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The very first humanoid robot called WABOT-1 was developed by a team of people at Waseda University in Japan in 1973. The evolution of robots was going through a new transformation now-a-robot was made to look and function almost like a human being. WABOT-1 consisted of a head, torso, arms, and legs, and could walk, move its arms, and pick up objects with its hands. While the other robotic creations of the time were sort of elementary in comparison, WABOT-1 could almost perform like a human: Motor abilities helped it navigate through obstacles undetected; it also listened and responded to simple verbal commands, making it one of a few that has, in a certain sense, interacted with humans. Developed in the infant days of robotics and controlled by an arsenal of motors combined with primitive seeds of artificial intelligence, WABOT-1, too, could operate rudimentarily on an independent level. If seen in contrast to the present-day robots, WABOT-1 appears primitive, except that it was anoth...

Plagiarism detection has been otherwise seen in a different light with the advent of AI in the reckoning. Now, the tools of AI which nowadays analyze text with advanced technologies like natural language processing and machine learning used to compare it to voluminous databases can be much more precise in detection.  This implies that much more subtle infringements could be detected than in direct copies. rater, rewriting is just a bit out of the reach of the classic style of plagiarism scanners. However, AI models can reach down to root words. It is not only about phrasing on the surface but rather about downloading deep into the true meanings of the words. As such, AI will get to understand and identify the same concepts, ideas, and meanings should the text undergo some slight alteration. Owing to this fact, such technology is well suited to scenarios in which an author has rewritten without acknowledging the sources.  The stylometric analysis is another vital part of an AI ...

Generative AI is artificial intelligence that creates new original content—text, images, music, as well as videos. Rapid progress has been, over recent years, opened new possibilities. GPT (Generative Pre-trained Transformers), applied to text, and DALL·E, used for images, are but two models that have generated much more than just buzz in the Generative AI arenas. In addition to these firms, there are dozens more that continue to innovate and expand on these concepts in many other applications around the world. Some great topics about generative AI are described below. Natural Language Generation (NLG): One of the most significant improvements in GenAI is in Natural Language Generation (NLG-derived models), which generate text that closely mimics human language. Applications include: automated content generation, customer support chatbots, machine translations through summarization of big data sets to composing news articles. Image and Art Generation: Using textual descriptions or prop...

NLP is a subset of AI, and primarily focuses on interaction between machines and human languages. It spans over making machines understand, interpret, and generate human languages towards propelling effective human communication along its path-an improvised method of interaction between man and machines. NLP is the most pivotal technology in research and application fields for chatbots, voice assistants, translation, sentiment analysis, and recommendation systems. NLP also faces dire challenges as human language is much more complex than structured data. Language is vague, context-dependent, and dramatically varies across different regions and cultures. Words can have several meanings, and even the structure of a sentence can highly change the meaning of a message. For example, the "bank of the river" and "bank of a financial institution" seem the same but certainly have different meanings. A few principles such as tokenization, part-of-speech tagging, named entitie...

Healthcare, security, automotive, and entertainment industries are some examples of the industry that has seen a revolution due to the image recognition system application based on artificial intelligence and machine learning. Here, the computer can read and interpret image data by recognizing and classifying objects in an image database-a development in this domain. The structure and functioning of an image recognition device, through deep learning and thus convolutional neural networks (CNNs), has grown up over time. CNNs were developed to imitate the human visual brain: the mechanism for processing imaging data by many layers of neurons. Each of the layers of neurons is specializing on searching different parts of the edge, a texture, or forms within the image. Then the data progresses through a number of layers, so their successively higher groups begin to identify the more complex patterns, enabling the network to understand the meaning of the image ultimately and even detect the ...