Real-Life Applications of Neural Networks | Smartsheet

As an August 2018 New York Times article notes, “The companies and government agencies that have begun enlisting the automation software run the gamut. They include General Motors, BMW, General Electric, Unilever, MasterCard, Manpower, FedEx, Cisco, Google, the Defense Department, and NASA.” We’re just seeing the beginning of neural network/AI applications changing the way our world works.

H3: Engineering Applications of Neural Networks

Engineering is where neural network applications are essential, particularly in the “high assurance systems that have emerged in various fields, including flight control, chemical engineering, power plants, automotive control, medical systems, and other systems that require autonomy.” (Source: Application of Neural Networks in High Assurance Systems: A Survey.)

We asked two experts in the engineering sector about how their applications improve retail, manufacturing, oil and gas, navigation, and information retrieval in office environments.

People use wireless technology, which allows devices to connect to the internet or communicate with one another within a particular area, in many different fields to reduce costs and enhance efficiency. Huw Rees, VP of Sales & Marketing for KodaCloud, an application designed to optimize Wi-Fi performance, describes just some uses.

Rees offers some everyday examples of Wi-Fi use: “Supermarket chains use Wi-Fi scanners to scan produce in and out of their distribution centers and individual markets. If the Wi-Fi isn’t working well, entire businesses are disrupted. Manufacturing and oil and gas concerns are also good examples of businesses where Wi-Fi is mission critical, because ensuring reliability and optimization is an absolute requirement,” he says.

Wi-Fi is great, but it takes a lot of oversight to do its job. “Most enterprise or large-scale wireless local area network solutions require near-constant monitoring and adjustment by highly trained Wi-Fi experts, an expensive way to ensure the network is performing optimally,” Rees points out. “KodaCloud solves that problem through an intelligent system that uses algorithms and through adaptive learning, which generates a self-improving loop,” he adds.

Rees shares how KodaCloud technology takes advantage of neural networks to continuously improve: “The network learns and self-heals based on both global and local learning. Here’s a global example: The system learns that a new Android operating system has been deployed and requires additional configuration and threshold changes to work optimally. Once the system has made adjustments and measuring improvements necessitated by this upgrade, it applies this knowledge to all other KodaCloud customers instantaneously, so the system immediately recognizes any similar device and solves issues. For a local example, let’s say the system learns the local radio frequency environment for each access point. Each device then connects to each access point, which results in threshold changes to local device radio parameters. Globally and locally, the process is a continuous cycle to optimize Wi-Fi quality for every device.”

A fast-developing technology, drones are used in disaster relief, oil, gas, and mineral exploration, aerial surveillance, real estate and construction, and filmmaking. Neill McOran-Campbell is CEO of Aeiou.tech, which designs advanced drone technology for use in many different sectors. “Our Dawn platform is an on-board series of sensors and a companion computer that allows virtually any unmanned aerial system to utilize the wide range of benefits that AI offers, from flight mechanics, such as navigation and obstacle avoidance, to services such as infrastructure inspection or package delivery,” says McOran-Campbell.

McOran-Campbell explains how Dawn functions based on two levels of biology: “At the first level, we use ANNs to process raw information. There are three different types of networks we use: recurrent neural networks, which use the past to inform predictions about the future; convolutional neural networks, which use ‘sliding’ bundles of neurons (we generally use this type to process imagery); and more conventional neural networks, i.e., actual networks of neurons. Conventional neural networks are very useful for problems like navigation, especially when they are combined with recurrent elements.

“At the more sophisticated, second level, Dawn’s structure emulates the best architecture that exists for processing information: the human brain. This allows us to break down the highly complex problem of autonomy the same way biology does: with compartmentalized ‘cortexes,’ each one with their neural networks and each with their communication pathways and hierarchical command structures. The result is that information flows in waves through the cortexes in the same way that it does in the brain. [In both instances, the process is optimized] for effectiveness and efficiency in information processing,” he explains.

Here’s a list of other neural network engineering applications currently in use in various industries:

• Aerospace: Aircraft component fault detectors and simulations, aircraft control systems, high-performance auto-piloting, and flight path simulations

• Automotive: Improved guidance systems, development of power trains, virtual sensors, and warranty activity analyzers

• Electronics: Chip failure analysis, circuit chip layouts, machine vision, non-linear modeling, prediction of the code sequence, process control, and voice synthesis

• Manufacturing: Chemical product design analysis, dynamic modeling of chemical process systems, process control, process and machine diagnosis, product design and analysis, paper quality prediction, project bidding, planning and management, quality analysis of computer chips, visual quality inspection systems, and welding quality analysis

• Mechanics: Condition monitoring, systems modeling, and control

• Robotics: Forklift robots, manipulator controllers, trajectory control, and vision systems

• Telecommunications: ATM network control, automated information services, customer payment processing systems, data compression, equalizers, fault management, handwriting recognition, network design, management, routing and control, network monitoring, real-time translation of spoken language, and pattern recognition (faces, objects, fingerprints, semantic parsing, spell check, signal processing, and speech recognition)

Business Applications of Neural Networks:

Real-world business applications for neural networks are booming. In some cases, NNs have already become the method of choice for businesses that use hedge fund analytics, marketing segmentation, and fraud detection. Here are some neural network innovators who are changing the business landscape.

