Applications of Neural Networks in the Real World
In a 3D Virtual Environment, a computer generates sensory impressions that are sent to a user through their senses. The type and quality of these impressions determine the level of immersion and the feeling of presence in the Virtual Environment. Ideally, information would be sent to the user through all their senses, using high resolution, high quality, and consistency at all times. Inspired by the behavior of the human brain, artificial neural networks are a very important field within artificial intelligence. Its objective is to try to find models that solve problems that are difficult to solve using conventional algorithmic techniques. Applications of neural networks are varied and their usage is across fields such as medicine, industry, education, and research, among others.
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How do Neural Networks Work?
As mentioned, the functioning of networks is similar to that of the human brain. Networks receive a series of input values and each of these inputs reaches a node called a neuron. The neurons of the network are in turn grouped into layers that form the neural network. Each of the neurons in the network in turn has a weight, a numerical value, with which it modifies the input received.
The new values obtained continue their way through the network. Below images will differentiate between information processing in human neurons and artificial neural networks.
Image 1 – A neuron of the human brain
Image 2 – An artificial neural network
Once the end of the network has been reached, an output is obtained which will be the prediction calculated by the network. The more layers the network has and the more complex it is, the more complex the functions it can perform.
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Applications of Neural Networks
Below are some of the groundbreaking applications of neural networks –
YouTube Recommender
YouTube is the world’s largest company for sharing, creating, and viewing audiovisual content. YouTube recommendations are responsible for helping more than 1 billion users discover personalized content. One of the biggest challenges they had to face when creating the algorithm is the amount of data that is uploaded to YouTube per second. Therefore, this neural network must have the ability to be sensitive (responsive) both to the latest content uploaded to the platform and to the user’s interactions with it.
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Dynamic Pricing on Amazon
Amazon is the undisputed leader in e-commerce. It is a known fact that it uses dynamic prices. According to a study, Amazon varies prices more than 2.5 million times a day. The challenge of this neural network is that prices in the digital age must be set in real-time based on the supply and demand of a certain product during a limited period. Companies like Walmart or Uber use these algorithms to offer prices that are more competitive to their customers.
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Identify Risks in Banking
HSBC is one of the banks that uses neural networks to transform the way they process loans and mortgages. This company uses neural networks and artificial intelligence algorithms to analyze the behavior of old clients and thus be able to give an estimate of the risk for a new client when acquiring a mortgage or loan.
Customize Marketing Strategies
In recent years, several companies use artificial intelligence to improve their marketing strategies. Neural networks have been helpful in processing a large amount of data such as – buyer profiles, purchase patterns, or other specific data for each company. These types of characteristics make them the perfect algorithms to analyze the market and propose a personalized marketing strategy per client. Sephora or Starbucks are two of the companies that use this type of artificial intelligence to increase their profits.
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Attributes of Neural Networks
Neural Networks have the capabilities to solve problems in massive datasets. Some of the powerful features of Neural Networks are –
Adaptive Learning – Neural networks can develop both linear, non-linear as well as complex relationships basis previous knowledge
Self-Organization – Neural networks can cluster and classify vast amounts of data, thereby contributing towards solving complicated visual problems.
Real-Time Operation – Neural networks can be highly responsive and give real-time answers. This feature is very useful in defense services and autonomous vehicles
Prognosis – Neural networks can make forecasts, offering applicability in a range of applications, such as weather and traffic forecast.
Fault Tolerance – Neural networks can fill in for the missing data in a huge data set with complicated values. The fault tolerance attribute is especially helpful where the failure of electronic devices may happen at any time.
Conclusion
In conclusion, we can say that neural networks are algorithms that can solve very complex problems in a very precise way. These types of algorithms need a large amount of training data and considerable computational effort and time to train them. The main challenge for companies when creating these algorithms is to have a base of the quantity and quality of data necessary to build a neural network, but for years the importance of using data as an asset has been highlighted, and most companies have a database furnished enough to take advantage of it.
FAQs
How hard is it to create a neural network?
It is simple to create simple neural networks but they are not of much use and need to be scaled up. It is a challenging task to train deep learning neural networks. Data scientists use a stochastic gradient descent algorithm, where model weights are updated using the backpropagation of the error algorithm. Optimization is a quite difficult task.
What is special about neural networks?
The idea behind neural networks is to ensure that intelligence can emerge from low-level processing. They follow logic-based approaches and achieve almost human-level intelligence. Neural networks make use of logical rules in the manipulation of symbols that represent certain concrete or abstract concepts.
What are artificial neural networks?
Artificial neural networks are calculation systems or algorithms inspired by the functioning of the human. In artificial neural networks, computational models work as a neuron to carry information to another through the synapse. It is basically about imitating what happens in the human brain but inside a computer.
How is a neural network trained?
Training a neural network consists of adjusting each of the weights of the inputs of all the neurons that are part of the neural network so that the responses of the output layer fit as closely as possible to the data that we know.
Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio
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