Specific topics
Generative AI (GenAI)
Generative AI offers powerful capabilities that can significantly enhance marketing communications, including content creation, personalization, and efficiency. However, it is important to be aware of its limitations, such as potential biases, lack of deep understanding, and legal risks including inadvertent negative impacts on or transfer of Sonova IP and third party IP infringement.
Balancing the use of AI with human oversight and ethical considerations is crucial to maximizing its benefits while minimizing potential downsides.
Compliance
Adherence to these standards is essential to maintain the integrity and reputation of the Phonak brand. Non-compliance may result in redesign. This directive serves as a comprehensive guide for all teams involved in the design and production of Phonak product packaging.
Definition of GenAI
Generative artificial intelligence (AI) is a type of AI that creates new content from existing data. In essence, generative AI is about using technology to create new and original content, expanding our creative and productive capabilities:
What it does
Based on what it has learned from existing examples, generative AI can create images, text, music and videos.
How it works
It uses advanced algorithms and neural networks to understand patterns in data and then generate something new and unique.
When content is generated by Sonova employees with an AI tool, you must comply with Sonova employees’ duties that you will find here: https://one.sonova.com/sites/genai-site/news/196922/ai-chatbots
Limitations of GenAI
Generative AI offers exciting capabilities when it comes to content creation. However, because generative AI rely on the data they were trained on,
many limitations are encompassed.
Limit of originality, creativity and human touch
Limited creativity: AI can mimic creative styles and generate variations, but it struggles with true innovation or the creation of genuinely original content.
Limited emotional intelligence: AI lacks true emotional intelligence, making it difficult for AI-generated content to resonate on a deep emotional level with audiences. It can mimic emotional language but does not genuinely understand or feel emotions.
Lack of true understanding
Difficulty in abstract concepts: AI struggles with abstract thinking, complex reasoning, or the generation of content that requires deep conceptual understanding.
Context misinterpretation: AI lacks deep understanding and common sense. It can misinterpret nuances, especially in complex or sensitive topics.
Surface-Level comprehension: AI operates based on patterns in data, which means it can generate content that sounds correct but lacks the depth or insight that a human expert would provide.
Ethical and bias issues
Infringement: AI-generated content may unintentionally replicate copyrighted or trademarked material, creating legal risks if used without proper rights.
Bias in AI models: AI systems can inherit biases present in the data they were trained on. This is particularly problematic in areas like advertising and direct customer communications.
Ethical concerns: The use of AI in generating content can raise ethical questions, such as the potential for spreading misinformation.
While AI can produce high-quality content, all these limitations can lead to
- content in violation of third-party rights
- repetitive content
- inconsistency in quality of output
- overly generic or not specialized enough content
- content that is contextually incorrect or nonsensical
- not up-to-date or outdated content
- inaccurate, irrelevant, or inappropriate content
- content that might be biased or discriminatory