NEURAL NETWORKS: HOW IT WORKS AND ITS INDUSTRY USE CASES

This article looks at the necessity of artificial intelligence and specifically neural systems in today's competitive business world. Some fundamental and domain applications of neural networks are discussed and highlighted.

INTRODUCTION

Over the past few years, technology has become very dynamic. It is fuelling itself at an ever-increasing rate. Computers are a prime component of this whole revolution that can help fight diseases by designing new drugs, computers can design better computers, computers simulate reality, and what not! This is a very exciting time for technology as the traditional boundaries are now becoming blurred.  

We often think that computers can only decide on whether a statement true or false. Such a logical statement are linked together to form a series of rules. To program a computer, all that needed is to precisely define the problem, write specifications and use these rules, exactly what to do. But it is difficult to program a computer for a more subjective task like, predicting what the weather is going to be, what the price of bitcoin tomorrow. These tasks are fact impossible to define accurately. The pattern needs to be recognized that is a very complex task for a computer.

So we need to make computers smarter that have the capability to make judgments, guesses and to change opinions. We humans learn by example and do not need to see every example to make a guess, a judgment based upon what we have been taught. 

As we know, with A lot of competition within all areas of industry, intelligent business decisions are more important than ever. Even more important is the case for military applications. Data analysis plays an important role as a critical strategic weapon in the business and operations of the armed forces. The inherent limitations of existing statistical technology make normal data analysis very tedious and often costly process-requiring assumptions, rigid rules, force-fitting of data, as well as extensive trial and error experimentation and programming. Interpreted errors, biases and mistakes are introduced. Valuable competitive insights are lost. Technology-based on artificial intelligence (Al) will soon become the only way to generate such systems economically. 

NEURAL NETWORKS

Neural computers are based on the biological processes of the brain. Terms like can learn brain-like, massively parallel, learning machines and revolutionary have been used to describe neural computing.  

Conventional computers concentrate on emulating human thought processes, rather than actually how they are achieved by the human brain. Neural computers, however, take an alternative approach in that they directly model the biological structure of the human brain and the way it processes information. This necessitates a new kind of architecture, which, like the human brain, consists of a large number of heavily interconnected processing elements operating in a parallel manner. 

Neural networks are mathematical models, originally inspired by biological processes in the human brain. They are constructed from a number of simple processing elements interconnected by weighted pathways to form networks. Each element computes its output as a non-linear function of its weighted inputs. When combined into networks, these processing elements can implement arbitrarily complex non-linear functions which can be used to solve classification, prediction, or optimization problems.

What is Neural Network 

Neural networks can be taught to perform complex tasks and do not require programming as conventional computers. In simple terms, a neural network is made up of a number of processing elements called neurons, whose interconnections are called synapses. Each neuron accepts inputs from either the external world or from the outputs of other neurons. Output signals from all neurons eventually propagate their effect across the entire network to the final layer where the results can be output to the real world. The synapses have a processing value or weight, which is learned during the training of the network. The functionality and power of the network primarily depend on the number of neurons in the network, the interconnectivity patterns or - topology, and the value of the weights assigned to each synapse.

ADVANTAGES OF NEURAL NETWORKS

 As seen already, neural computers have the ability to learn from experience, to improve their performance, and to adapt their behavior to new and changing environments. Unlike conventional rule-based systems, neural networks are not programmed to perform a particular task using rules. Instead, they are trained on historical data, using a learning algorithm.

1.  Neural networks can provide highly accurate and robust solutions for complex non-linear tasks, such as fraud detection, business lapse/churn analysis, risk analysis, and data mining. 
2. One of their main benefits is that the method for performing a task need not be known in advance; instead, it is automatically inferred from the data. Once learned, the method can be quickly and easily adjusted to track changes in the business environment.
3.  A further advantage of neural networks over conventional rule-based systems and fuzzy systems is that once trained, they are far more efficient in their storage requirements and operation; a single mathematical function can replace a large number of rules. 
4. An added benefit of this more compact mathematical representation is that it introduces a natural form of regularisation or generalization. This makes neural systems extremely robust to noisy, imprecise, or incomplete data. 

APPLICATIONS OF NEURAL NETWORKS 

Artificial neural networks have become an accepted information analysis technology in a variety of disciplines. This has resulted in a variety of commercial applications (in both products and services) of neural network technology (The applications that neural networks have been put to and the potential possibilities that exist in a variety of civil and military sectors are tremendous.) 

Given below are domains of commercial applications of neural network technology. 

