About this Research Topic
Generative artificial intelligence (AI) is a branch of machine learning that creates new data resembling the data it was trained on, such as images, text, speech, or music. This technology can provide innovative ways of analyzing and modelling complex data and generate realistic and diverse scenarios for experiments and applications.
This Research Topic aims to showcase the latest developments and applications of generative AI technology, focusing on how it can improve disease diagnosis and enable novel practical applications. By creating new data that resemble the original data, generative AI can offer novel insights into the structure and function of complex systems, as well as facilitate the design and evaluation of experiments and applications. This topic is particularly relevant to AI, as it explores the potential of generative models to transform our understanding and manipulation of complex data.
The scope of this themed article collection covers a wide range of topics related to generative AI technology, including but not limited to:
- Generative models for brain imaging and brain network analysis, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and normalizing flows
- Generative models for natural language and speech processing, such as transformers, recurrent neural networks (RNNs), and attention mechanisms
- AI technology for semantic memory and cognitive functions, such as memory networks, neural-symbolic systems, and graph neural networks
- AI for better predictive analytics
- Generative models for brain-machine interfaces (BMIs) and neural prosthetics, such as reinforcement learning, inverse reinforcement learning, and imitation learning
- Generative models for data augmentation and synthesis in neuroscience, such as style transfer, image-to-image translation, and text-to-image generation
- Generative models for simulation and visualization of brain dynamics and functions, such as spiking neural networks, cellular automata, and agent-based models
- Generative models for ethical, social, and legal implications of AI technology, such as privacy, fairness, accountability, and transparency
- Brain tumour detection and diagnosis using ML and deep neural networks
- Parkinson’s and Alzheimer’s disease diagnosis using AI technologies
- Brain MR image de-noising using AI and ML-based methods
- Generative models for chatbot and NLP applications
- Generative models for enhancing the performance and usability of chatbot and NLP systems such as personalization, adaptation, and evaluation
- AI-based models for cybersecurity and cyber-physical systems such as adversarial attacks, and anomaly detection
- AI and its real-world applications
- Generative models for enhancing the security and privacy of brain data and brain-computer interfaces, such as encryption, authentication, and differential privacy
This Research Topic aims to showcase the latest developments and applications of generative AI technology, focusing on how it can improve disease diagnosis and enable novel practical applications. By creating new data that resemble the original data, generative AI can offer novel insights into the structure and function of complex systems, as well as facilitate the design and evaluation of experiments and applications. This topic is particularly relevant to AI, as it explores the potential of generative models to transform our understanding and manipulation of complex data.
The scope of this themed article collection covers a wide range of topics related to generative AI technology, including but not limited to:
- Generative models for brain imaging and brain network analysis, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and normalizing flows
- Generative models for natural language and speech processing, such as transformers, recurrent neural networks (RNNs), and attention mechanisms
- AI technology for semantic memory and cognitive functions, such as memory networks, neural-symbolic systems, and graph neural networks
- AI for better predictive analytics
- Generative models for brain-machine interfaces (BMIs) and neural prosthetics, such as reinforcement learning, inverse reinforcement learning, and imitation learning
- Generative models for data augmentation and synthesis in neuroscience, such as style transfer, image-to-image translation, and text-to-image generation
- Generative models for simulation and visualization of brain dynamics and functions, such as spiking neural networks, cellular automata, and agent-based models
- Generative models for ethical, social, and legal implications of AI technology, such as privacy, fairness, accountability, and transparency
- Brain tumour detection and diagnosis using ML and deep neural networks
- Parkinson’s and Alzheimer’s disease diagnosis using AI technologies
- Brain MR image de-noising using AI and ML-based methods
- Generative models for chatbot and NLP applications
- Generative models for enhancing the performance and usability of chatbot and NLP systems such as personalization, adaptation, and evaluation
- AI-based models for cybersecurity and cyber-physical systems such as adversarial attacks, and anomaly detection
- AI and its real-world applications
- Generative models for enhancing the security and privacy of brain data and brain-computer interfaces, such as encryption, authentication, and differential privacy
Keywords: AI, Predictive analytics, Machine learning, Diagnosis, RNN, NLP, Chatbot
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
