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Machine Learning and Deep Learning Revolutionize Artificial Intelligence
2021
The concept of Artificial Intelligence (AI) is present in many aspects of our professional life. From automatic translation to predictive maintenance, to infused intelligence in the applications you use every day, it's not about the future, but about the company present. The fields of application and potential uses of Artificial Intelligence are more and more diverse: understanding of natural language, visual recognition, robotics, autonomous system, Machine Learning. In this manuscript, we will show what is the Machine Learning concept and the Deep Learning as well as their position in artificial intelligence, their strengths and their flaws. We will present some algorithms that the Learning machine uses. We will also d iscuss the statistics used in these algorithms to adapt the links between the Artificial Neural Networks to strengthen or destroy the links in order to have a good approximation of the input data.
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. In this paper, main topics about deep learning have been covered. The relationship between artificial intelligence, machine learning and deep learning has been mentioned briefly. Detailed information about deep learning has been given, ie. History and future of deep learning. Artificial neural networks has been reviewed. The importance of GPU and deep learning in big data have been shown deeply. Using areas of deep learning have been explained. Benefits and weaknesses of deep learning have been covered. The informations about deep learning algorithms, libraries and tools have been given.
APSIPA Transactions on Signal and Information Processing
In this paper we look at recent advances in artificial intelligence. Decades in the making, a confluence of several factors in the past few years has culminated in a string of breakthroughs in many longstanding research challenges. A number of problems that were considered too challenging just a few years ago can now be solved convincingly by deep neural networks. Although deep learning appears to be reducing the algorithmic problem solving to a matter of data collection and labeling, we believe that many insights learned from ‘pre-Deep Learning’ works still apply and will be more valuable than ever in guiding the design of novel neural network architectures.
Therapeutic Advances in Urology, 2021
Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.
The upsurge of deep learning for computer vision applications
International Journal of Electrical and Computer Engineering (IJECE), 2020
Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos & images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is...
Springer, 2022
Artificial Intelligence means the ability of computers to perform tasks that normally require human intelligence, like speech recognition, visual perception, finding the anomalies in a radiology image, and giving a correct diagnosis. Artificial Intelligence (AI) especially deep learning has demonstrated remarkable progress in image recognition. Many articles have been published on this topic, however, most of them are full of computer science jargon and mathematical formulas. In this review, we present from the basics to the latest updates, in a language that is easily understandable by a radiologist or anyone without a maths background.
Deep Learning: A Vision for Computer
International Journal for Research in Applied Science and Engineering Technology -IJRASET, 2020
Artificial intelligence (AI) is countered to be one of the most trusted techniques to cope with variety of issues. Researchers are delving deeper by using the techniques of AI, such as Machine learning (ML) and deep learning (DL). ML has attained a high attraction in the industry and it is utilized by many applications. Due to drastic increase in data, these techniques are becoming popular amongst the researchers. Long with this, deep learning is the branch of ML which outperformed the conventional techniques of machine learning. This paper presents the brief account on ML and DL. It reviewed how machine and deep learning are utilized and perform different operations. Along with this, a literature survey is presented on the basis of three different domains: Security, Health Management and Big data. This paper gives an overview on Machine learning and deep learning along with the work proposed in this domain.
RG, 2023
The rapid evolution of Artificial Intelligence (AI) has ushered in a new era of technological innovation, with profound implications across various domains. This research paper delves into the realm of AI algorithms, deep learning, and neural networks, dissecting their advancements and multifaceted applications. Beginning with a historical overview, we navigate through the landscape of AI algorithms, tracing their development and pivotal milestones. A comprehensive exploration of deep learning unfolds, shedding light on the intricate architecture of deep neural networks and the underlying mechanisms of backpropagation and gradient descent. Neural networks, inspired by biological systems, are unveiled in their diverse forms, encompassing feedforward, recurrent, and convolutional paradigms. The paper then embarks on an in-depth analysis of recent advancements, showcasing the transformative potential of AI. Reinforcement learning emerges as a powerful paradigm, exemplified by algorithms and real-world applications. Generative Adversarial Networks (GANs), an ingenious innovation, demonstrate their prowess in various creative applications. Attention mechanisms, a recent breakthrough, enhance the performance of neural networks across tasks demanding contextual comprehension. The applications of AI algorithms and neural networks span across industries, from revolutionizing medical diagnostics and enabling precise treatments to reshaping financial landscapes through algorithmic trading. Natural Language Processing (NLP) is explored as a bridge in communication, while autonomous vehicles and robotics exemplify the fusion of AI with mobility. Even the creative domains of art and music are not immune to AI's touch, as demonstrated by AI-generated masterpieces. However, this journey is not devoid of challenges. Ethical considerations loom large as AI penetrates every facet of human existence. Privacy concerns and the interpretability of AI decisions demand careful attention. As we conclude, we reflect on the past advancements, anticipate future breakthroughs, and emphasize the importance of responsible innovation in harnessing the boundless potential of AI algorithms, deep learning, and neural networks.
Modern Machine and Deep Learning Systems as a way to achieve Man-Computer Symbiosis
Man-Computer Symbiosis (MCS) was originally envisioned by the famous computer pioneer J.C.R. Licklider in 1960, as a logical evolution of the then inchoate relationship between computer and humans. In his paper, Licklider provided a set of criteria by which to judge if a Man-Computer System is a symbiotic one, and also provided some predictions about such systems in the near and far future. Since then, innovations in computer networks and the invention of Internet were major developments towards that end. However, with most systems based on conventional logical algorithms, many aspects of Licklider’s MCS remained unfulfilled. This paper explores the extent to which modern machine learning systems in general, and deep learning ones in particular best exemplify MCS systems, and why they are the prime contenders to achieve a true Man Computer Symbiosis as described by Licklider in his original paper in the future. The case for deep learning is built by illustrating each point of the original criteria as well as the criteria laid by subsequent research into MCS systems, with specific examples and applications provided to strengthen the arguments. The efficacy of deep neural networks in achieving Artificial General Intelligence, which would be the perfect version of an MCS system is also explored.