Generative AI

Language translation and interpretation

Large Language Models (LLM), one type of generative AI, help transcend language barriers with translation and interpretation by improving efficiency and accuracy. Fast and accessible translation applications are becoming vital in countries with unique languages, like Korea, Hungary and Thailand, to break down language barriers to other economies in important sectors such as international trade.

Countries with many languages have begun using generative AI to communicate better between national communities. India is using language models to translate laws and other official documents into more than one hundred languages spoken across the country.

Chatbots and virtual assistants

Generative AI plays can power conversational AI systems and enable chatbots and virtual assistants to generate human-like responses to user queries and engage in more natural conversations. Chatbots have become commonplace in customer service online.

ChatGPT is a chatbot based on a large language model that responds to prompts to generate human-like text responses to a wide array of questions thanks to the vast amounts of text date used and deep learning techniques used to train it.

The biggest risks with chatbots and virtual assistants are misleading and incorrect outputs that appear to be true.

Coding and content creation

Generative AI is helping developers write code, creating significant efficiencies. It can generate high-quality, realistic texts and videos, which can save time and cut production costs.

In creative industries such as music and graphic design, AI generates melodies, graphic content and other images. Video game producers can use it to generate game assets and develop playing environments much faster.

For both coding and content, there is always a danger of infringing upon copyright and plagiarism.

Data augmentation

In machine learning, generative AI can augment training data, leading to better models. It can help create additional synthetic data to enhance the diversity and size of datasets. That, in turn, can improve the model’s generalisation and performance.

On the downside, using generative AI for data augmentation may introduce synthetic data that doesn’t accurately represent real-world scenarios, potentially leading to overfitting and reduced model generalisation. In anomaly detection, generative AI could generate novel anomalies not present in the actual data and cause false positives, compromising the detection system’s reliability.

Healthcare and pharmaceuticals

Generative AI can respond to patients’ questions with precise information about preventative care and explanations for medical conditions and treatments. It can generate synthetic medical images and augment existing ones to enhance limited datasets, create training samples and validate machine learning models. This helps improve accuracy and is particularly helpful in scenarios with limited real patient data.

Generative AI can assist pharmaceutical research by generating and optimising molecular structures with desirable properties for new drugs. This enriches and accelerates the drug discovery process.

Generative AI models might inadvertently introduce biases or ethical concerns into medical research and decision-making processes and leak private data into the public domain.