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What is generative AI?

[fa icon="calendar"] Mar 8, 2023 8:47:11 AM / by Chris Davies

Artificial Intelligence (AI) has come a long way in the past few decades. Today, AI is capable of performing a wide range of tasks, from recognizing objects in images and translating languages to playing games and driving cars. However, one of the most exciting and rapidly developing areas of AI is generative AI, which aims to create new content, rather than simply analysing or recognising existing content.

Generative AI is a subset of machine learning that involves using algorithms to generate new content, such as images, music, text, or even videos. Unlike traditional machine learning algorithms that are trained on large datasets and used to make predictions based on that data, generative AI models are trained to create entirely new content based on the patterns and structures present in the data.

At Model Office we are now incorporating generative AI onto MO® our compliance chat bot and other software resources such as Consumer Duty client value survey, where retail advice firms can generate their own client surveys to track good client outcomes. 

Generative AI can be divided into two main categories: discriminative models and generative models. Discriminative models are used to classify or recognize data, while generative models are used to create new data. Some common examples of generative AI include:

  • Image synthesis: Generative models can be trained to create realistic images that look like they were taken by a human photographer. These models can be used for a variety of applications, from generating realistic training data for computer vision algorithms to creating virtual worlds for video games.
  • Text generation: Generative models can also be used to create new text, such as news articles, product descriptions, or even entire novels. These models are often used in natural language processing applications, such as chatbots or virtual assistants.
  • Music composition: Generative models can be used to compose new pieces of music, either by mimicking the style of a particular composer or by creating entirely new compositions based on patterns and structures found in existing music.
  • Video generation: Generative models can also be used to create new videos, either by combining existing video clips or by creating entirely new video content based on patterns and structures found in existing videos.

One of the most popular and widely used generative AI models is the Generative Adversarial Network (GAN), which was introduced in 2014 by Ian Goodfellow and his colleagues at the University of Montreal. GANs are based on two neural networks that compete against each other: a generator network that creates new content, and a discriminator network that tries to distinguish between the generated content and real content. The two networks are trained together, with the generator network trying to create content that can fool the discriminator network, and the discriminator network trying to correctly identify which content is real and which is generated.

GANs have been used for a wide range of applications, from creating realistic images of faces and landscapes to generating new music and videos. However, GANs are also notoriously difficult to train, and often require large amounts of data and computing power to achieve good results.

Another popular generative AI model is the Variational Autoencoder (VAE), which was introduced in 2013 by Diederik Kingma and Max Welling. VAEs are based on two neural networks: an encoder network that maps input data to a latent space, and a decoder network that maps the latent space back to the original input data. VAEs can be used for a wide range of generative applications, from image and text generation to video and music generation.

Generative AI has the potential to revolutionize a wide range of industries, from entertainment and gaming to healthcare and education. However, there are also important ethical and social considerations to take into account, such as the potential for generative AI to perpetuate or amplify biases present in the training data, or the potential for generative AI to create content that is misleading or harmful.

In conclusion, generative AI is an exciting and rapidly developing area of AI that has the potential to transform. 

Written in partnership ChatGPT

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Topics: Financial regulation, Financial business development, fintech, Human resource development, client engagement, regtech, Constructive compliance, Risk management, practice management, FCA, AI, artificial intelligence

Chris Davies

Written by Chris Davies

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