Discover the current capabilities of artificial intelligence (AI) in different industries and domains. AI has revolutionized deep learning, automation, and cognitive computing, bringing advancements in computer vision and fraud detection.
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Current capabilities of artificial intelligence In the present time, AI has been able to make it easy for the different machines to learn about the technologies in a proper manner. There are different examples of AI which are like the computers and are used for the self-driving of the cars, with the reliability of handling deep learning with the NLP. AI has been able to capitalize the advancements with deep learning in solutions for SAS (Hoeckelmann et al., 2015). AI has been able to work on the automation process with repeated learning and a discovery of the data. It is hardware driven with the robotic automation rather than the automation of the manual tasks. Hence, AI tends to add intelligence for the products where it is not sold as an individual application; rather, it works on the products that are already used. AI has been able to adapt the current progressive learning. It handles the structure and the data regularities where the algorithm can acquire the skill properly (Johnson, Hofmann, Hutton & Bignell, 2016). The model can adapt to the different standards where the adjustments need to be made through training and adding the data. AI has been able to analyze the deep data through the use of neural networks which have different hidden layers. Apart from this; it has been able to change the incredible computer power and big data (Kokina & Davenport, 2017). There are deep learning models mainly to handle the accuracy through the use of the neural networks. Some of the examples are Alexa, which is completely based on algorithm of the deep learning procedure. AI has been able to hover on a larger market where the algorithms are based on self- learning. The data has been set with intellectual property, and the role of the data has been to create a competitive advantage (Russell, Dewey & Tegmark, 2015). As per the analysis, the AI has been able to work on the different relationship and the patterns which bring the analytics to the industries and domains. The improved performance of the existing technologies like the computer vision and the time series analysis helps in handling the break down of the economic barriers, which include the language and planning of the translation barriers. AI has been working on the system that detects the fraud easily. It has the self-learning system where the technology has been able to probe the complex data for learning the different specific tasks that are common for a human being. The cognitive computing through AI and then working on the simulation process helps in achieving a better possibility to interpret different images. It includes the computer vision which is depending on the recognition of the patterns and then handling the
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deep learning standards that is for recognizing on how the machines can process and then analyze the different images. References Hoeckelmann, M., Rudas, I. J., Fiorini, P., Kirchner, F., & Haidegger, T. (2015). Current capabilities and development potential in surgical robotics.International Journal of Advanced Robotic Systems,12(5), 61. Johnson, M., Hofmann, K., Hutton, T., & Bignell, D. (2016, July). The Malmo Platform for Artificial Intelligence Experimentation. InIJCAI(pp. 4246-4247). Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing.Journal of Emerging Technologies in Accounting,14(1), 115-122. Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence.Ai Magazine,36(4), 105-114.