades of experience, and a multitude of industry certifications, TTI has the knowledge and expertise to help bring enterprise networks into the machine learning era — securely and successfully.
If your network is already struggling under the weight of existing connectivity requirements, partnering with a cybersecurity expert like TTI is the most cost-effective way to bring your network up to grade. TTI can provide a comprehensive assessment of your current network environment, including a thorough report on the state of your network and clear recommendations on what can be done to improve it.
June 21, 2018
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