7 reasons why you need to get onboard with machine learning

There’s never been a more exciting time than the present to be involved in technology. We’ve said this time and again, but it bears repeating . . .  the convergence of mobile, cloud, collaboration, and information technologies over the past 5 years have created unheard of opportunities to drive new dimensions of innovation and creativity. Here at the beginning of 2015 we are witnessing amazing new breakthroughs as digital technologies continue to transform and reinvent our notions of work and progress. Internet of Things, automation, wearables, and smart machines are at our doorstep and giving us new capabilities that would have seemed like science fiction only a few short years ago.




One of the biggest buzzwords in the digital technologies market right now is “machine learning.” As a subset of artificial intelligence, machine learning is not new by any means. Machine learning is a practical form of artificial intelligence, and represents the science of getting computers to act without being explicitly programmed. The field of machine learning has gained momentum in recent years through the confluence of open source and cross-collaboration capabilities, along with the commoditization of new digital technologies.

Machine learning is an exciting and disruptive force in the world of technology and small business owners especially will want to be clear about the promises, possibilities, and even perils (if not adopted!) of this emerging technology. Let’s walk through 7 of the biggest reasons you need to get onboard with machine learning in 2015.

1. Machine learning is all around us

We see examples of machine learning all around us and many of them we take for granted on a daily basis: Google’s page ranking system, photo tagging on Facebook, customized product recommendations from Amazon, or automatic spam filtering on Gmail are all examples of machine learning.

2. Machine learning represents a new paradigm in computing

In a recent paper on the topic, Pedro Domingos says it this way: “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields.”

3. Machine learning is the core of ‘smart machine’ technology

Smart machines are systems that employ artificial intelligence and machine learning algorithms to make decisions and solve problems without human intervention. Smart machines are seen in the following applications: context aware devices, such as smartphones that can sense their physical environment and adapt their behavior accordingly; intelligent personal assistants like Google Now and Apple Siri; smart advisors like IBM Watson; and autonomous vehicles like driverless cars and Amazon’s fleet of delivery drones called Prime Air.




4. Machine learning will drive the semantic web

The internet has developed through various stages over the past 20 years, from primarily an information repository (Web 1.0) to a social networking tool (Web 2.0). The next stage (Web 3.0?) is the semantic web. The basic idea here is to enable machines to “understand” and respond to complex human requests based on their meaning. This approach requires information to be semantically structured in a way that can be processed directly by machines. Instead of the consumer going to the internet, the internet is customizable to the needs of the consumer.

5. Machine learning will require new paradigms in data modeling

The explosion of machine learning and Big Data will require a further shift away from relational database models. Triplestore databases represent an alternative form of data storage and processing that rely on triples (single data entities comprised of three serialized elements <subject, predicate, object>) to address semantic structures in web language. The complex query a triplestore is capable of performing is illustrated in this example: “find all meetings that happened in November 2010 within 5 miles of Berkeley that were attended by the three most influential people among Joe’s friends and friends-of-friends.”

6. Machine learning will profoundly disrupt the future of jobs

Research shows that many CEOs are underestimating the systemic and deep impact that smart machines will have through 2020, as well as the potential for them to replace millions of middle-class jobs in the decades to come. Even more sobering, some experts suggest that the impacts of machine learning will extend to the C-suite as well.

7. Machine learning will create new learning opportunities

IT and business professionals will need to keep their jobs skills relevant and updated by ensuring they pursue competencies and cognitive tasks that machines can’t touch. This will require ongoing training and development in higher order skills such as coding, statistics, visualization, linguistics, information management, and Big Data.




The bottom line here is this: organizations of all sizes should get onboard with machine learning immediately. Don’t try to boil the ocean but begin incrementally by understanding what it is, identifying business relevant use cases, assessing the Big Data landscape, and hiring consultancies and vendors who can advise on an initial proof of concept. Businesses that fail to adopt machine learning as a platform for improved operations, problem solving, and customer service will get left behind, plain and simple!


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