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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 networksโ€™ essential algorithms, dependence on big data, latest innovations, and future. We include inside information from pioneers, applications for engineering and business, and additional resources.

A branch of machine learning, neural networks (NN), also known as artificial neural networks (ANN), are computational models โ€” 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.

Neural networksโ€™ human-like attributes and ability to complete tasks in infinite permutations and combinations make them uniquely suited to todayโ€™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.

Attributes of Neural Networks

With the human-like ability to problem-solve โ€” and apply that skill to huge datasets โ€” 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.
  • Prognosis: NNโ€™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.

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 โ€œrememberโ€ 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

Real-World and Industry Applications of Neural Networks

Coors Brewers, Ltd., based in Burton-upon-Trent, Britainโ€™s brewing capital, is proud of having the United Kingdomโ€™s top beer brands, a 20 percent share of the market, years of experience, and some of the best people in the business. Popular brands include Carling (the countryโ€™s best-selling lager), Grolsch, Coors Fine Light Beer, Col and Korenwolf.

Todayโ€™s customer has a wide variety of options regarding what he or she drinks. A drinkerโ€™s choice depends on various factors, including mood, venue, and occasion. Coorsโ€™ goal is to ensure that the customer chooses Coors brands no matter what the circumstances are.

Summary

The case concerns the development of a flavor prediction innovation (neural networks) by a leading British brewing company, Coors Brewers Ltd. Although a large amount of chemical and sensory data is available, techniques for determining the relationship between the two variables are lacking. Neural networks offer a quicker prediction method than the test panels traditionally used to test beer flavors1. Coors, motivated by the need to expand its market presence, set out to find a technique that would determine beer flavor based exclusively on analytical (chemical) data. The technique would enable the firm to create brands that could meet the diverse tastes and expectations of drinkers.

Coors developed the technique in a three-phase project. Initially, Coors used a single neural network to determine how chemical composition influences flavor based on sensory and analytical data it had collected. To implement it, Coors trained the MLP neural network using different combinations of sensory and analytical data.

This enabled the firm to analyze each quality and sensory output individually. Normalized data allowed the network to compare different outputs and minimize network error. However, the technique could not define meaningful relationships between output and input data because only a single quality was analyzed at a time. This limited data variability. In addition, โ€˜noiseโ€™ created by extraneous inputs affected the techniqueโ€™s effectiveness.

Coors developed an improved version that included a software switch to eliminate the effect of insignificant inputs and reduce the network error. The method was exhaustive as it evaluated all input combinations. However, it yielded a large dataset (combinations) that could not be solved with the available computational methods. To overcome this limitation, Coors developed a genetic algorithm to determine the input/output combination that could yield a lower network error and thus, predict flavor more accurately. The results indicated that the trained genetic algorithm could accurately predict a number of flavors using trained chemical data. The current technique can only predict a few flavors. Moreover, it does not take into consideration other sensory factors.

Why is beer flavor important to Coorsโ€™ profitability?

Customer beer preferences and choices are never constant. A customerโ€™s drink choice often depends on the occasion, settings, and psychological state1. Coors, as one of the leading firms in the British brewing industry, intends to expand its flavors to reflect the changing and diverse customer preferences and needs. The beer flavor is one way the company can differentiate its products in order to provide customers a broad array of drinks that suit all situations.

The beer flavor is an important quality that drinkers take into consideration in choosing a brand-appropriate for a particular occasion. The traditional testing methods (panel tests) are time-consuming. In contrast, alternative techniques that are faster and accurate can give a company a strong competitive advantage in the industry. Coors aims to come up with a novel method of predicting flavor based on chemical data alone. This will enable the company to develop beer brands that meet customer expectations.

What is the objective of the neural network used at Coors?

The aim of Coorsโ€™ neural network is to select input/output combinations with the least network error in order to predict beer flavors more accurately. Such combinations will improve the accuracy and speed of predicting beer flavors. The alternative method (panel testing) is slow and requires multiple inputs. In contrast, neural networks have the ability to predict beer flavor using only the chemical input data.

Coors implemented different versions of neural networks in a bid to develop a refined prediction technique. The genetic algorithm technique developed synthesizes chemical inputs and releases sensory outputs, which define a beerโ€™s flavor. The company uses input and output data that have been gathered from panel testing over the years1. The role of the neural network is to model the link between inputs and outputs. Coors has improved the performance of its neural networks over the years producing a highly accurate prediction tool. The prediction accuracy of the genetic algorithm tool stems from its ability to reduce network error.

Why were the results of Coorsโ€™ neural network initially poor, and what was done to improve the results?

Coors launched its project by implementing a single neural network. This product was made up of a two-layered MLP sourced from an external developer. It focused on a single input (chemical quality) and output (flavor). Using normalized data drawn from input combinations, the network was trained to do cross-comparisons of different sensory outputs. However, the single neural network had two significant limitations that affected the quality of the results. First, it used a single input variable, which reduced data variability. This meant that no meaningful relationships could be modeled. Second, โ€˜noiseโ€™ caused by extraneous inputs affected the networkโ€™s effectiveness.

To improve data variability, Coors expanded the product range. This generated more analytical input data for training the network. The second modification was the introduction of a software switch that allowed the training of the network using input/output combinations making up the probability space1. This exhaustive search, though effective in removing the โ€˜noiseโ€™, generated many combinations. The huge number of combinations per each sensory attribute (up to 16.7 per flavor) was mathematically impossible to compute1. A gene algorithm method, which could select relevant inputs, was developed to replace this method. The gene algorithm technique is a more accurate method of searching inputs with minimal network error for accurate flavor prediction.

Conclusion :-

The use of Artificial Neural Networks (ANN) to simulate complex decisions can results in increased opportunity if properly implemented. In the case of Coors, significant time, energy, and money were spent developing an ANN that incorrectly applied bias and noise to the decision domain. This resulted in the response being invalid to the issues analyzed. Coors should have recognized these potential issues in the ANN scoping and construction phase, thus eliminating the potential invalid outcome. If Coors had used a Multiple Input, Multiple Output (MIMO) variant of the Multilayer Perceptron (MLP) architecture, I believe the outcome would had been ground breaking within the industry. Regardless of the ANN model used, human testing will still be required because taste is ultimately a difficult sense to distill into logical parameters for analysis. The results of an appropriate constructed ANN would benefit Coors through reduced test variant development requirements and their associated costs.

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