Generative AI has emerged as a transformative technology, revolutionising industries from art and entertainment to healthcare and finance. It involves using advanced algorithms to create new content— images, music, text, or even complex data models. This article aims to provide a comprehensive overview of generative AI. We’ll explore the fundamental concepts of generative AI, how it works, and its various applications, making it accessible for beginners eager to understand this innovative field.
What is Generative AI?
Generative AI refers to a class of artificial intelligence models that can create new and original content (such as text, images, music, or even code), often in response to user prompts, and at scale, based on patterns learned from existing data.
Applications of Generative AI
The versatility of generative AI has driven its widespread adoption across various sectors, revolutionizing how tasks are performed and enhancing productivity. Key applications include:
Written content augmentation and creation: Producing a “draft” output of text in a desired style and length
Question answering and discovery: Enabling users to locate answers to input, based on data and prompt information
Summarization: Offering shortened versions of conversations, articles, emails and webpages
Classification of content for specific use cases: sorting by sentiment, topic, etc.
Software coding: Code generation, translation, explanation and verification
The impact of generative AI extends beyond these applications, promising to transform industries such as healthcare, where it can enhance patient care and ease personalized medicine; education, by providing custom-made learning experiences and resources; finance, through improved risk assessment and fraud detection; and entertainment, by revolutionizing content creation and audience engagement.
How Does RAG Work?
Retrieval-augmented generation (RAG) is a powerful approach in generative AI that combines two key processes. In the first step, data is loaded into the system, split into manageable chunks, embedded into a vector space for easy access, and stored in a database. This prepares the information for efficient retrieval. In the second step, when a user poses a query, the system retrieves relevant information from the database and uses a generative model to create coherent and contextually appropriate responses based on the retrieved data.
Ethical Considerations
As generative AI continues to evolve, important ethical considerations are raised. One of the primary concerns is the risk of spreading false information. Additionally, if the training data contains biases, the generative AI system may inadvertently produce biased outputs, perpetuating stereotypes or inequalities. Privacy is another significant concern, as generative AI often relies on large datasets, which may include sensitive personal information. To mitigate these risks, it is essential to adopt responsible AI practices such as closely monitoring outputs, providing feedback, and not inputting any personally identifiable or sensitive information.
Where to Start?
For individuals and organisations interested in exploring generative AI, start by familiarising yourself with AI concepts through online courses or workshops, as the ones listed below:
As you embark on this journey, consider the endless possibilities that generative AI presents. From enhancing creative processes to reshaping industries, generative AI has the potential to transform the way we work, create, and innovate. How do you envision its impact on your industry or daily life? Share your thoughts and join the conversation!
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by Margarida Pereira
@ Passio Consulting
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