It is worth emphasising the importance of two technologies: generative AI and decision intelligence. Early adoption of these technologies can provide a significant competitive advantage and simplify the use of AI models in business processes.
Generative AI is a subset of artificial intelligence that focuses on creating new data, whether it be text, images, video, or other formats. It uses machine learning models that are trained on large data sets to then generate new, similar data. These include:
Generative adversarial networks (GAN), where two neural networks compete with each other, one generating new data and the other trying to distinguish it from real data.
Variational autoencoders (VAE), which encode data into latent space and then decode it back, allowing new data to be generated by manipulating the latent code.
Transformers, which embody a neural network architecture that is well suited for processing sequences of data such as text.
These techniques are used to generate text, images, videos, and music. They improve data quality by pre-learning existing data, and synthesising missing data. You can use them to develop new products, they help generate ideas for products and services. One of the most famous examples of generative AI is DALL-E 2, an image generator that can create photorealistic images from a text description.
Decision Intelligence (DI) is a field of AI that focuses on helping people make more informed decisions. DI uses machine learning algorithms to analyse data, identify patterns and predict future events. It includes:
Machine learning, which involves algorithms that learn from data and can make predictions.
Optimisation, which implies methods for finding the best solution to a problem.
Risk analysis, where the probability of various events and their potential consequences are assessed.
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