At a time when finding qualified workers for particular jobs is becoming increasingly difficult, especially in the tech sector, neural networks and AI are moving the needle. Ed Donner, Co-Founder and CEO of untapt, uses neural networks and AI to solve talent and human resources challenges, such as hiring inefficiency, poor employee retention, dissatisfaction with work, and more. “In the end, we created a deep learning model that can match people to roles where they’re more likely to succeed, all in a matter of milliseconds,” Donner explains.

“Neural nets and AI have incredible scope, and you can use them to aid human decisions in any sector. Deep learning wasn’t the first solution we tested, but it’s consistently outperformed the rest in predicting and improving hiring decisions. We trained our 16-layer neural network on millions of data points and hiring decisions, so it keeps getting better and better. That’s why I’m an advocate for every company to invest in AI and deep learning, whether in HR or any other sector. Business is becoming more and more data driven, so companies will need to leverage AI to stay competitive,” Donner recommends.

The field of neural networks and its use of big data may be high-tech, but its ultimate purpose is to serve people. In some instances, the link to human benefits is very direct, as is the case with OKRA’s artificial intelligence service.

“OKRA’s platform helps healthcare stakeholders and biopharma make better, evidence-based decisions in real-time, and it answers both treatment-related and brand questions for different markets,” emphasizes Loubna Bouarfa, CEO and Founder of Okra Technologies and an appointee to the European Commission’s High-Level Expert Group on AI. “In foster care, we apply neural networks and AI to match children with foster caregivers who will provide maximum stability. We also apply the technologies to offer real-time decision support to social caregivers and the foster family in order to benefit children,” she continues.

Like many AI companies, OKRA leverages its technology to make predictions using multiple, big data sources, including CRM, medical records, and consumer, sales, and brand measurements. Then, Bouarfa explains, “We use state-of-the-art machine learning algorithms, such as deep neural networks, ensemble learning, topic recognition, and a wide range of non-parametric models for predictive insights that improve human lives.”

According to the World Cancer Research Fund, melanoma is the 19th most common cancer worldwide. One in five people on the planet develop skin cancer, and early detection is essential to prevent skin cancer-related death. There’s an app for that: a phone app to perform photo self-checks using a smartphone.

“SkinVision uses our proprietary mathematical algorithm to build a structural map that reveals the different growth patterns of the tissues involved,” says Matthew Enevoldson, SkinVision’s Public Relations Manager.

Enevoldson adds that the phone app works fast: “In just 30 seconds, the app indicates which spots on the skin need to be tracked over time and gives the image a low, medium, or high-risk indication. The most recent data shows that our service has a specificity of 80 percent and a sensitivity of 94 percent, well above that of a dermatologist (a sensitivity of 75 percent), a specialist dermatologist (a sensitivity of 92 percent), or a general practitioner (a sensitivity of 60 percent). Every photo is double-checked by our team of image recognition experts and dermatologists for quality purposes. High-risk photos are flagged, and, within 48 hours, users receive personal medical advice from a doctor about next steps.” The app has 1.2 million users worldwide.

Keeping track of data in any work environment and making good use of it can be a challenge. Rob May is CEO and Co-Founder of Talla, a company that builds “digital workers” that assist employees with daily tasks around information retrieval, access, and upkeep. “We give businesses the ability to adopt AI in a meaningful way and start realizing immediate improvements to employee productivity and knowledge sharing across the organization,” May explains. “If a company stores their product documentation in Talla, its sales reps can instantly access that information while on sales calls. This ability to immediately and easily access accurate, verified, up-to-date information has a direct impact on revenue. By having information delivered to employees when they need it, the process of onboarding and training new reps becomes better, faster, and less expensive.”

Talla’s neural network technology draws on different learning approaches. “We use semantic matching, neural machine translation, active learning, and topic modeling to learn what’s relevant and important to your organization, and we deliver a better experience over time,” he says. May differentiates Talla’s take on AI: “This technology has lifted the hood on AI, allowing users to train knowledge-based content with advanced AI techniques. Talla gives users the power to make their information more discoverable, actionable, and relevant to employees. Content creators can train Talla to identify similar content, answer questions, and identify knowledge gaps.”

Here are further current examples of NN business applications:

• Banking: Credit card attrition, credit and loan application evaluation, fraud and risk evaluation, and loan delinquencies

• Business Analytics: Customer behavior modeling, customer segmentation, fraud propensity, market research, market mix, market structure, and models for attrition, default, purchase, and renewals

• Defense: Counterterrorism, facial recognition, feature extraction, noise suppression, object discrimination, sensors, sonar, radar and image signal processing, signal/image identification, target tracking, and weapon steering

• Education: Adaptive learning software, dynamic forecasting, education system analysis and forecasting, student performance modeling, and personality profiling

• Financial: Corporate bond ratings, corporate financial analysis, credit line use analysis, currency price prediction, loan advising, mortgage screening, real estate appraisal, and portfolio trading

• Medical: Cancer cell analysis, ECG and EEG analysis, emergency room test advisement, expense reduction and quality improvement for hospital systems, transplant process optimization, and prosthesis design

• Securities: Automatic bond rating, market analysis, and stock trading advisory systems

• Transportation: Routing systems, truck brake diagnosis systems, and vehicle scheduling

The use of neural networks seems unstoppable. “With the advancement of computer and communication technologies, the whole process of doing business has undergone a massive change. More and more knowledge-based systems have made their way into a large number of companies,” researchers Nikhil Bhargava and Manik Gupta found in “Application of Artificial Neural Networks in Business Applications.”