Research for industry usecases of Neural Network..
Welcome to my blog ,today we will see What is Neural Network ? in brief and It’s Industry Use Case .
📌Research for industry usecases of Neural Networks and create a blog, Article or Video elaborating how it works.
Today, neural networks (NN) are revolutionizing business and everyday life, bringing us to the next level in artificial intelligence (AI). By emulating the way interconnected brain cells function, NN-enabled machines (including the smartphones and computers that we use on a daily basis) are now trained to learn, recognize patterns, and make predictions in a humanoid fashion as well as solve problems in every business sector.
In this article, we offer the most useful guide to neural networks’ essential algorithms, dependence on big data, latest innovations, and future. We include inside information from pioneers, applications for engineering and business, and additional resources.
What Are Neural Networks?
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.
The most groundbreaking aspect of neural networks is that once trained, they learn on their own. In this way, they emulate human brains, which are made up of neurons, the fundamental building block of both human and neural network information transmission.
“Human brains and artificial neural networks do learn similarly,” explains Alex Cardinell, 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. “In both cases, neurons continually adjust how they react based on stimuli. If something is done correctly, you’ll get positive feedback from neurons, which will then become even more likely to trigger in a similar, future instance. Conversely, if neurons receive negative feedback, each of them will learn to be less likely to trigger in a future instance,” he notes.
How the Biological Model of Neural Networks Functions
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.
How Artificial Neural Networks Function
ANNs are statistical models designed to adapt and self-program by using learning algorithms in order to understand and sort out concepts, images, and photographs. For processors to do their work, developers arrange them in layers that operate in parallel. The input layer is analogous to the dendrites in the human brain’s neural network. The hidden layer is comparable to the cell body and sits between the input layer and output layer (which is akin to the synaptic outputs in the brain). The hidden layer is where artificial neurons take in a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes. These weighted inputs generate an output through a transfer function to the output layer.
How Do You Train a Neural Network?
Once you’ve structured a network for a particular application, training (i.e., learning), begins. There are two approaches to training. 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.
There’s still a long way to go in the area of unsupervised learning. “Getting information from unlabeled data, [a process] we call unsupervised learning, is a very hot topic right now, but clearly not something we have cracked yet. It’s something that still falls in the challenge column,” observes Université de Montréal’s Yoshua Bengio in the article “The Rise of Neural Networks and Deep Learning in Our Everyday Lives.”
Bengio is referring to the fact that the number of neural networks can’t match the number of connections in the human brain, but the former’s ability to catch up may be just over the horizon. Moore’s Law, which states that overall processing power for computers will double every two years, gives us a hint about the direction in which neural networks and AI are headed. Intel CEO Brian Krzanich affirmed at the 2017 Computer Electronics Show that “Moore’s Law is alive and well and flourishing.” Since its inception in the mid-20th century, neural networks’ ability to “think” has been changing our world at an incredible pace.
A Brief History of Neural Networks
Neural networks date back to the early 1940s when mathematicians Warren McCulloch and Walter Pitts built a simple algorithm-based system designed to emulate human brain function. Work in the field accelerated in 1957 when Cornell University’s Frank Rosenblatt conceived of the perceptron, the groundbreaking algorithm developed to perform complex recognition tasks. During the four decades that followed, the lack of computing power necessary to process large amounts of data put the brakes on advances. In the 2000s, thanks to the advent of greater computing power and more sophisticated hardware, as well as to the existence of vast data sets to draw from, computer scientists finally had what they needed, and neural networks and AI took off, with no end in sight. To understand how much the field has expanded in the new millennium, consider that ninety percent of internet data has been created since 2016 That pace will continue to accelerate, thanks to the growth of the Internet of Things (IoT).
For more background and an expansive timeline, read “The Definitive Guide to Machine Learning: Business Applications, Techniques, and Examples.”
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 are able to 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, 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.
- 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.
Tasks Neural Networks Perform
Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:
- Classification: NNs organize patterns or datasets into predefined classes.
- Prediction: They produce the expected output from given input.
- Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
- Associating: You can train neural networks to “remember” patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.
Neural networks are fundamental to deep learning, a robust set of NN techniques that lends itself to solving abstract problems, such as bioinformatics, drug design, social network filtering, and natural language translation. Deep learning is where we will solve the most complicated issues in science and engineering, including advanced robotics. As neural networks become smarter and faster, we make advances on a daily basis.
Audi at NIPS: new approaches to AI on the way to autonomous driving
◾ Conference for artificial intelligence in California
◾ Audi innovation project: Neural network generates highly precise 3D models of the environment
◾ Networked worldwide in the field of AI technology
On the road to autonomous driving, Audi continues powering ahead at top speed: The company is exhibiting an innovative pre-development project at the world’s most important symposium for artificial intelligence (AI) — the NIPS conference in Long Beach, California (USA). The project uses a mono camera that uses AI to generate an extremely precise 3D model of a vehicle’s environment. The conference is co-sponsored by Audi and takes place December 4 to 9.
The new Audi A8 is the first car in the world developed for conditional automated driving at Level 3 (SAE). The Audi AI traffic jam pilot handles the task of driving in slow-moving traffic up to 60 km/h (37.3 mph), provided that laws in the market allow it and the driver selects it. A requirement for automated driving is a mapped image of the environment that is as precise as possible — at all times. Artificial intelligence is a key technology for this.
A project team from the Audi subsidiary Audi Electronics Venture (AEV) now is presenting a mono camera at the Conference and Workshop on Neural Information Processing Systems (NIPS) that uses artificial intelligence to generate an extremely precise 3D model of the environment. This technology makes it possible to capture the exact surroundings of the car.
A conventional front camera acts as the sensor. It captures the area in front of the car within an angle of about 120 degrees and delivers 15 images per second at a resolution of 1.3 megapixels. These images are then processed in a neural network. This is where semantic segmenting occurs, in which each pixel is classified into one of 13 object classes. This enables the system to identify and differentiate other cars, trucks, houses, road markings, people and traffic signs.
The system also uses neural networks for distance information. The visualization is performed here via ISO lines — virtual boundaries that define a constant distance. This combination of semantic segmenting and estimates of depth produces a precise 3D model of the actual environment.
Audi engineers had previously trained the neural network with the help of “unsupervised learning.” In contrast to supervised learning, unsupervised learning is a method of learning from observations of circumstances and scenarios that does not require pre-sorted and classified data. The neural network received numerous videos to view of road situations that had been recorded with a stereo camera. As a result, the network learned to independently understand rules, which it uses to produce 3D information from the images of the mono camera. The project of AEV holds great potential for the interpretation of traffic situations.
Along with the AEV, two partners from the Volkswagen Group are also presenting their own AI topics at the Audi booth for this year’s NIPS. The Fundamental AI Research department within the Group IT’s Data:Lab focuses on unsupervised learning and optimized control through variational inference, an efficient method for representing probability distributions.
Finally, the Audi team from the Electronics Research Laboratory of Belmont, California, are demonstrating a solution for purely AI-based parking and driving in parking lots and on highways. In this process, lateral guidance of the car is completely carried out through neural networks. The AI learns to independently generate a model of the environment from camera data and to steer the car. This approach requires no highly precise localization or highly precise map data.
In developing autonomous driving cars, Audi is benefiting from a large network in the artificial intelligence field of technology. The network includes companies in the hotspots of Silicon Valley, in Europe and in Israel.
In 2016, Audi became the first automobile manufacturer to participate at NIPS with its own exhibition booth. The brand appears again this year as a sponsor of NIPS and is seeking to further develop its network in California. AI specialists can also learn about employment opportunities with Audi there.