Generative AI (GenAI) refers to AI based systems specialized in creating new content, such as text, images, videos, music or complex data. Many also support classification and prediction.
They learn from vast amounts of data to produce outputs that resemble their training data. Hence;
- More creative
- Practical applications
Technologies behind GenAI
- Generative Adversarial Networks (GAN) Use two neural networks in competition to create highly realistic synthetic data.
- Diffusion models Generate content by gradually adding and then removing noise from data resulting in high quality outputs.
- Transformer models Support LLMs Utilize self attention mechanisms to generate coherent and contextually relevant text. - Are being adapted for multimodal tasks.
*Disadvantages of GenAI *
While GenAI has its unique technical foundations, these systems raise significant societal and ethical concerns.
- Misuse - Can be exploited to create deepfakes thus undermining trust in digital media
- Ease to create synthetic data amplifies risks related to privacy, security and manipulation.
- Impact on employment - May lead to workforce disruption and necessitate widespread reskilling.
- Sustainability - Training and running GenAI models involves substantial computational resources, resulting in high energy consumption and significant carbon footprint.
Most practical GenAI tools today are based on foundation models, which are then fine-tuned for specific
applications. The field is also advancing toward multimodal models capable of processing and generating
content across text, images, and audio, enabling richer, more flexible AI-based systems. Regulatory
frameworks, such as the EU AI Act [EU AI Act], are emerging to guide the responsible development and
use of these technologies.
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