The Potential of Generative AI
Transforming technology, business and art through innovative AI applications (English Edition)
1st Edition
Published on 31. December 2023
332 pages
978-93-5551-916-0 (ISBN)
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Divit Gupta | Anushree Srivastava
The Potential of Generative AI
Transforming technology, business and art through innovative AI applications
Book
12/2023
BPB Publications
€40.70
Article not available for order
Content
- Intro
- Cover Page
- Title Page
- Copyright Page
- Foreword
- Dedication
- About the Authors
- About the Reviewer
- Acknowledgement
- Preface
- Table of Contents
- 1. Introduction to Generative AI
- Introduction
- Structure
- Objectives
- Defining generative AI and its evolution
- Key components and mechanisms
- Key components
- Generative models
- Autoregressive models
- Mechanisms
- Evaluation
- Evolutionary trajectory
- Breakthroughs in generative models
- Applications in the real world
- Challenges and advancements
- Anticipated future trajectory
- Conclusion
- 2. Generative AI in Industries
- Introduction
- Structure
- Objectives
- Significance and impact of generative AI on various industries
- Healthcare and drug discovery
- Advancing molecular generation
- Enhancing biomedical imaging
- Targeted drug design and optimization
- Personalized medicine and treatment plans
- Case studies and success stories
- Ethical considerations and future prospects
- Responsible data use and patient privacy
- Addressing bias and fairness
- Informed consent in personalized medicine
- Transparency in model decision-making
- Global access to healthcare innovations
- Ongoing ethical discourse and governance
- Art and entertainment
- Generative art
- Algorithmic composition
- Evolutionary algorithms in art
- Interactive generative art
- Machine learning and style transfer
- Procedural generation in digital art
- Collaboration between humans and algorithms
- Generative art installations
- Ethical considerations in algorithmic art
- Creative assistance in content generation
- Interactive and immersive experiences
- AI-generated music and composition
- Visual arts and style transfer
- AI-enhanced filmmaking and animation
- Creative chatbots and interactive storytelling
- Generative AI in virtual fashion design
- AI-generated literature and poetry
- Ethical considerations in AI-generated art
- Marketing and content creation
- Automated content generation
- Personalized marketing campaigns
- Social media management
- Predictive analytics for customer behaviour
- Chatbots for customer interaction
- Visual content generation
- Sentiment analysis in marketing
- Dynamic pricing optimization
- Content curation and trend analysis
- Email marketing optimization
- Manufacturing and design
- Generative design in product development
- Additive manufacturing and 3D printing
- Predictive maintenance and quality control
- Supply chain optimization
- Robotics and automation
- Customization and mass personalization
- Energy efficiency in manufacturing
- Simulations for prototyping and testing
- Human-robot collaboration in manufacturing
- Finance and risk management
- Algorithmic trading and quantitative finance
- Fraud detection and cybersecurity
- Credit scoring and loan approval
- Personalized financial advice
- Market sentiment analysis
- Dynamic risk management
- Automated compliance and regulatory reporting
- Portfolio optimization and asset allocation
- Insurance underwriting and claims processing
- Stress testing and scenario analysis
- Human resources and recruitment
- Automated resume screening
- Predictive hiring analytics
- Candidate matching and recommendations
- Diversity and inclusion initiatives
- Chatbots for candidate interaction
- Employee retention strategies
- Skills gap analysis
- Automated onboarding processes
- Performance management enhancements
- Workforce planning and scalability
- Robotics and automation
- Generative design in robotics
- Automated manufacturing processes
- Adaptive and learning robotics
- Predictive maintenance for robots
- Human-robot collaboration
- Intelligent vision systems
- Autonomous vehicles and drones
- AI-enhanced robotic process automation
- Warehouse and logistics automation
- Urban planning and architecture
- Generative design for urban layouts
- Smart infrastructure planning
- Environmental sustainability in architecture
- Traffic flow optimization
- Mixed-use development planning
- Crisis and disaster response planning
- Heritage preservation and adaptive reuse
- Public space design and accessibility
- Community-driven design through AI feedback
- Challenges and considerations
- Future outlook
- Conclusion
- 3. Fundamentals of Generative Models
- Introduction
- Structure
- Objectives
- Overview of generative models
- Generative adversarial networks
- NVIDIA
- OpenAI
- DALL-E
- Text-to-image synthesis
