What is generative AI?

Generative AI (Gen AI) is a type of artificial intelligence that uses deep learning models to create new content—such as text, images, and videos—based on patterns in the data it was trained on. It can also adapt its knowledge to solve new problems.

How does GenAI work?

Like all artificial intelligence, generative AI works by using machine learning models—very large models that are pre-trained on vast amounts of data.

Foundation Models (FMs):

• Trained on broad, unlabeled data.
• Learn patterns to predict the next item in a sequence.
• Used for tasks like enhancing images or generating text.

Large Language Models (LLMs):

• A type of FM specialised in language tasks.
• Examples include GPT models, which generate, summarise, and classify text.
• Learn from massive datasets and apply knowledge across different contexts.

How to Evaluate Generative AI Models?

A successful generative AI model must meet three key criteria:

• Quality – Outputs should be high-quality and natural, especially for user-facing applications like speech or image generation.
• Diversity – The model should capture a wide range of patterns and avoid bias while maintaining quality.
• Speed – Fast generation is essential for interactive applications like real-time image editing.

Most popular GenAI applications

GenAI is transforming multiple industries with its ability to create text, audio, visuals, and synthetic data, enabling innovative applications across various fields.

Language

Large Language Models (LLMs) power text-based applications like essay writing, code development, translation, and genetic sequence analysis.

Audio

• AI-generated music, speech, and sound effects.
• Recognises objects in videos and creates matching audio.

Visual

• Creates 3D images, avatars, videos, and illustrations.
• Designs logos, edits images, and generates visuals for virtual reality and gaming.
• Assists in drug discovery by visualising chemical compounds.

Synthetic Data

• Generates data when real data is unavailable or restricted.
• Helps train AI models efficiently by reducing labelling costs.

Industry Applications

• Automotive: AI simulates 3D environments for car development and autonomous vehicle training.
• Natural Sciences: Assists in medical research, imaging, and genomic analysis. Enhances weather forecasting and disaster prediction.
• Entertainment: Streamlines content creation for video games, films, animation, and virtual reality.

What are the limitations of GenAI?

Despite their advancements, generative AI systems can sometimes produce inaccurate or misleading information. They rely on patterns and data they were trained on and can reflect biases or inaccuracies inherent in that data. Other concerns related to training data include:

• Accuracy Issues – AI can produce misleading or biased results based on the data it was trained on.
• Security Risks – Privacy concerns arise if proprietary data is used, and a lack of transparency can lead to security vulnerabilities.
• Limited Creativity – AI-generated content often lacks true originality and emotional depth, making it feel repetitive.
• High Costs – Training and running generative AI models require significant computational resources.
• Lack of Explainability – AI models function as "black boxes," making it difficult to understand how they generate specific outputs.

Challenges of implementing Generative AI

Implementing generative AI comes with several technical, ethical, and regulatory challenges that organizations must address to ensure responsible and effective use.

Data requirements

This poses a major hurdle, as generative AI models need vast amounts of high-quality, diverse, and unbiased data for effective training. In fields like healthcare and finance, data scarcity and privacy concerns make this even more difficult. Synthetic data—artificially generated data that mimics real-world characteristics—is emerging as a solution to address these limitations while maintaining privacy.

Training complexity

This is another challenge, as developing and fine-tuning advanced AI models requires extensive computational power, expertise, and financial investment. For smaller organisations, this can be a significant barrier. Techniques like distributed training, where multiple machines process data simultaneously, and transfer learning, which refines pre-trained models for specific tasks, help mitigate these difficulties.

Controlling the output

This remains difficult, as models may produce inaccurate, biased, or even offensive content. Ensuring high-quality outputs requires diverse and representative training data, along with robust filtering and monitoring mechanisms to prevent misinformation and harmful content from being generated.

Ethical issues

Ethical concerns are a growing issue, particularly with the rise of deepfakes, AI-generated misinformation, and fraudulent activities. Malicious use of generative AI can spread false narratives, manipulate public opinion, and create security risks. Solutions like digital watermarking, blockchain verification, and AI literacy initiatives can help prevent misuse and improve public awareness.

Regulatory hurdles

They present another major challenge, as laws and policies struggle to keep pace with rapid AI advancements. The lack of clear legal frameworks creates uncertainties around liability, intellectual property, and ethical use. Policymakers need to develop adaptive regulations to ensure responsible AI deployment while fostering innovation.