Machine Learning is Everywhere: Preparing for the Future
The influence and impact of machine learning can be seen in everything from our morning coffee orders to the online banking apps we use.
The technology is infusing a deeper intelligence and understanding into the applications that touch our lives, to dramatically improve our experiences. In addition, it’s helping to spawn entirely new business innovations and models, such as autonomous vehicles and virtual personal assistants.
In fact, machine learning is so prevalent and pervasive that it’s difficult to imagine an enterprise being able to survive without embracing it in the next five years. Especially considering predictions of continued global data growth. This is why it is not only important, but critical to understand the current state of machine learning and what the future holds.
Machine Learning: It’s All Around You
Today, machine learning methods are used to continually improve the personalization of specific technologies. Within an enterprise, unique algorithms and models are built to power everything from fraud detection and recommendation engines to medical-image classification and aircraft scheduling.
For example, fraud currently costs the financial industry approximately $80 billion annually. Existing fraud detection systems operate on a set of rules, such as flagging ATM withdrawals over a certain amount or credit card purchases that take place outside a card holder’s home country. Rather than singling out specific transaction types, machine learning is used to analyze historical transaction data to build models that can detect fraudulent patterns before they happen. Next time you get a fraud alert, you can thank machine learning.
Separately. with the rise of virtual personal assistants, machine learning is working behind the scenes to learn queues from speech patterns to improve machine to human engagements while also learning to make informed decisions and discern from surroundings just like a human driver with autonomous vehicles.
Adjusting to the New Data Normal
The implementation of machine learning across all verticals serves as a reminder for businesses that embracing this new data and intelligent landscape is the new normal. Currently, organizations collect data from multiple points across the business, but this process will need fine tuning so that existing business strategies and models can meet new data demands. Take for instance, important data from customers, vendors, employees, and processes. This information is collected constantly, if a company does not have a proper machine learning strategy, roadblocks may emerge, such as being unable to use data assets advantageously.
Across all industries, machine learning plays a critical role in how organizations better understand and cater to their customers, make more strategic business decisions, and optimize company workflows. For example restaurants can better cater to their customers by building a machine learning model to analyze the busiest time of day, the most popular food items, and estimated wait times to more accurately stock supplies and schedule wait staff for an improved customer experience. Machine learning is becoming a clear competitive advantage for organizations. If machine learning is not on the roadmap or part of the company’s business strategy, remaining competitive will become more and more difficult.
Machine learning is reshaping businesses across industries from large enterprises to local coffee shops. As machine learning infiltrates these industries, all humans begin to carry a data trail. Each person is generating and sharing data. In the future organizations will capitalize on deeper customer understanding with improved and personalized experiences.
Thinking about the future, machine learning will make its biggest mark in helping workers and businesses to more efficiently use time and gain a deeper understanding of their data. There is so much industry knowledge locked away in PDFs, medical files ,and even cookbooks. Tapping into this data, being able to organize, process and assimilate years of unstructured data points will accelerate the acquisition of knowledge, reducing the time to innovation and unearthing of new ideas.
Take movie making or scientific research as two examples. In the film industry, Hollywood trailer producers can use using machine learning to understand, evaluate, and categorize the footage and images collected on set. From there, machine learning gives producers the power to automatically create movie trailers by taking advantage of image analysis paired with natural language processing. In regards to research and even medicine, we’ve already witnessed the power machine learning can have in gathering data and analyzing professional journals.
What’s next is the role machine learning can have in improving accuracy and optimizing the performance of operational research Imagine being able to accurately conduct research in a matter of weeks instead of months or reducing medical errors.
Machine learning has huge potential but it will require implementing proprietary machine learning tools that can handle the workload. However, as the need for sophisticated machine learning algorithms evolve, it is important to consider who can handle large quantities of data generation as the future will require the deep learning only possible by full comprehension and analysis of all data sets. Survival in the future will be mean incorporating complex machine learning algorithms into business process and having the ability to scale through new connections. In the future, the success of a business will be measured by its ability to innovate as expressed through its data knowledge.
About the author: Dinesh Nirmal is IBM Vice President of Analytics Development, responsible for delivering advanced analytics solutions covering data management, Hadoop, artificial intelligence, machine learning and more. During his 20 years’ IBM service Dinesh has held technical leadership and senior management roles, from IMS Director, VP of Smarter Process, VP of Analytics and z Systems to his current role. Dinesh is also Site Executive of IBM Silicon Valley Laboratories and an R Consortium board member. He also owns the mission for the IBM Machine Learning Hub and IBM Spark Technology Center.