Editorial
Businesses are being promised unbridled productivity and unmatched efficiencies with adoption of machine learning and artificial intelligence, but it’s not entirely clear how that will occur.
Will machine learning produce machines that are truly autonomous and smart, or are they comparable to personal assistants such as Siri, Cortana and Google Now?
Understanding the criteria of what goes into a productive machine learning implementation sets a path for how this new technology can be applied to business and the benefits. These new intelligent algorithms automate sifting through mountains of data, making it easier to find patterns and solutions.
Machine Learning Underpins New Business Models
From the use of machine learning in one’s day-to-day life to its role in business automation and data analysis, it is having a major impact on the present and will shape future generations.
It has and will continue to change work, play and life as a whole. Machine learning technology is underpinning major advances in every industry and is paving the way for new business models, which disrupt incumbents who don’t innovate quickly enough.
Many established companies are undergoing digital transformation to protect their businesses from competition, minimize unplanned downtime and save costs. IDC expects that by 2020, almost 50 percent of IT budgets will be dedicated to digital business transformation, with its demand for continuous innovation and collaboration among cross-functional teams.
What Makes Machine Learning Smart?
Smart, automated aspects of artificial intelligence are the basis for machine learning. Algorithms adapt and react based on experience and context, and become more intelligent through exposure to rich data sources.
This data source becomes the model to drive learning behavior over time. The crucial element behind machine learning is constant access to comprehensive data, be it from a central repository or, most frequently, from disparate sources, coupled with contextual awareness that will allow the system to teach itself.
With machine learning, we have moved beyond only rule-based programming to algorithms that automatically discover and learn relationships and patterns in unstructured (and structured) data — independent of human intervention.
Speeding Adaptation to Change
Today’s abundance of large-scale storage, processing and fast networks, which have enabled the big data movement, are critical pillars for enabling machine learning technology. These capabilities allow software systems to learn from one another, which is crucial for effective machine learning.
And although advanced programming and automation is helpful, only true machine learning will allow businesses to adapt more quickly to change, both at the macro and micro levels. So what does it look like?
What Is Possible Today
Mobile, cloud and embedded technologies — where sensors are creating new streams of data, such as IoT — are redefining how customers interact with businesses and provide new rich data sources to mine for insight.
These new data sets and algorithms are often key competitive advantages. Satisfaction needs to be anticipated and reached immediately by end users.
Customers demand the ability to access information on any device in real-time. The companies and brands that satisfy the expectations of end users will find success. That’s a big part of the reason why 42 percent of total US technology spending will be on customer facing processes by 2018, according to an IDC report.
Keeping Businesses Competitive
Every business that wants to remain competitive is making changes to become a technology business, putting user experience as the top priority.
Companies are quickly innovating to take their place as part of tomorrow’s Fortune 500. As Ray Wang, CEO of Constellation Research, pointed out, “Since 2000, 52 percent of the names on the Fortune 500 list are gone [...]. The changes are the result of digital business models creating disruption in the marketplace.”
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Digital transformation has introduced previously unseen complexity. It’s no longer feasible to have human involvement in every aspect of work.
IT performance goals are challenged by the flood of increased traffic, and applications and devices that need to gain access to data in order to work seamlessly across technologies. Companies that mine user-generated data to optimize customer experiences are responding with their own applications that harness machine learning capabilities.
The Age of Digital Assistants
Google recently announced Alo, a human digital assistant, that takes the data mining capabilities present in Google Now to another level. This assistant provides the ability to be two steps ahead, reminding the user to bring an umbrella before leaving work at the usual time of 7:05 am.
Google Home goes deeper by integrating data from Nest thermostats’ habits to keep homes comfortable, lit properly and just the way the user likes it; autonomous vehicles are freeing consumers from long commutes and learning from each other as the Tesla Autopilot system does; and search applications become data sources themselves along with being attuned to user preferences and interests.
Democratizing Machine Learning
Intelligent algorithms are doing the same for businesses. The same benefits consumers are already enjoying from machine learning in their daily lives are now expected in their work.
Technology giants are making machine learning more accessible to the average user by building easily reusable cloud services. These services come in the form of Amazon Machine Learning, Microsoft Azure Machine Learning, and Google Cloud ML.
Although these services lower the bar for incorporating machine learning into existing technologies they also almost predicate that your data be housed within these clouds, but most enterprises are and will continue to be hybrid, using a combination of traditional data centers, some running private cloud and public cloud.
Adaptation takes time and customers don’t have the patience to wait.
Things to Keep in Mind
Machine learning may seem like a magical tool for the enterprise, but it’s not. There are a few important aspects of which to be aware: for intelligent algorithms to continuously become smarter, they need to take multiple factors into account before providing output, and the data they leverage in this process must be rich and streamed in real-time.
A machine learning system is only as effective as the data that it is fed and the environment it adapts to.
Ultimately, we will only be able to truly take advantage of what insights are hidden within the data when we can manage the complexity it introduces — and it will take the aid of machines to do so. We are just getting a glimpse at the tip of the iceberg. The future will be one in which the Turing test will be too easily bested.
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