Understanding Deep Learning: How Modern AI Works: The Backbone of Modern AI

By: Indu Eswar Shivani Nayuni, Department Of Computer Science & Engineering(Data Science), Student Of Computer Science & Engineering(Data Science) ,Madanapalle Institute of Technology and Science, Angallu(517325),Andhra Pradesh . indunayuni1607@gmail.com

Abstract

To be noted that deep learning became the basis for the subsequent development of the current level of AI. Thus, this article is a research article concerned with deep learning, which is a category of machine learning that employs multilayer neural networks. The final layer of learning solutions is a deep learning algorithm which is end to end learning where one does not have to manually extract the features to be learned. In the article the author dwells upon architecture of deep learning, distinctions of basic notions, methods applied in deep learning with reference to good practices and present-day implementations. This creates awareness that deep learning models like the CNNs and the RNNs are the foundation of professions like computer vision or natural language processing and speech recognition. In the same way, the article describes the disadvantages and difficulties of using deep learning: large data demands, the amount of computations wanted and issues with the textual content’s interpretive simplicity. When considering these aspects, the article shall urge clarification on how deep learning is the basis of many current AI application and the trends that exist concerning it.

Keywords subsequent development, deep learning, basic notions, computer visions, AI applications.

Introduction

Deep learning is at the pinnacle of artificial intelligence systems and marks a qualitatively new development level of artificial intelligence systems. Being an offshoot of machine learning, deep learning involves the neural network with many layers which are referred to as deep neural networks that analyses data. This is important as it has become central to realising unprecedented advances in almost all fields of AI related activities including image recognition, natural language processing, self-driving vehicles and many others. [1]

It is for this self-learning characteristic that can acquire features directly from raw data, and so, does not require feature extraction. This capability makes it possible for the modeling capability to identify good models that would predict the system with high vitality and greater accuracy of detailed patterns. The concepts like convolutional neural network (CNN) and recurrent neural network (RNN) are some of the examples of deep learning that have the potential to bring next level changes in fields like computer vision for the classification of visuals, era of natural language processing for interpretation of human language.[2]

Like every field, deep learning also encompasses challenges and problems to be addressed and, as in the given field, it has had growth in the past years. From them one could mention the need for big and various data, the need for computation, and explainability issues. To be able to apply deep learning technology, these elements have to be understood so that it is possible to understand how to apply deep learning optimally and efficiently[3].

This article gives an outline of what deep learning is, and its principles, then reviews the known effective methods and applications of this approach. In this regard, to allow the reader to localize the technology by providing him/her with a brief working understanding of the basic concepts of the contemporary technological development and the potentials of deep learning, we described the working scheme of this subdivision of the AI.

comparison of deep learning with traditional machine learning and classical AI approaches

the following table1 represents the comparison of deep learning with traditional machine learning and classical AI approaches

table 1 comparision between traditional machine learning vs deep learning vs classical AI

Aspects	Traditional machine learning	Deep Learning	Classical AI
Model Complexity	this usually comes with relatively less complex model such as decision trees, linear regression or Support Vector Machines (SVM). Employ highly parameterized	neural networks that has several layers (deep neural networks).	Commonly includes rule-based system and symbolic logic.
Feature engineering	Involves extraction of the features and choice of the features to be used by the domain specialists.	It is able to automatically predict and determine several features from raw data that eliminates the need for a feature engineering step.	Depends a great deal on the inclusion of rules, which are often designed manually and making use of logic.
Data Requirements	Suitable for use with less extensive data; the application may be slow due to data quality and/or volume.	Extremely data sensitive and entails a large amounts of training data in order to prevent model over-fitting.	Can work with a small data set and most of the time gets designed with set rules as to how it will operate.
computational Resources	Usually less complex in terms of computational requirements; can be implemented on normal computers.	Very computationally intensive; often needs GPUs or TPUs to run.	Neocognitively more efficient; primarily rules processing
Training Time	The training process takes less time because of simpler models as compared to more complex models.	Extended training time because of rigid designs and high volumes of data.	Usually there is fast exploitation of decision-making speed but has constraints of long hours of rule formulation for rule-based systems.
Interpretability	In comparison with other predictive models, for instance, with neural networks, decision tree and linear regression models are more qualitative.	In comparison with the linear models it might be highly non linear which is also its downside: they are harder to comprehend & to debug.	On their side, they are very interpretable; however it may take a lot of effort to adapt the functions of the rule–based systems if that is needed.
Applications	Appropriate where the data is of structured kind for reasons of classification, regression and clustering.	Performs the best in the tasks with the unsorted data such as image, language, and voice.	Often used when solving problems that require facts and figures and which involve sequences and series calculations.
Adaptability	It is adapted with model recompiling and functionality features change if it is used in face of new data.	Poses ability to learn and can be trained from the data and increases in intelligence with new learnt information and training period.	Robustness is relatively poor; any changes in rules and logics have to be introduced in the system manually.

