Artificial Neural Network: The Brain Behind Today’s Smart Technology
The Artificial Neural Network(ANN) and How It Works
Artificial Neural Network
Psychologist Frank Rosenblatt invented the first Artificial Neural Network or ANN called “Perceptron” in 1958. It was designed to imitate the way a human brain processes and analyzes visual data, like pictures and words.
Also, it helps computers, devices, and software to have a self-learning ability and enables them to recognize objects, analyze patterns, and even solve problems that are impossible or too difficult for human standards.
Google DeepMind is one of the best examples of an advanced neural network that drives the future of machine learning. Google DeepMind made an AI system called AlphaGo that plays the Chinese board game Go.
By learning through an artificial neural network, AlphaGo has beaten the professional Go player and 18-time world champion, Lee Sedol, with a brutal score of 4–1 last 2016. It created a buzz in the tech world.
ANN can learn from its “experiences” or based on previous data and patterns it has processed. Thus, AlphaGo was able to beat the grandmaster using its brilliant “artificial neurons.”. In more recent news Google DeepMind has announced shifting its AI focus from games to science: “From building “AI agents” that can play games to building AI agents that can have real-world impact, particularly in areas of science like biology.”. Google DeepMind is planning on using its AI products and research to leverage advances in healthcare, physics, and global warming.
Neurons and Synapses
Synthetic synapses connect artificial neurons. Synapses serve as a connector that allows the transfer of information or signal from one neuron to another. ANN’s neurons and synapses can perform calculations and create neuromorphic chips to understand images, sounds and respond to changes in data.
These hundreds of thousands of artificial neurons that are connected by the synapses process information through the three distinct layers;
- Input layer
- Hidden layer
- Output layer
They work together to produce and present one output report.
- Input layers are like researchers, they gather, analyze and interpret all the needed raw data for a research study.
- Hidden layers are like vintners, they extract substances from quality grapes and use them to create the best, wine out of it.
- Output Layer is like corporate secretaries, they sometimes receive messages and instructions from different callers, e.g., clients, employees, and people in business, and inform their bosses about them.
Learning How the Neural Networks Learn
As mentioned earlier, ANN can work the way the human brain works and can learn the way we learn. Howard Rheingold, an American critic, teacher, and writer who’s famous for his specialties on the social, cultural, and political implications of modern technology, stated;
“The neural network is this kind of technology that is not an algorithm, it is a network that has weights on it, and you can adjust the weights so that it learns. You teach it through trials.”
Architecture Of Neural Network
It’s a fact that the neural network can operate and improve its performance after “teaching” it but it needs to undergo some process of learning to acquire information and be familiar with them.
ANN’s Method of Learning
ANN can also learn through different methods and techniques. Here are ANN’s three methods of learning.
This method of learning is dependent because it’s under the supervision of facilitators called data scientists. With the help of this process, it can categorize unlabeled data into two groups: Classification and Regression.
- Classification is a method of prediction or identification of which class the input data is part of(discrete value). For example, the computer analyzes and pictures if it’s an animal or not — like solving a yes or no problem — then categorizes by its kind, whether it’s a dog or a cat.
- Regression is an estimation process between two variables and their relationship. This method analyzes or predicts the value of the input based on the given data. Let’s say, a product’s data is already available — the size, weight, height, or anything — regression then predicts the price or the value of the product.
In unsupervised learning, the system is provided or presented with unlabeled input data, and the system’s algorithms act independently and group the uncategorized information based on similarities and differences without being supplied with the correct outputs.
Here are two methods of unsupervised learning:
Cluster Analysis is a tool to solve classification problems; it sorts or sub-divides things, people, events, etc., into groups, or clusters, according to their similarity, to strengthen the degree of association and connection between members of the same cluster. Its objective is to reveal relationships, structures, associations, and hidden patterns in mass data.
Associate Analysis is a discovery method to reveal relationships among variables in huge data, and how those data are related to or associated with each other — making an easier way to come up with outputs. Like when we buy an item on Amazon, there are suggestions such as: “Customers also bought,” and giving similar items that we purchased. Moreover, all these suggestions are based on past information that is related to the presented data.
This method allows machines to automatically determine the best behavior or action depending on the circumstance, to maximize or improve its performance. Reward feedback is needed for machines to learn; this is called the reinforcement signal. This method allows the artificial neural network not just to function as the brain, but also to behave and act like the biological one.
What are neural networks emulating in human brain structure, and how does training work?
All mammalian brains consist of interconnected neurons that transmit electrochemical signals. Neurons have several components: the body, which includes a nucleus and dendrites; axons, which connect to other cells; and axon terminals or synapses, which transmit information or stimuli from one neuron to another. Combined, this unit carries out communication and integration functions in the nervous system. The human brain has a massive number of processing units (86 billion neurons) that enable the performance of highly complex functions.
