Generative AI

Generative AI is a variety of artificial intelligence (AI) focused on creating data. It has become an increasingly popular area of research in recent years due to its ability to produce results that are both novel and meaningful. The goal of generative AI is usually to create something new, e.g., text, images, music or even video content from scratch using specific algorithms. In contrast with other forms of AI such as supervised learning and reinforcement learning which require labeled datasets for training purposes, generative models can operate without any explicit guidance or direct input from humans.

The most common type of generative model used today is the Generative Adversarial Network (GAN), first introduced by Ian Goodfellow et al in 2014 [1]. GANs consist two neural networks – a generator network responsible for producing synthetic examples and a discriminator network tasked with distinguishing between real samples from fake ones created by the generator network[2]. By competing against each other during the training phase these two networks learn how to generate realistic looking data resembling those found in their original dataset . This technology has been applied successfully across various fields including computer vision[3] , natural language processing(NLP)[4], audio generation[5]and healthcare diagnostics[6]. Another form often employed are Variational Autoencoders (VAE) which use probability-based methods like Bayesian inference techniques instead of adversarial approaches [7][8 ]. VAEs have been used extensively for image synthesis tasks such as face manipulation.[9]

While this field still remains relatively new compared to others within Artificial Intelligence there have already been several notable successes worth mentioning: Google’s DeepDream project produced dreamlike psychedelic visuals based on convolutional neural networks; OpenAI’s MuseNet was able combine multiple styles into single pieces while retaining musical quality; NVIDIA’s GauGAN offered photorealistic landscape rendering capabilities etc.. All these projects represent just some fascinating applications enabled by advances made within Generative AI space over past few years but much more exciting possibilities await us ahead!

References:

1-) Goodfellow IJ et al.(2014). “Generative Adversarial Networks”. arXiv preprint arXiv:1406.2661 2-) Brownlee J.(2020). “How To Develop A GAN For Image Generation With Keras”, Machine Learning Mastery 3- ) Karras T et al.(2018). “Progressive Growing Of GANS For Improved Quality Synthesis”, IEEE Conference On Computer Vision & Pattern Recognition 4-) Zhu WL et al.(2019). “Text Fusion Network : Leveraging Source Specific Contextual Representations For Text Generation “, Proceedings Of The 2019 Conference On Empirical Methods In Natural Language Processing 5 – ) Engel KA at el(2017).”Neural Audio Synthesis Of Musical Notes With WaveNet Auto Encoders” 7th International Workshop On Machine Learning And Music 6 – ) Esteva A at el(2017)”Dermatologist Level Classification Of Skin Cancer With Deep Neural Networks “, Nature 7- ) Kingma DP & Welling M.”Auto Encoding Variational Bayes”,International Conference On Learning Representations 8 – )Higgins ITEet Al.,”beta-VAE Exploring Disentangling In High Dimensions Using A LowDimensional Latent Space 9-, Liu YFet Al,”Unsupervised Attention Based Face Manipulation”
709