Analysis

Neural nets are perspective direction of AI development based on machine learning which different by its perspectives, benefits, and challenges comparing with standard machine learning and AI paradigms. Here’s an analysis of these methodologies:Here is more detail on these methodologies and it is represented in the fig 1[3]

1. Model Complexity and Flexibility:

• Traditional Machine Learning: Such as the decision tree for instance or linear regression models or support vector machines are not as complex as the former and are based on algorithmic systems. They are suitable when dealing with numerical data and with the problems that involve relations between the variables not very complex. However, they may not be very efficient in managing large on high dimensional data and, more often, expect you to pre-process the features of the data yourself[4].
• Deep Learning: Ignores the non-linear relationship that exists in the data when the data sets are complex since it is enabled to capture patterns and relationship that are multiple in the data and this it requires multiple layers in the neural network. This makes DL models more useful in un-structured data ( e. g. , images, natural language, speech), but also more ‘black-box’ and more resource intensive from the computational perspective.
• Classical AI: Often it uses the internalized information and rules stated in the natural language, the black box, which cannot be modified without the system’s notice. At the same time, it should be mentioned that the outcomes of such systems are very easy to explain, however, such systems are not as flexible as some of the more recent development in this sphere such as, for instance, the machine learning and deep learning frameworks. Based on what was discussed they do not seem to be as flexible as the more formal rules for the kind of tasks which require compliance with logical rules.

2. Feature Engineering and Data Requirements:Feature Engineering and Data Requirement:

• Traditional Machine Learning: It involves manual selection of the features to be extracted and the input samples to be pre-screened and it is therefore very time-consuming and is dependent on the technical knowledge of one. Despite this, this approach is also confined by the quantity and quality of information that is obtainable within the ASD.
• Deep Learning: They scale the features extraction through these hierarchal layers very well that negligible amounts of manual interventions need to be done. However, it is most effective in performing this action when trained more, and on large data sets which are why it is termed data intrusions and resource hogging[5].
• Classical AI: Sometimes use rigid, that does not depend on much data, but may lack enough flexibility when handling dynamic data. Unlike the learning from data, the rule based systems utilize the know-how that is embodied in the certain language.

3. Computational Resources and Training Time:Equipment, Program, Time:

• Traditional Machine Learning: In most cases it needs a less computational effort and when applied incurs less time for learning as compared to all the other algorithms. It also makes models able to be trained on standard hardware making them feasible to use in different applicative domains[6].
• Deep Learning: This results in a huge demand for processing; this is GPUs or TPUs used the function of neural networks and quantity of data. To work with implementations of deep learning, time and money are needed for training of the models, however, high perspectives in solving of the tasks are opened[7].
• Classical AI: Another feature that does not seem otherwise to be associated with high computational demands is that, in general, the systems do not take data and learn from it but are rule based systems. Training is normally a small component of T while it may take a lot of time to stipulate the rule set.

4. Interpretability and Adaptability:

• Traditional Machine Learning: Some of the most common models, like the decision tree or the linear regression, are easy to explain, so the users can understand the results and, therefore, trust them. Modeling plasticity is done through model re-training and feature tweaking:
• Deep Learning: Models can also be termed as “black boxes” as a result of their intricate nature or design and therefore understanding and even diagnosing the problem can be very difficult. But the deep learning systems can learn with more data and can even refine themselves while so learning automatically from patterns[8].
• Classical AI: Which is highly interpretable compared to, for instance, deep learning since it is a rule-based method. Flexibility is low and permission is required to edit rule and logic to cater for new circumstances or new data.

5. Applications and Use Cases:

• Traditional Machine Learning: Appropriate where the relationships are less complex and especially for structured data for tasks including classification, regression as well as clustering. Due to the described features it is actively applied in the field of finance, healthcare and in the field of marketing[9].
• Deep Learning: It is well optimized for the tasks connected with unstructured data as images, voice recognition, natural language processing, or other AI-based systems. They have led to evolution of areas such as computer vision and the voice assistant business.
• Classical AI: Es suitable for the jobs that do need lot of logical reasoning and representation such as expert systems and decision- support systems. It is applied in fields such as the prostate diagnostic system and expert systems.

Conclusion

Deep learning has become one of the most common techniques in artificial intelligence due to its ability to process vast amount of unstructured data and making progress in branches such as computer vision or natural language processing. The probability that the model can address very diverse kinds of patterns in the context of large datasets provides unparalleled options still, it has problems, including high computational demand and issues associated with the model’s explainability. However, it is also important to notice that traditional machine learning and methods of artificial intelligence referred to as classical, are used nowadays because of its efficiency, interpretability and applicability to structured data. Integrating such approaches and megainformatics and dealing with the issues connected to the deep learning is going to be crucial for driving the full worth of the concept and the further development of the AI.

Reference

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Cite As

Nayuni I.E.S (2024) Understanding Deep Learning: How Modern AI Works: The Backbone of Modern AI, Insights2Techinfo, pp.1

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