Future Application of Artificial Neural Networks

Artificial neural networks simplify life and are widely used in computer learning. In order to analyse the future of artificial neural networks, it is essential to assess the current implementation using a computer. To understand how neural networks are used in applications, the following examples are given:

• Banking: Through a digital platform, banks use neural networks to identify fraudulent activity and enhance customer services (Qi et al., 2019, p. 4).
• Facebook's application uses ANNs to identify family and friends in shared photos in the social media. Similar to this, Instagram uses ANNs to suggest hashtags or emojis based on the text that is being typed.
• Customer creditworthiness, consumer demand forecasting, inventory level optimization, and stock market forecasting are examples of business analytics in finance.

People could not stay connected with one another 24/7 to share information prior to the invention of Facebook, Instagram, and other applications. In 2020, Dutta et al. (p. 5) Thanks to the use of cutting-edge technology, it was made possible. Obtaining the necessary information was also a difficult task before Google, like looking for a needle in a haystack. Despite some obstacles, artificial neural networks have a promising future in computer learning. To solve the problems with neural networks, the challenges must be worked on.

When neural networks are combined with complementary technology, such as symbolic functions, their weaknesses can be easily made up for. The difficult part is figuring out how to get the systems to cooperate to produce the desired result. Engineers have, however, already been working on it. Everything has the capacity to scale up for increased complexity and power. Engineers can now produce more powerful and affordable CPUs and GPUs thanks to technological advancement, which makes it possible to create algorithms with greater efficiency.

As a result, they can create a neural network that can process more data more quickly. As a result, instead of needing 10,000 examples, it learns to recognise patterns with just 1000. (Mishra et al., 2020, p.7). Also, neural networks can grow horizontally to be used in a variety of applications rather than being developed vertically for more powerful processing.

Neural networks can potentially be adopted by hundreds of business sectors to run their operations more effectively, create new products, reach out to new customers, and increase consumer safety. Truly speaking, it is underused. With engineers' efforts for greater accessibility, greater acceptance, and more innovation in the industry or applications, the world economy can expand.

However, there are still difficulties with neural network deployment and training procedures. In order to comprehend the behaviour of the model as it reflects the training data, a lot of weights must be set (Lee et al., 2021, p. 3). It also requires an explanation for the model's extensive use of numerical weight values. Working on artificial intelligence is crucial for the deployment of neural networks in industries like health care or law. It'll make choices that will alter your life. Additionally, networks' resilience must increase in order to be deployed in specific contexts where they can affect people's lives significantly or change them, like self-driving cars.

For instance: When the stickers are applied to self-driving cars, for instance, they are unable to recognise the stop signal. Understanding the information or data that is being processed is therefore necessary for solving the problem. Therefore, it is important to conduct research in a number of areas to implement artificial intelligence, including the creation of hybrid AI. Connecting neural networks and symbolic artificial intelligence is the main goal. Artificial intelligence can be used in any industry or field.

To produce genuine results, though, the programming data needs to be improved. In important fields like law or health care, choices can have a significant impact on people's lives. Neural networks effectively suggest text, image recognition, or information for applications over social media or the internet. But since it must be based on zero-error results to decide on people's lives, its application in the key fields can help build a stronger health care system. Engineers must therefore ensure that any errors or issues are addressed in order to make AI reliable while implementing it in key fields.

In summary, artificial intelligence is essential to the advancement of society. Artificial neural networks are thus used. ANNs play a significant role in easing people's lives when using social media, starting businesses, and looking up information. However, there are significant obstacles like black boxes, time constraints, and data volume. However, engineers can work on improving CPU efficiency at a low cost to make neural networks practical. As engineers work on the important issues related to artificial neural networks, major fields like health care and law could benefit.

References

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