Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

The Amount of AI development nowadays is astounding. Deep learning, big data, and data science are currently in vogue like never before. Nowadays, businesses like Google, Facebook, IBM, and Amazon use AI to develop incredible technolog y 6 Privacy and surveillance, bias or discrimination, and the potential philosophical problem of using human judgment are among the legal and ethical problems . Artificial intelligence has a bright future and will be highly intelligent. 7 Artificial intelligence is in its early stages but is already widespread in applications that artificial intelligence has brought about in society. As a result of its use, worries about more recent digital technologies becoming a new source of inaccuracy and data breaches have increased. 8 6 James V. Stone, “Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning”, 2019 7 Nick Bostrom, “Super intelligence: Paths, Dangers, Strategies”, 2014 8 Kevin Warwick, “Artificial Intelligence: The Basics”, 2011 such as policing, stock trading, social networking, and medical diagnosis. The impact of AI is unevenly distributed across economic activity and social and political life. The full effects of AI are difficult to predict, and both optimistic and pessimistic predictions tend to contain exaggerations. Roy Amara's insight suggests that people overestimate the immediate effects and underestimate the long-term effects of new technologies like AI. However, it is widely acknowledged that AI will have significant effects on society. The approach we take to AI will shape our future, as stated by the European Commission. The mastery of AI is seen to global dominance, as stated by the president of Russia. AI is described as "a combination of technologies that enable computers to see, learn, reason, and aid in decision-making to solve issues in ways that are comparable to what people do," in a book published by Microsoft in 2018. (Yet AI differs from human reasoning in important ways.) Artificial intelligence is defined as "systems that demonstrate intelligent behavior by assessing their surroundings and executing actions with some degree of autonomy to achieve certain goals," according to the European Commission's Commu- nication on AI. AI can resolve issues people cannot independently, particularly issues requiring enormous volumes of data and several possible solutions. AI could adjust for bias and human error. AI is not magic, though. Every AI algorithm incorporates human judgments and trade-offs. Algorithms do not have any value. AI may add new kinds of bias or errors or duplicate human error or bias. b) Interconnected Disciplines within the Broader Field of AI 1. Machine Learnin g 9 A data analysis technique called machine learning automates the creation of analytical models. It is a subfield of artificial intelligence founded on the notion that machines can learn from data, spot patterns, and make judgments with little assistance from humans. 2. Evolution of Machine Learning Modern machine learning is distinguished from its predecessors by advancements in computing technologies. The concept of computers learning from data without explicit programming led to the emergence of artificial intelligence research. The iterative nature of machine learning, where models adjust and learn from previous calculations, is crucial for trustworthy and reproducible outcomes. While machine learning techniques have existed for some time, recent breakthroughs in processing vast amounts of data have propelled its progress. Well-known applications include self-driving cars, exemplifying the core of computer learning, and online recommendations from platforms like Netflix and Amazon, which utilize machine learning to understand user preferences. Machine learning also plays a role in analyzing customer feedback on platforms like Twitter and involves a combination of language rule creation and machine learning. Additionally, fraud investigation stands out as a significant real-world application of machine learning. 3. Machine Learning and Artificial Intelligenc e 10 Machine learning is a particular branch of artificial intelligence that teaches a machine how to learn, whereas artificial intelligence is the general science that aims to emulate human abilities. : The increasing popularity of data mining, Bayesian analysis, and machine learning is driven by factors such as expanding data quantities, diverse data types, more powerful and affordable computation, and reasonably priced data storage. These factors enable the creation of models that can analyze complex data, provide faster and accurate answers, and uncover lucrative opportunities or potential threats. Machine learning focuses on developing techniques that use data to improve performance on specific tasks, often incorporating elements of artificial intelligence. Machine learning algorithms generate predictions or decisions without explicit programming, making it useful in various fields such as medicine, email filtering, speech recognition, and computer vision. While closely related to computational statistics, not all machine learning is 9 Giuseppe Bonaccorso, “Machine Learning Algorithms”, 2nd Edition, Packt, 2018 10 Parag Mahajan, “Artificial Intelligence in Healthcare: AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone”, 2019 © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 12 ( ) Year 2023 D Journey of Artificial Intelligence Frontier: A Comprehensive Overview