A branch of machine learning, neural networks (NN), also known as artificial neural networks (ANN), are computational models — essentially algorithms. Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques. Neural networks have provided us with greater convenience in numerous ways, including through ridesharing apps, Gmail smart sorting, and suggestions on Amazon. Founder and CEO of Cortx, an artificial intelligence company that uses neural networks in the design of its natural language processing solutions, including an automated grammar correction application, Perfect Tense.
There are two approaches to train a Neural Network. Supervised learning provides the network with desired outputs through manual grading of network performance or by delivering desired outputs and inputs. Unsupervised learning occurs when the network makes sense of inputs without outside assistance or instruction.
Attributes of Neural Networks
With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:
- Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, software uses adaptive learning to teach math and language arts.
- Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
- Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
- Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.
- Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.
Rees shares how KodaCloud technology takes advantage of neural networks to continuously improve. 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.
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)
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