- Creative AI and beyond
- Continual research contributions
- Ethical considerations
- Collaborative approach
- Education and outreach
- Google Brain
- Image-to-image translation
- Style transfer
- Progressive generative adversarial networks
- Conditional generative adversarial networks
- Interactive generative adversarial networks
- Application in TensorFlow
- Collaborations and publications
- AI ethics and fairness
- Facebook AI
- Image synthesis and enhancement
- GANs for style transfer
- Deep generative models
- Conditional generative adversarial networks and user interaction
- Generative models for video
- Open-source contributions
- AI research for social good
- Ethical considerations
- IBM
- Generative adversarial networks for data augmentation
- Generative models in artificial intelligence research
- Creative applications
- Generative adversarial networks for anomaly detection
- Explainability and interpretability
- Quantum machine learning
- Industry-specific applications
- AI ethics and fairness
- Using generative adversarial networks
- Step 1: Defining the problem
- Step 2: Choosing a GAN architecture
- Step 3: Data preparation
- Step 4: Model training
- Step 5: Optimization and fine-tuning
- Step 6: Application deployment
- Variational autoencoders
- Overview of variational autoencoder architecture
- Training process
- Example: Image generation with Variational autoencoders
- Real-world applications of variational autoencoders
- Challenges and advancements
- Examples of variational autoencoders implementations
- Google's Magenta Studio
- OpenAI's DALL-E
- DeepChem
- PyTorch's variational autoencoders implementation
- TensorFlow Probability
- Variational autoencoders implementation framework
- Autoencoders
- Key concepts
- Autoencoders implementation framework
- CycleGAN
- Key concepts
- Examples of CycleGAN implementations
- ZooGAN
- CycleGAN for art style transfer
- CycleGAN for object transfiguration
- Pix2PixHD
- DeepArt.io
- Using CycleGAN
- Bidirectional Encoder Representations from Transformers
- Key concepts
- Examples of Bidirectional Encoder Representations from Transformers implementations
- Hugging Face Transformers library
- Google's Bidirectional Encoder Representations from Transformers GitHub repository
- Bidirectional Encoder Representations from Transformers for TensorFlow 2.0
- Future directions and ongoing research
- DeepDream
- Origins and working principle
- Artistic applications
- Cultural impact
- Challenges and ethical considerations
- Understanding the underlying principles
- Underlying principles of generative models
- Mathematical foundations
- Probability theory
- Linear algebra
- Generative modelling as mathematical composition
- Generative adversarial networks
- Variational autoencoders
- Training mechanisms
- Loss functions
- Adversarial loss of generative adversarial networks
- Reconstruction loss of variational autoencoders
- Perceptual loss for Style Transfer and Image Generation
- Cycle consistency loss for CycleGAN
- Balancing act of loss functions
- Generative model evaluation
- Ethical considerations
- Comparison with discriminative models
- Transfer learning in generative models
- Case studies and real-world applications
- Fundamental differences between generative and discriminative models
- Decoding the dichotomy
- Training methodology
- Applications
- Uncertainty handling
- Trade-offs and synergy
- Context in ummary
- Conclusion
- 4. Applications Across Industries
- Introduction
- Structure
- Objectives
- Exploring generative AI in healthcare, finance, entertainment, and more
- Generative AI in healthcare
- Medical imaging enhancement
- Application in medical imaging
- Real-world impact
- Example use case
- Industry adoption
- Drug discovery and molecular design
- Application in drug discovery
- Real-world impact
- Example use case
- Industry adoption
- Personalized treatment plans
- Context and challenges
- Generative AI's role
- Real-world impact
- Example use case
- Industry adoption
- Medical text generation
- Context and challenges
- Generative AI's role
- Real-world impact
- Example use case
- Industry adoption
- Predictive analytics for patient outcomes
- Context and challenges
- Generative AI's role
- Real-world impact
- Example use case
- Industry adoption
- Synthetic data generation for research
- Context and challenges
- Generative AI's role
- Real-world impact
- Example use case
- Industry adoption
- Generative AI in the financial sector
- Fraud detection and prevention
- Context and importance
- Generative AI's role
- Real-world impact
- Algorithmic trading strategies
- Context and importance
- Generative AI's role
- Real-world impact
- Customer service chatbots
- Context and importance
- Generative AI's role
- Real-world impact
- Credit scoring and risk assessment
- Context and importance
- Generative AI's role
- Real-world impact
