From Big Data to Deep Learning

Big data, machine learning, and artificial intelligence – what do these all mean? This article introduces these concepts along with some applications of these technologies.

Big Data refers to large volume, velocity, and variety of data sets that require innovative ways to store and analyze it.

Predictive analytics is often discussed along with Big Data. Predictive analytics uses historical data and big data. It applies modeling, data mining, and machine-learning techniques to forecast potential future outcomes. This differs from descriptive analytics, which focuses on analyzing what happened in the past. Prescriptive analytics takes predictions to the next step, by recommending actions and decisions. It is referred to as the final frontier of analytic capabilities.

Artificial Intelligence studies how to make computer programs exhibit human intelligence. Machine Learning is a method of improving predictions through pattern recognition and exposing the algorithm to new data. It differs from traditional computer science programming where humans gave explicit instructions in code. Deep Learning is a branch of machine learning. Deep learning uses neural networks to represent the data in multiple layers without exposure to historical data. It has the potential to bring artificial intelligence closer to reality.

Predictive analytics is prevalent in many industries. Take the retail industry for example. Retailers are trying to leverage big data, predictive analytics and machine learning to better understand customers and improve sales. In an interview with Kerry Liu CEO of Rubikloud, a Canadian big data startup that applies machine learning techniques to enable retailers make decisions, he said many retailers are using outdated historical models and face tremendous pressure on big box retailers from Amazon and other e-commerce startups. To take advantage of big data, retailers need to make their data consumable by analytics systems. Big data doesn’t necessarily mean more data; it’s important to identify the right data sources to achieve the business objective. Then, analytics and machine learning can be applied to gain insights and make predictions.

On the other hand, application of deep learning is still at its early stages. It is beginning to see successes in medical field, where results from Enlitic’s deep learning algorithms are able to aid physicians in making diagnosis.