Enterprises at every stage of growth from startups to Fortune 500 firms are using AI, machine learning, and deep learning technologies for a wide variety of applications. Deep learning, as the fastest growing area in AI, is empowering much progress in all classes of emerging markets and ultimately will be instrumental in ways we haven’t even imagined.
Already, deep learning serves as the enabling technology for many application areas such as autonomous vehicles, smart personal assistants, precision medicine, and much more. But the advancements aren’t limited to a few business-specific areas. Deep learning is shaping innovation across many industries. Applications of AI, such as fraud detection and supply chain optimization, are being used by some of the world’s largest companies.
In this article, we’ll examine a handful of compelling business use cases for deep learning in the enterprise (although there are many more). Here is an analysis prepared by McKinsey Global Institute that shows how deep learning techniques can be applied across industries, alongside more traditional analytics:
Oil & Gas Industry
Baker Hughes, a GE company (BHGE), is using AI to help the oil and gas industry distill data in real time in order to significantly reduce the cost of locating, extracting, processing, and delivering oil. Deep learning neural networks are used to unseal insights from data that were previously hidden in order to achieve important goals such as seismic modeling, automated well planning, predicting machinery failure, and optimizing supply chains.
With deep learning, well operators are able to visualize and analyze massive volumes of production and sensor data such as flow rates, pump pressures, and temperatures. This capability affords better insights into critical issues such as predicting which pieces of equipment might fail and how these failures could affect systems on a wider basis.
Deep learning algorithms allow oil and gas companies to determine the best way to optimize their operations as conditions continue to change. For instance, they can turn large volumes of seismic data images into 3-dimensional maps designed to improve the accuracy of reservoir predictions. Specifically, they can use deep learning to train models to predict and improve the efficiency, reliability, and safety of expensive drilling and production operations.
Construction company Bechtel Corp. has a deep learning use case which is aimed at optimizing construction planning. The company is using reinforcement learning models similar to those used by AlphaGo (developed by Alphabet’s Google DeepMind), the software that defeated elite human players of the game Go, to find the fastest route to build projects. The model runs step-by-step simulations of projects, testing out sequences of installing pipe laying concrete to find the optimal sequence.
There are a number of characteristics unique to construction that have historically left the industry less reliant on technology than others. One is that each project is unique, which means there’s essentially no availability of training data from past projects that can be used for training algorithms. In order to get over this hurdle, reinforcement learning is used where simulations essentially become the training data set.
Bechtel is just starting to explore the huge potential for bringing deep learning use cases to the construction industry. As with other industries, the goal is to take the company’s industry knowledge and align it with deep learning to advance the industry forward.
Financial Services Industry
There are many opportunities for applying deep learning technology in the financial services industry. One important task that deep learning can perform is e-discovery. For example, large investment houses like JPMorgan Chase are using deep learning based text analytics for insider trading detection and government regulatory compliance. Hedge funds use text analytics to drill down into massive document repositories for obtaining insights into future investment performance and market sentiment. The use case for deep learning based text analytics revolves around its ability to parse massive amounts of text data to perform analytics or yield aggregations.
Deep learning also has a number of use cases in the cybersecurity space. One of the advantages of deep learning has over other approaches is accuracy. In many cases, the improvement approaches a 99.9% detection rate. The high risk and cost associated with not detecting a security threat make the expense related with deep learning justified.
Deep learning can play a number of important roles within a cybersecurity strategy. Use cases include automating intrusion detection with an exceptional discovery rate. Deep learning also performs well with malware, as well as malicious URL and code detection. Deep learning for cybersecurity is a motivating blend of practical applications along with untapped potential. With proper vetting, it’s well worth the effort to ensure the time and investment required for implementing a solution that yields the anticipated gains.
Deep learning’s power can also be seen with how it’s being used in social media technology. Let’s take Pinterest for example, which includes a visual search tool that lets you zoom in on a specific object in a “Pin” (or pinned image) and discover visually similar objects, colors, patterns and more. The company’s engineering team used deep learning to teach their system how to recognize image features using a richly annotated data set of billions of Pins curated by Pinterest users. The features can then be used to compute a similarity score between any two images and identify the best matches.
Deep learning is rapidly transforming many industries including healthcare, energy, fintech, transportation, and many others, to rethink traditional business processes with digital intelligence. Early adopter industries have witnessed a profound effect on the workplace and great potential in terms of developing deep learning applications, which can be used for yielding forecasts, detecting fraud, attracting new customers, and so much more. The opportunities and capabilities are substantial and that’s why many enterprises are investing in deep learning for building out their existing applications as well as developing new solutions.