 Business - Marketing, Real Estate

Document & Form Processing - Machine printed character recognition, Graphics recognition, Handprinted character recognition, Cursive handwritten character recognition 

Finance Industry - Market trading, Fraud detection, Credit rating 

Food industry - Odour/aroma analysis, Product development, Quality assurance

Energy Industry - Electrical load forecasting, Hydroelectric dam operation, Natural gas 

Manufacturing Process control - Quality control 

Medical & Health Care Industry - Image analysis Drug development, Resource allocation 

Science & Engineering - Chemical engineering, Electrical engineering, Weather forecasting

 Transportation & Communication 

Some applications of neural networks are:

• Forecasting the Behaviour of Complex Systems 
• Signal Processing
• Data Compression 
• Paint Quality Inspection 
• DNA Sequence Analysis

INDUSTRY USE CASES 

Top Companies Using Artificial Neural Network(ANN)

• Nvidia Corp. 
• Alphabet 
• Salesforce.com 
• Amazon.com 
• Microsoft Corp
• IBM 
• Facebook 

NVIDIA CASE STUDIES 

NIVIDA used a Neural network to develop their one SDK name is TensorRT. it is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications.

TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyper-scale data centers, embedded, or automotive product platforms.

TensorRT is built on CUDA, NVIDIA’s parallel programming model, and enables you to optimize inference leveraging libraries, development tools, and technologies in CUDA-X? for artificial intelligence, autonomous machines, high-performance computing, and graphics.

TensorRT provides INT8 and FP16 optimizations for production deployments of deep learning inference applications such as video streaming, speech recognition, recommendation, fraud detection, and natural language processing. Reduced precision inference significantly reduces application latency, which is a requirement for many real-time services, as well as autonomous and embedded applications.

With TensorRT, developers can focus on creating novel AI-powered applications rather than performance tuning for inference deployment.

1. Reduce Mixed Precision

Maximizes throughput by quantizing models to INT8 while preserving accuracy

2. Layer and Tensor Fusion

Optimizes use of GPU memory and bandwidth by fusing nodes in a kernel

3. Kernel Auto-Tuning

Selects best data layers and algorithms based on the target GPU platform

4. Dynamic Tensor Memory

Minimizes memory footprint and reuses memory for tensors efficiently

5. Multi-Stream Execution

Uses a scalable design to process multiple input streams in parallel

6. Time Fusion

Optimizes recurrent neural networks over time steps with dynamically generated kernels

World-Leading Inference Performance

TensorRT powered NVIDIA’s wins across all performance tests in the industry-standard MLPerf Inference benchmark. It accelerates every model across the data center and edge in computer vision, speech-to-text, natural language understanding (BERT), and recommender systems.

Accelerates Every Inference Platform

TensorRT can optimize and deploy applications to the data center, as well as embedded and automotive environments. It powers inference solutions such as NVIDIA TAO, NVIDIA DRIVE?, NVIDIA Clara?, and NVIDIA Jetpack?.

TensorRT is also integrated with application-specific SDKs such as NVIDIA DeepStream, Jarvis, Merlin?, Maxine?, and Broadcast Engine to provide developers a unified path to deploy intelligent video analytics, conversational AI, recommender systems, video conference, and streaming apps in production.

CONCLUSION

Neural computers perform very favorably in business and military applications. They do not require explicit programming by an expert and are robust to noisy, imprecise, or incomplete data. The reason one should use neural computing technology is the competition! 

Thank you! Hope you like this article!!

Add alt text

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TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers, embedded, or automotive product platforms.

TensorRT is built on CUDA, NVIDIA’s parallel programming model, and enables you to optimize inference leveraging libraries, development tools, and technologies in CUDA-X? for artificial intelligence, autonomous machines, high-performance computing, and graphics.

TensorRT provides INT8 and FP16 optimizations for production deployments of deep learning inference applications such as video streaming, speech recognition, recommendation, fraud detection, and natural language processing. Reduced precision inference significantly reduces application latency, which is a requirement for many real-time services, as well as autonomous and embedded applications.

With TensorRT, developers can focus on creating novel AI-powered applications rather than performance tuning for inference deployment.

Add alt text

No alt text provided for this image

1. Reduce Mixed Precision

Maximizes throughput by quantizing models to INT8 while preserving accuracy

2. Layer and Tensor Fusion

Optimizes use of GPU memory and bandwidth by fusing nodes in a kernel

3. Kernel Auto-Tuning

Selects best data layers and algorithms based on the target GPU platform

4. Dynamic Tensor Memory

Minimizes memory footprint and reuses memory for tensors efficiently

5. Multi-Stream Execution

Uses a scalable design to process multiple input streams in parallel

6. Time Fusion

Optimizes recurrent neural networks over time steps with dynamically generated kernels

World-Leading Inference Performance

TensorRT powered NVIDIA’s wins across all performance tests in the industry-standard MLPerf Inference benchmark. It accelerates every model across the data center and edge in computer vision, speech-to-text, natural language understanding (BERT), and recommender systems.

