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AI refers to the simulation of human intelligence by machines. It encompasses a range of technologies designed to perform tasks that typically require human cognition, such as learning, problem-solving, and decision-making. Example in the U.S.: AI powers virtual assistants like Siri and Alexa, enabling users to perform tasks via voice commands.
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A subset of AI, Machine Learning involves algorithms that allow systems to learn from data and improve over time without explicit programming. Example in the U.S.: ML is widely used in predictive analytics for businesses, including recommendation systems in e-commerce platforms like Amazon.
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Deep Learning, a specialized subset of ML, uses neural networks with many layers to analyze complex data patterns. It is instrumental in image and speech recognition. Example in the U.S.: Deep Learning algorithms drive facial recognition technology used by security agencies and social media platforms.
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NLP focuses on the interaction between computers and human language. It enables machines to understand, interpret, and generate human language.
https://ai-terms-glossary.com
Example in the U.S.: Chatbots and customer service tools that provide automated responses rely heavily on NLP. -
This branch of AI enables machines to interpret visual data from the world, such as images and videos. Example in the U.S.: Applications include self-driving cars that analyze road conditions and surveillance systems that detect suspicious activity.
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Modeled after the human brain, ANNs are algorithms designed to recognize patterns and solve problems in data. Example in the U.S.: ANNs power fraud detection systems used by banks and financial institutions.
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Generative AI creates new content, such as images, text, and music, based on input data. Technologies like GPT and DALL-E are examples. Example in the U.S.: Businesses use generative AI for content creation, including marketing copy and graphic design.
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Ethical AI emphasizes the development and deployment of AI systems that align with societal values and legal standards. Example in the U.S.: Ethical AI discussions are pivotal in addressing bias in hiring algorithms and privacy issues in surveillance.
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Edge AI refers to deploying AI applications on devices at the edge of networks, reducing latency and improving performance. Example in the U.S.: Smart home devices like thermostats use Edge AI to function efficiently without relying on cloud processing.