Industry Use-Case of Neural Networks(NN)
Gaurav Rathi
Cloudops Engineer | RHCSA | RHCE | RHCoE(CEPH) ,(OpenShift) | Docker | Ansible | Kubernetes | OpenStack | AWS | MLOps | Jenkins |
What is a Neural Network?
Neural networks can be taught to perform complex tasks and do not require programming as conventional computers. They are massively parallel, extremely fast, and intrinsically fault-tolerant. They learn from experience, generalize from examples, and are able to extract essential characteristics from noisy data. They require significantly less development time and can respond to situations unspecified or not previously envisaged. They are ideally suited to real-world applications and can provide solutions to a hos' of currently impossible or commercially impractical problems. 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 eitl~er 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.
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
0 Marketing Real Estate
* Document & Form Processing
0 Machine printed character recognition Graphics recognition
0 Hand printed character recognition
o Cursive handwritten character recognition
* Finance Industry
o Market trading
0 Fraud detection
o Credit rating
* Food industry Odour/aroma analysis Product development
NEURAL NETWORKS- INDIAN.SCENARIO
A lot of opportunities exist in the country for Al technologies, especially neural computing applications. Though most of the work is being done around robotics and expert systems, there are also people and organizations capable of developing neural system products. The potential sectors of application range from manufacturing, banking, and finance, defense, telecommunications, pharmaceuticals to the holiday industry. A substantial amount of work is being done at the Centre for Artificial Intelligence and Robotics (CAIR, Bangalore) and the Institute for Robotics and Intelligent Systems (IRIS, Bangalore). They have developed a neural network for optical character recognition. The project is complete and awaits commercialization.
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.
Audi uses NN & 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 from 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 the 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 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, is demonstrating a solution for purely AI-based parking and driving in parking lots and on highways. In this process, the 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, 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.