Accelerates Every Inference Platform

TensorRT can optimize and deploy applications to the data center, as well as embedded and automotive environments. It powers inference solutions such as NVIDIA TAO, NVIDIA DRIVE?, NVIDIA Clara?, and NVIDIA Jetpack?.

TensorRT is also integrated with application-specific SDKs such as NVIDIA DeepStream, Jarvis, Merlin?, Maxine?, and Broadcast Engine to provide developers a unified path to deploy intelligent video analytics, conversational AI, recommender systems, video conference, and streaming apps in production.

CONCLUSION

Neural computers perform very favourably in business and military applications. They do not require explicit programming by an expert and are robust to noisy, imprecise or incomplete data. The reason one should use neural computing technology is the competition! 

Thank you! Hope you like this article!!

This article looks at the necessity of artificial intelligence and specifically neural systems in today's competitive business world. Some fundamental and domain applications of neural networks are discussed and highlighted.

INTRODUCTION

Over the past few years, technology has become very dynamic. It is fuelling itself at an ever increasing rate. Computers are a prime component of this whole revolution that can help fight diseases by designing new drugs, computer can design better computers, computer simulate reality and what not! This is a very exciting time for technology as the traditional boundaries are now becoming blurred.  

We often think that computer can only decide on whether statement true or false. Such a logical statement are linked together to form a series of rules. To program a computer, all that needed is to precisely define the probelm, write specidfication and use these rules, exaclty what to do. But it is dififcult to a program a computer for more subjective task like, prediction what the weather is going to be, what the proce of bitcoin tomarrow. These task are the fact imposisible define accurately. Pattern need ot be recognised that are very complex task for a computer.

So we need to make computer more smart that have capability to make judgements, guesses and to change opinions. We humans learn by example and do not need to see every examples to make a guess, a judgement based upon what we have been taught. 

As we know, A lot of competition within all areas of industry, intelligent business decisions are more important than ever. Even more important is the case for military applications. Data analysis plays an important role as a critical strategic weapon in business and operations of the armed forces. The inherent limitations of existing statistical technology makes normal data analysis a very tedious and often costly process-requiring assumptions, rigid rules, force fitting of data, as well as extensive trial and error experimentation and programming. Interpreted errors, biases and mistakes are introduced. Valuable competitive insights are lost. Technology based on artificial intelligence (Al) will soon become the only way to generate such systems economically. 

NEURAL NETWORKS

Neural computers are based on the biological processes of the brain. Terms like can learn brain-like, massively parallel, learning machines and revolutionary have been used to describe neural computing.  

Conventional computers concentrate on emulating human thought processes, rather than actually how they are achieved by the human brain. Neural computers, however, take an alternative approach in that they directly model the biological structure of the human brain and the way it processes information. This necessitates a new kind of architecture, which, like the human brain, consists of a large number of heavily interconnected processing elements operating in parallel manner. 

Neural networks are mathematical models, originally inspired by biological processes in the human brain. They are constructed from a number of simple processing elements interconnected by weighted pathways to form networks. Each element computes its output as a non-linear function of its weighted inputs. When combined into networks, these processing elements can implement arbitrarily complex non-linear functions which can be used to solve classification, prediction or optimisation problems.

What is Neural Network 

Neural networks can be taught to perform complex tasks and do not require programming as conventional computers. In simple terms, a neural network is made up of a number of processing elements called neurons, whose interconnections are called synapses. Each neuron accepts inputs from either the external world or from the outputs of other neurons. Output signals from all neurons eventually propagate their effect across the entire network to the final layer where the results can be output to the real world. The synapses have a processing value or weight, which is learnt during training of the network. The functionality and power of the network primarily depends on the number of neurons in the network, the interconnectivity patterns or - topology, and the value of the weights assigned to each synapse.

ADVANTAGES OF NEURAL NETWORKS

 As seen already, neural computers have the ability to learn from experience, to improve their performance and to adapt their behaviour to new and changing environment. Unlike conventional rule-based systems, neural networks are not programmed to perform a particular task using rules. Instead, they are trained on historical data, using a learning algorithm.

