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Artificial Intelligence for Precision Medicine
Published in Kamal Kumar Sharma, Akhil Gupta, Bandana Sharma, Suman Lata Tripathi, Intelligent Communication and Automation Systems, 2021
Alan Turing defined AI concepts while John McCarthy first used the ‘artificial intelligence’ terminology in 1956 at a Dartmouth conference. In AI, machines could execute jobs that require human intelligence, for example, learning, reasoning and diagnosing diseases. In 1959, while working at IBM, Arthur Samuel first introduced the ‘machine learning’ (ML) terminology. ML and AI are often used alternatively; however, ML is a means of fulfilling AI. With conventional programming, humans compose a particular algorithm to allow machines to carry out a certain job. However, for complicated jobs, such as image recognition, particular transcribed rules might be neither pragmatic nor inclusive enough. ML enables computers to learn such rules instantly with no obvious programming, which renders ML the desirable framework for AI applications [9].
The Turing Test
Published in Nicolas Sabouret, Lizete De Assis, Understanding Artificial Intelligence, 2020
In the Turing test, you have a human in a room and a computer with an artificial intelligence program in the other. You can communicate using a keyboard (to type your messages) and a screen (to read their responses). Thus, you have a keyboard and a screen to speak with the human, and another keyboard and screen to speak with the program, but you do not know which one is connected to the AI program imitating a human and which one is connected to the real human typing the responses. To complicate things a little more, all the responses are given within the same time interval. The speed of the answers isn’t taken into consideration: you can only consider the content of the answers to distinguish between the human and the AI.
Artificial Intelligence
Published in Ravi Das, Practical AI for Cybersecurity, 2021
To start off with, probably the first well-known figure in the field of Artificial Intelligence is that of Alan Turing. He was a deemed to be a pioneer in the field of computer science, and in fact, is very often referred to as the “Father of Artificial Intelligence.” Way back in 1936, he wrote a major scientific paper entitled “On Computable Numbers.” In this famous piece of work, he actually lays down the concepts for what a computer is and what its primary purposes are to be. It is important to keep in mind that computers hardly existed during this time frame, and in fact the first “breed” of computers would not come out until much later in the next decade.
Impact of AI and COVID-19 on manufacturing systems: An Asia Pacific Perspective on the two Competing exigencies
Published in Production & Manufacturing Research, 2023
Malini Mittal Bishnoi, Swamynathan Ramakrishnan, Swathi Suraj, Ashish Dwivedi
The threat of computers taking over human jobs has dated to the very beginning of the 1950s when the Turing machine was brought to the public eye and subsequently when the term ‘Artificial Intelligence’ was first coined. The question that arises therefore is, ‘What defines human intelligence? Robert J. Sternberg defines Human intelligence as ‘the potential to learn from experience, adjust to new situations, comprehend and handle abstract concepts, and employ knowledge to manipulate one’s environment’ (Sternberg, 2020). During the COVID-19 pandemic, the momentum of development and demand for AI tools has undeniably increased with more companies opting for AI solutions that can withstand a future pandemic (Hippold, 2020). As unemployment rates increase with the global economy taking a plunge, previous fears of AI taking over the role of human beings in businesses have increased multifold. The unprecedented COVID-19 pandemic has disrupted the balance between job redundancy and generation within AI.
Exploring the usability of the text-based CAPTCHA on tablet computers
Published in Connection Science, 2019
CAPTCHA stands for “Completely Automated Public Turing test to tell Computers and Humans Apart”. It has some similarities and dissimilarities with the Turing test. Similarly to the Turing test, the CAPTCHA asks the complex task in the domain of the artificial intelligence to be solved. But, unlike the Turing test, the evaluator of the answers is the machine (Naor, 1996). Basically, it can differentiate if the answered questions are solved by the humans, i.e. computer users or computer robot programs usually called bots. In this way, CAPTCHA incorporates many elements of Artificial Intelligence (AI). Hence, any program that passes the tests generated by a CAPTCHA can be used to solve a hard unsolved AI problem (Von Ahn, Blum, & Langford, 2004). However, the primary goal is that a CAPTCHA task should be easily solved by computer users and almost impossible to be solved by bots (Von Ahn et al., 2004).
Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art
Published in Advanced Robotics, 2019
In 1950, Alan Turing introduced the concept of Artificial Intelligence (AI) after the first computers were developed [37,38]. He proposed the Turing Test, which attempted to distinguish machine from people through several questions and answers. If the computer passed the test, it was considered as ‘intelligence’ like human. Based on the Turing Test, some researchers studied on AI, then AI was established as an academic discipline in 1956 [39]. AI was defined as intelligent agent to perceive variable environment and decide what to do with learning and problem solving. However, even though many researchers tried to apply AI to different research areas over 60 years, it was not remarkably developed until 21st century, because there were some challenges to perceive variable environment as a limitation of computational capability. In 2000s, AI has been noticeably developed with the popularization of the Internet, sensors, big data, interconnection and fusion of data and knowledge and so on [40]. Nowadays, AI has been studied in many research areas, such as natural language processing, knowledge representation, automated reasoning, and machine learning [41,42]. Among them, machine learning is one of key research areas for AI. Many researchers have been trying to recognize patterns or make predictions from data using machine learning without programming the computer. In particular, machine learning is being studied in unmanned aerial vehicles (UAVs) for autonomous flight or intelligent behavior. Machine learning enables UAVs to solve various challenges such as intelligent control strategy and object recognition.