Why Do We Use Neural Networks?
Neural networks’ human-like attributes and ability to complete tasks in infinite permutations and combinations make them uniquely suited to today’s big data-based applications.
Because neural networks also have the unique capacity (known as fuzzy logic) to make sense of ambiguous, contradictory, or incomplete data, they can use controlled processes when no exact models are available.
According to a report published by Statista, in 2017, global data volumes reached close to 100,000 petabytes (i.e., one million gigabytes) per month; they are forecasted to reach 232,655 petabytes by 2021. With businesses, individuals, and devices generating vast amounts of information, all of that big data is valuable, and neural networks can make sense of it.
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, the 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.
Real-World and Industry 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.”
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.
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.”
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 an 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 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 develops 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 the next steps.” The app has 1.2 million users worldwide.
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 Challenges of Neural Networks
- Training: A common criticism of neural networks, particularly in robotics applications, is that excessive training for real-world operations is mandatory. One way to overcome that hurdle is by randomly shuffling training examples. Using a numerical optimization algorithm, small steps — rather than large steps — are taken to follow an example. Another way is by grouping examples in so-called mini-batches. Improving training efficiencies and convergence capabilities is an ongoing research area for computer scientists.
- Theoretical Issues: Unsolved problems remain, even for the most sophisticated neural networks. For example, despite its best efforts, Facebook still finds it impossible to identify all hate speech and misinformation by using algorithms. The company employs thousands of human reviewers to resolve the problem. In general, because computers aren’t human, their ability to be genuinely creative — prove math theorems, make moral choices, compose original music, or deeply innovate — is beyond the scope of neural networks and AI.
- Inauthenticity: The theoretical challenges we address above arise because neural networks don’t function exactly as human brains do — they operate merely as a simulacrum of the human brain. The specifics of how mammalian neurons code information is still unknown. Artificial neural networks don’t strictly replicate neural function, but rather use biological neural networks as their inspiration. This process allows statistical association, which is the basis of artificial neural networks. An ANN’s learning process isn’t identical to that of a human, thus, its inherent (at least for now) limitations.
The Future of Neural Networks
“We need to remember that artificial neural networks and deep learning are but one set of techniques for developing solutions to specific problems. Right now, they’re the ‘big thing,’” opines Richard Yonck, Founder and Lead Futurist of Intelligent Future Consulting and author of Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence.
Here are some likely future developments in neural network technologies:
- Fuzzy Logic Integration: Fuzzy logic recognizes more than simple true and false values — it takes into relative account concepts, like somewhat, sometimes, and usually. Fuzzy logic and neural networks are integrated for uses as diverse as screening job applicants, auto-engineering, building crane control, and monitoring glaucoma. Fuzzy logic will be an essential feature in future neural network applications.
- Pulsed Neural Networks: Recently, neurobiological experiment data has clarified that mammalian biological neural networks connect and communicate through pulsing and use the timing of pulses to transmit information and perform computations. This recognition has accelerated significant research, including theoretical analyses, model development, neurobiological modeling, and hardware deployment, all aimed at making computing even more similar to the way our brains function.
- Specialized Hardware: There’s currently a development explosion to create the hardware that will speed and ultimately lower the price of neural networks, machine learning, and deep learning. Established companies and startups are racing to develop improved chips and graphic processing units, but the real news is the fast development of neural network processing units (NNPUs) and other AI-specific hardware, collectively referred to as neurosynaptic architectures.
- Neurosynaptic chips are fundamental to the progress of AI because they function more like a biological brain than the core of a traditional computer. With its Brain Power technology, IBM has been a leader in the development of neurosynaptic chips. Unlike standard chips, which run continuously, Brain Power’s chips are event-driven and operate on an as-needed basis. The technology integrates memory, computation, and communication.
- Improvement of Existing Technologies: Enabled by new software and hardware as well as by current neural network technologies and the increased computing power of neurosynaptic architectures, neural networks have only begun to show what they can do. The myriad business applications of faster, cheaper, and more human-like problem-solving and improved training methods are highly lucrative.
- Robotics: There have been countless predictions about robots that will be able to feel like us, see like us, and make prognostications about the world around them. These prophecies even include some dystopian versions of that future, from the Terminator film series to Blade Runner and Westworld. There are so many things that have to happen before these systems can truly think in a fluid, non-brittle way. One of the critical factors I bring up in my book is the ability to establish and act on self-determined values in real-time, which we humans do thousands of times a day. Without this, these systems will fail every time conditions fall outside a predefined domain.”
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