Generative AI (Gen AI)

GANs are used for image generation, style transfer and content creation. They consist of two neural networks: a generator and a discriminator. The generator creates data while the discriminator evaluates its authenticity, improving its ability to create realistic data.  

VAEs operate on the principles of probabilistic modelling for image generation, data compression and image reconstruction. They aim to learn about the underlying probabilistic distribution of the data. 

RNNs are neural networks specifically designed for sequential data, such as time series data. They are primarily used for text generation, machine translation and speech recognition.  

LSTMs are a specialised type of RNN that captures long-range dependencies in sequential data. They are the most effective in NLP tasks, including language modelling, text generation and sentiment analysis. 

GPT models leverage transformer architecture and large-scale pre-training on text data to generate text. They are primarily used for natural language understanding and generational tasks, including chatbots, content generation, translation and more.  

Transformers are neural networks that use deep learning architecture to predict new text based on sequential data. They can learn context and “transform” one type of input into a different type of output to generate human-like text and answer questions.  

Gen AI generates new data that resembles those in a given dataset. Generative AI aims to understand and replicate the structures and patterns present in the data to create new samples.  

Gen AI uses various models to generate data by learning from actual data samples or creating representations of a probability distribution that generates the data.   

It can also be applied to image generation, text synthesis, music composition, data augmentation and more. 

Predictive AI primarily predicts future events or outcomes based on historical data. It uses various machine learning algorithms, such as regression, classification and time-series forecasting models to analyse data patterns and make informed predictions about what will happen next.  

These models learn from labels or historical data to predict unseen or future data. 

Common use cases for predictive AI include financial forecasting, demand forecasting, risk assessment, healthcare diagnosis, recommendation systems, predictive maintenance and resource-allocation optimisation. 

LLMs primarily focus on understanding and generating human-like text. They excel at language translation, text summarisation, sentiment analysis, answering questions and text generation. 

LLMs are typically based on deep learning architectures, particularly transformer-based architectures. They are trained on large text datasets, learning language patterns, grammar, context and semantics. 

LLMs are commonly used for NLP tasks, including chatbots, virtual assistants, content generation and more. 

Gen AI gives banks many opportunities to enhance operational efficiency, elevate customer experiences, manage risks and combat financial fraud. 

Through capabilities like market simulation, fraud detection, portfolio management, algorithmic trading support, personalised customer service, Anti Money Laundering (AML) compliance and credit scoring and risk assessment, Gen AI empowers financial institutions to optimise strategies, detect and prevent fraudulent activities, tailor services to individual clients and navigate complex regulatory landscapes. 

By leveraging Gen AI technologies, banks can foster innovation, improve decision-making processes and bolster trust and security within the financial ecosystem. 

AI stands poised to revolutionise healthcare, promising more accurate diagnoses, personalised treatments and efficient drug discovery processes. 

Gen AI addresses critical challenges in healthcare, from data scarcity to treatment optimisation through medical image generation, drug molecule design and personalised treatment planning. 

Moreover, its applications extend to medical text generation for streamlined documentation, medical education through realistic simulations and healthcare chatbots for personalised patient interaction, ultimately advancing patient care, medical training and clinical efficiency. 

Gen AI can revolutionise education by fostering personalised learning experiences, enhancing instructional efficiency and broadening access to high-quality educational materials. 

Through capabilities like curriculum design, personalised learning plans, virtual tutoring, immersive learning experiences, automated assessments and content summarisation, Gen AI empowers educators to tailor instruction to individual student needs, facilitate interactive learning and provide timely feedback.  

By leveraging gen AI tools, educators can create engaging and effective learning environments that cater to diverse learning styles, ultimately enriching the educational experience for students and educators alike.