The recent developments in soft computing cannot be complete without noting
the contributions of artificial neural machine learning systems that draw
inspiration from real cortical tissue or processes that occur in human brain.
The universal approximability of such neural systems has led to its wide spread
use, and novel developments in this evolving technology has shown that there is
a bright future for such Artificial Intelligent (AI) techniques in the soft
computing field. Indeed, the proliferation of large and very deep networks of
artificial neural systems and the corresponding enhancement and development of
neural machine learning algorithms have contributed immensely to the
development of the modern field of Deep Learning as may be found in the well
documented research works of Lecun, Bengio and Hinton. However, the key
requirements of end user affordability in addition to reduced complexity and
reduced data learning size requirement means there still remains a need for the
synthesis of more cost-efficient and less data-hungry artificial neural
systems. In this report, we present an overview of a new competing bio-inspired
continual learning neural tool Neuronal Auditory Machine Intelligence
(Neuro-AMI) as a predictor detailing its functional and structural details,
important aspects on right applicability, some recent application use cases and
future research directions for current and prospective machine learning experts
and data scientists.


In recent years, the field of soft computing has made significant advancements, and these advancements are incomplete without acknowledging the contributions of artificial neural machine learning systems. These systems draw inspiration from the functioning of real cortical tissue in the human brain and have gained widespread use due to their universal approximability. The future of artificial intelligent techniques in the realm of soft computing looks promising, thanks to novel developments in this field.

The Rise of Deep Learning

One of the driving forces behind the progress in soft computing is the emergence of deep learning. The proliferation of large and deep neural networks, coupled with the advancement and refinement of neural machine learning algorithms, has revolutionized this domain. Renowned researchers such as Lecun, Bengio, and Hinton have made significant contributions to deep learning, which has paved the way for groundbreaking applications and research possibilities.

Challenges in Artificial Neural Systems

While artificial neural systems have shown tremendous potential, there are still challenges that need to be addressed. Two key requirements in this regard are affordability for end users and reduced complexity. Additionally, the data learning size required for artificial neural systems needs to be reduced. Meeting these requirements would enable wider adoption and utilization of these systems.

The Neuro-AMI Approach

This report presents an overview of a new bio-inspired continual learning neural tool called Neuronal Auditory Machine Intelligence (Neuro-AMI). This tool holds promise as a predictor and offers functional and structural details that set it apart from existing options. Understanding the right applicability of Neuro-AMI is crucial to harness its full potential.

Recent Application Use Cases

Neuro-AMI has been applied successfully in various domains, showcasing its wide range of applications. Some recent use cases include financial market prediction, medical diagnosis, speech recognition, and natural language processing. These examples highlight the versatility and potential impact of Neuro-AMI in solving complex real-world problems.

Future Research Directions

For machine learning experts and data scientists, Neuro-AMI offers exciting prospects and avenues for further research. Future research directions in this field should focus on optimizing the cost-efficiency of artificial neural systems and reducing their data dependency. By continuously advancing and refining these systems, we can unlock new possibilities and further enhance their effectiveness.


The integration of artificial neural systems into soft computing has paved the way for groundbreaking advancements. Deep learning, along with the contributions of notable researchers, has expanded the horizons of this field. With the introduction of Neuro-AMI, a bio-inspired continual learning neural tool, we have taken another step towards more cost-efficient and data-hungry artificial neural systems. By combining expertise from multiple disciplines, we can continue to push the boundaries of soft computing and unlock new frontiers of AI applications.

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