 Neural networks can provide highly accurate and robust solutions for complex non-linear tasks, such as fraud detection, business lapse/churn analysis, risk analysis and data-mining. 

One of their main benefits is that the method for performing a task need not be known in advance; instead it is automatically inferred from the data. Once learned, the method can be quickly and easily adjusted to track changes in the business environment.

 A further advantage of neural networks over conventional rule-based systems and fuzzy systems is that, once trained, they are far more efficient in their storage requirements and operation; a single mathematical function can replace a large number of rules. 

An added benefit of this more compact mathematical representation is that it introduces a natural form of regularisation or generalisation. This makes neural systems extremely robust to noisy, imprecise or incomplete data. 

APPLICATIONS OF NEURAL NETWORKS 

Artificial neural networks have become an accepted information analysis technology in a variety of disciplines. This has resulted in a variety of commercial applications (in both products and services) of neural network technology (The applications that neural networks have been put to and the potential possibilities that exist in a variety of civil and military sectors are tremendous.) 

Given below are domains of commercial applications of neural network technology. 

 Business - Marketing, Real Estate

Document & Form Processing - Machine printed character recognition, Graphics recognition, Hand printed character recognition, Cursive handwritten character recognition 

Finance Industry - Market trading, Fraud detection, Credit rating 

Food lndustry - Odour/aroma analysis, Product development, Quality assurance

Energy Industry - Electrical load forecasting, Hydroelectric dam operation, Natural gas 

Manufacturing Process control - Quality control 

Medical & Health Care Industry - Image analysis Drug development, Resource allocation 

Science & Engineering - Chemical engineering, Electrical engineering, Weather forecasting

 Transportation & Communication 

Some applications of naural networks are:

Forecasting the Behaviour of Complex Systems 

Signal Processing

Data Compression 

Paint Quality Inspection 

DNA Sequence Analysis

INDUSTRY USE CASES 

Top Companies using Artificial Neural Network(ANN)

Nvidia Corp. 

Alphabet 

Salesforce.com 

Amazon.com 

Microsoft Corp

IBM 

Facebook 

NVIDIA CASE STUDIES 

NIVIDA used Nueral network to develop their one SDK name is TensorRT. it is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications.

Add alt text

No alt text provided for this image

TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers, embedded, or automotive product platforms.

TensorRT is built on CUDA, NVIDIA’s parallel programming model, and enables you to optimize inference leveraging libraries, development tools, and technologies in CUDA-X? for artificial intelligence, autonomous machines, high-performance computing, and graphics.

TensorRT provides INT8 and FP16 optimizations for production deployments of deep learning inference applications such as video streaming, speech recognition, recommendation, fraud detection, and natural language processing. Reduced precision inference significantly reduces application latency, which is a requirement for many real-time services, as well as autonomous and embedded applications.

With TensorRT, developers can focus on creating novel AI-powered applications rather than performance tuning for inference deployment.

Add alt text

No alt text provided for this image

1. Reduce Mixed Precision

Maximizes throughput by quantizing models to INT8 while preserving accuracy

2. Layer and Tensor Fusion

Optimizes use of GPU memory and bandwidth by fusing nodes in a kernel

3. Kernel Auto-Tuning

Selects best data layers and algorithms based on the target GPU platform

4. Dynamic Tensor Memory

Minimizes memory footprint and reuses memory for tensors efficiently

5. Multi-Stream Execution

Uses a scalable design to process multiple input streams in parallel

6. Time Fusion

Optimizes recurrent neural networks over time steps with dynamically generated kernels

World-Leading Inference Performance

TensorRT powered NVIDIA’s wins across all performance tests in the industry-standard MLPerf Inference benchmark. It accelerates every model across the data center and edge in computer vision, speech-to-text, natural language understanding (BERT), and recommender systems.

Accelerates Every Inference Platform

TensorRT can optimize and deploy applications to the data center, as well as embedded and automotive environments. It powers inference solutions such as NVIDIA TAO, NVIDIA DRIVE?, NVIDIA Clara?, and NVIDIA Jetpack?.

TensorRT is also integrated with application-specific SDKs such as NVIDIA DeepStream, Jarvis, Merlin?, Maxine?, and Broadcast Engine to provide developers a unified path to deploy intelligent video analytics, conversational AI, recommender systems, video conference, and streaming apps in production.

CONCLUSION

Neural computers perform very favourably in business and military applications. They do not require explicit programming by an expert and are robust to noisy, imprecise or incomplete data. The reason one should use neural computing technology is the competition! 

Thank you! Hope you like this article!!