Network based systems

Scope of AI, Deep Learning, and Neural Network, Market 2021

Dublin, September 29, 2021 (GLOBE NEWSWIRE) – The “Next wave of deep learning models and applications (RNN, CNN and GaN)” report was added to offer.

As digitization advances across all industries, AI is increasingly adopted as more and more business processes are automated.

With this, the expectations of AI in terms of applications that can be realized using AI also expand, and thus a more complex set of neural networks have been introduced that should take advantage of the advancements of computing power to enable the next generation of applications where AI will have higher decision-making power and greater autonomy over decision-making.

An impressive collaboration between universities and industry has accelerated the commercialization of new research projects around AI and machine learning.

Companies such as Google and Nvidia have also taken the lead in applied research around AI, which has resulted in the development of algorithms that now form the basis of self-driving cars, simulation software and other smart apps.

In short, this research study highlights the following points:

  • Scope of AI, Deep Learning and Neural Network
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Generative Contradictory Networks (GANs)

Main topics covered:

1.0 Strategic imperatives
1.1 Why is it more and more difficult to develop? The strategic imperative: the factors of pressure on growth
1.2 The strategic imperative
1.3 The impact of the 3 main strategic imperatives on the artificial intelligence industry
1.4 About the Growth Pipeline Engine
1.5 Growth Opportunities Fuel the Growth Pipeline Engine

2.0 Scope and Methodology of the Research

3.0 Introduction-AI and Neural Networks
3.1 AI systems have evolved to meet the expectations of modern applications, which demand higher levels of autonomy
3.2 Neural networks have found applications in all sectors and have benefited from the ubiquity of high performance computing
3.3 While supervised learning supports most major business AI applications, other frameworks show promising potential
3.4 Deep learning supported by neural networks enabled complex and layered decision making
3.5 Neural networks use a complex step-by-step decision-making process that emulates human decision-making

4.0 Convolutional Neural Networks
4.1 CNNs rely on a series of convolution and grouping layers to process images
4.2 CNNs excel at simplifying the characteristics of complex input data for faster processing
4.3 CNNs are central to computer vision in several commercial applications
4.4 CNNs have been used to spot microscopic flaws and anomalies in images, speeding up flaw detection processes

5.0 Recurrent neural networks
5.1 RNNs are suitable for applications requiring sequential data processing
5.2 RNNs have internal memory which allows them to process entries in the context of previous entries
5.3 Voice assistants such as Google, Siri and Alexa depend on RNNs for speech and context analysis
5.4 While current RNN applications are addressed to voice and speech, new applications in image analysis and robotics are emerging

6.0 Generative adversarial networks
6.1 GANs use neural networks in a zero-sum game to derive a realistic replica of the input data
6.2 GANs are an enhancement of the UL approach that automates the continuous learning process
6.3 GANs are ideally suited for applications where the creative decision-making process needs to be automated
6.4 CNNs used as discriminators and generators enable a range of applications in healthcare and entertainment

7.0 Businesses in action
7.1 Google
7.2 Nvidia
7.3 Adobe
7.4 Microsoft
7.5 IBM

8.0 Growth opportunities
8.1 Growth opportunity 1: Data monetization and data brokering for traditionally conservative industries
8.2 Growth Opportunity 2: Testbeds and Simulated Environments for AI Executives
8.3 Growth Opportunity 3: Out-of-the-box Neural Network Integrations with Commercial Applications

9.0 Key contacts

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