• January 25, 2017

The Rise of Machine Learning

Machine learning influences all the latest trends.

By Marcus Borba, Data and Analytics Expert, and CEO at Borba Consulting

“Machine learning gives computers the ability to learn without being explicitly programmed so that they can create algorithms that can learn from and make predictions on data.” — Arthur Samuel

Machine learning has made huge progress in recent years, and its capabilities have been incorporated into many types of software and platforms, enabling professionals to take advantage of them without knowing how they work. This is a time of monumental change in computing, mainly because machine learning has an important role in advancing the way that algorithms build applications. But how will the use of machine learning grow in the coming years? Here are some areas to watch.

  • Smart Applications: Every application can be a smart application due to the fast generation of new data and the ease of use of machine learning platforms. Smart applications can produce real-time predictions and get better through time. Online retailers, for example, are developing smart applications using machine learning to improve services and personalize the customer shopping experience. Several companies are already using—and many others plan to use—machine learning to provide an improved customer experience.
  • Forecasting: Machine learning is a good tool for developing forecasting because it has the capability to learn from data and can provide a solution for demand forecasting. Machine learning models have been used as classical statistical models in forecasting. The supply chain management industry is using machine learning for forecasting to build even more accurate predictors of demand to improve planning.
  • Recommendation Systems: Machine learning can be applied in many recommendation system models and algorithms. The recommender approach is classically based on the notion of similarity between user-user or item-item, and a neighborhood of similar items. Online shopping sites use machine learning to personalize product recommendations, loyalty programs and offers, websites, and real-time notifications.
  • Image Recognition: There are many applications for image recognition, though the most well-known is facial recognition. Machine learning techniques are progressively using convolutional neural networks, causing the field of image recognition to continuously grow.
  • Prescriptive Analytics: Prescriptive analytics are the third phase of business analytics: descriptive, predictive, and prescriptive analytics. Prescriptive analytics go further than predictive analytics by providing real rules directly applicable to the business. The prescriptive model can enable decision-makers to take immediate action based on the rules that come from the model itself and probabilistic forecasts. Optimization algorithms can use machine learning to leverage data from intelligent devices and sensors.
  • Signal Processing: Machine learning can help with the characterization of signals, including images, audio, and video, for several speech and image processing problems. Machine learning for signal processing can be used in images for object detection and recognition and biometrics representation. It also can be used to characterize sounds, retrieve music, separate sounds in mixtures, and synthesize speech.
  • Internet of Things: The Internet of Things (IoT) offers many opportunities for machine learning and data processing. IoT platforms offer machine learning capabilities that allow users to analyze sensor data, look for correlations, and determine what response to take.

Machine learning has the potential to help consumers and enterprises with decision-making and predictive responses, and the benefits can be applied across industries. How will you leverage it?

Like this story? Learn more about other megatrends driving enterprise transformation.

Marcus Borba is an analytics expert and CEO of Borba Consulting.

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