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The policy serves as a framework to promote ethical AI practices, protect data, ensure transparency, and mi1gate potential risks associated with both non-generative and generative AI within the en1re group. As AI continues to evolve, the policy provides fundamental principles and considerations that will guide our approach to AI usage, ensuring alignment with our company values and objectives. It is designed to foster innovation while priori1zing the well-being of our clients, employees, and stakeholders. 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These systems are more versatile and can produce original output based on patterns learned during training. Generative AI often leverages deep learning and neural networks to create new content. Examples of industry-related Generative AI: Text Generation: Large language models like ChatGPT are examples of generative AI that produce coherent and contextually relevant text based on prompts or inputs. Art Generation: Users can create images, drawings, or other forms of art using generative AI algorithms trained on vast datasets of artistic styles. Music Composition: Generative AI models can compose original music in various genres by learning from existing compositions. Speech and Video Synthesis: AI-powered video synthesis can create videos, simulate human voices, or alter existing footage. 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Unlike generative AI, these systems cannot create new content or generate novel information beyond their training data. Instead, they rely on predefined algorithms and patterns to execute tasks and provide solutions. Examples of industry-related Non-generative AI: Image Recognition: Non-generative AI models can be trained to recognize objects, patterns, or faces in images, making them valuable tools across various applications. Recommendation Systems: E-commerce platforms often use non-generative AI to suggest products or content based on user behavior, purchase history, or preferences. Speech Recognition: Virtual assistants and voice-controlled devices utilize non-generative AI to convert spoken language into actionable commands. Generative AI: Explanation and Examples Generative AI, also referred to as creative AI, involves models capable of generating new content or data that was not present in the original training set. These systems are more versatile and can produce original output based on patterns learned during training. Generative AI often leverages deep learning and neural networks to create new content. Examples of industry-related Generative AI: Text Generation: Large language models like ChatGPT are examples of generative AI that produce coherent and contextually relevant text based on prompts or inputs. Art Generation: Users can create images, drawings, or other forms of art using generative AI algorithms trained on vast datasets of artistic styles. Music Composition: Generative AI models can compose original music in various genres by learning from existing compositions. Speech and Video Synthesis: AI-powered video synthesis can create videos, simulate human voices, or alter existing footage. Distinctions between Non-generative and Generative AI Understanding the distinctions between non-generative and generative AI is crucial because it defines their capabilities and potential use cases across industries. As we integrate AI technologies into our company’s operations, these definitions will guide implementation and usage strategies. It is important to note that while these definitions provide valuable insight, our AI implementation approach will be further refined by considering additional factors such as compliance, data privacy, security, industry best practices, business objectives, and risk 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AI: Explanation and Examples\nNon-generative AI, also known as traditional or narrow AI, refers to artificial intelligence systems designed to perform specific, predefined tasks within a limited scope. Unlike generative AI, these systems cannot create new content or generate novel information beyond their training data. Instead, they rely on predefined algorithms and patterns to execute tasks and provide solutions.\n\nExamples of industry-related Non-generative AI:\nImage Recognition: Non-generative AI models can be trained to recognize objects, patterns, or faces in images, making them valuable tools across various applications.\nRecommendation Systems: E-commerce platforms often use non-generative AI to suggest products or content based on user behavior, purchase history, or preferences.\nSpeech Recognition: Virtual assistants and voice-controlled devices utilize non-generative AI to convert spoken language into actionable commands.\n\nGenerative AI: Explanation and Examples\nGenerative AI, also referred to as creative AI, involves models capable of generating new content or data that was not present in the original training set. These systems are more versatile and can produce original output based on patterns learned during training. Generative AI often leverages deep learning and neural networks to create new content.\n\nExamples of industry-related Generative AI:\nText Generation: Large language models like ChatGPT are examples of generative AI that produce coherent and contextually relevant text based on prompts or inputs.\nArt Generation: Users can create images, drawings, or other forms of art using generative AI algorithms trained on vast datasets of artistic styles.\nMusic Composition: Generative AI models can compose original music in various genres by learning from existing compositions.\nSpeech and Video Synthesis: AI-powered video synthesis can create videos, simulate human voices, or alter existing footage.\n\nDistinctions between Non-generative and Generative AI\nUnderstanding the distinctions between non-generative and generative AI is crucial because it defines their capabilities and potential use cases across industries. 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