- Generative AI in the entertainment sector
- Generative art and design
- Interactive and immersive experiences
- AI-generated music and composition
- Visual arts and style transfer
- AI-enhanced filmmaking and animation
- Creative chatbots and interactive storytelling
- Ethical considerations in AI-generated art
- Case studies showcasing real-world applications
- Healthcare
- Case study: Medical imaging enhancement in oncology
- Outcomes
- Finance
- Case study: Fraud detection and prevention in financial transactions
- Entertainment
- Case study: AI-enhanced filmmaking and animation
- Manufacturing and design
- Case study: Generative design in aerospace engineering
- Urban planning and architecture
- Case study: Urban planning with generative AI
- Human resources and recruitment
- Case study: AI-enhanced recruitment in human resources
- Robotics and automation
- Other sectors
- Future trends and potential disruptions
- Gartner
- Forrester
- Conclusion
- 5. Creative Expression with Generative AI
- Introduction
- Structure
- Objectives
- Generative AI in art, music, and design
- Algorithmic artistry
- Real-world examples and case studies
- Impact and future trends
- Generative adversarial networks in visual arts
- Evolution of style transfer
- Case study: Google's DeepDream
- Overview of DeepDream
- How DeepDream works
- Visual aesthetics and artistic impact
- Popularization and accessibility
- Impact on the artistic community
- Interactive art installations
- AI-generated NFT art
- Fusion of technology and creativity
- Unique features of AI-generated NFTs
- Artist collaborations and AI
- Tokenized ownership and digital scarcity
- Impact on the art market:
- Harmonies of code and melody
- Algorithmic musical composition
- Unique melodic patterns
- Collaborative initiatives
- Personalized music experiences
- Real-world examples
- Aesthetic revolution in design
- Algorithmic design creativity
- Architectural innovations
- Product and industrial design
- User-centric interfaces
- Real-world examples
- Exploration of design options
- Parametric and performance-driven design
- AI-driven decision support
- Real-world applications
- AI-Generated art installations
- Collaborations between humans and AI
- Google's Magenta and music composition
- Examples and use cases
- Value
- Human-AI collaboration
- NVIDIA's DeepArt and DeepDream in visual arts
- Examples and use cases
- Value
- Human-AI collaboration
- Autodesk's generative design in architecture
- Examples and use cases
- Value
- Human-AI collaboration
- OpenAI's GPT-3 in creative assistance
- Examples and use cases
- Value
- Human-AI collaboration
- Ethical considerations in creative AI
- Bias
- Bias in creative AI
- Types of bias in creative AI
- Real-world examples of bias in creative AI
- Consequences of bias in creative AI
- What can be done to address bias in creative AI
- Copyright and ownership
- Privacy
- Transparency
- Examples of ethical concerns in creative AI
- How to address ethical concerns in creative AI
- Job displacement
- Misinformation and disinformation
- Weaponization
- Autonomy
- Conclusion
- 6. Generative AI in Business and Innovation
- Introduction
- Structure
- Enhancing product development and design
- Leveraging generative AI in product development
- Ford Motor Company
- Eli Lilly and Company
- Nike
- Procter & Gamble
- Optimizing existing designs
- Personalizing products and services
- Retail
- Media and entertainment
- Financial services
- Healthcare
- Innovations in manufacturing and supply chain
- Impact of innovations in manufacturing and supply chain
- Siemens
- Additional benefits of using generative AI to optimize the design of casting molds
- Future of generative AI in casting mold design
- Jet engines
- Additional benefits of using generative AI to optimize the production of jet engines
- Future of generative AI in jet engine production
- Walmart
- Additional benefits of using generative AI to predict demand and optimize inventory levels
- Future of generative AI in demand forecasting and inventory optimization
- Amazon
- Additional benefits of using generative AI to improve route planning
- Future of generative AI in route planning
- Netflix
- Additional benefits of using generative AI to recommend movies and TV shows
- Future of generative AI in movie and TV show recommendations
- Spotify
- Additional benefits of using generative AI to recommend music
- Future of generative AI in music recommendations
- Strategies for leveraging generative AI in business
- Implementation roadmaps
- Cross-functional collaboration
- Data quality and accessibility
- Ethical considerations and transparency
- Contextual understanding
- Intellectual property management
- User feedback integration
- Regulatory compliance
- Strategies for leveraging generative AI in business
- Conclusion
- 7. Deep Dive into GANs
- Introduction
- Structure
- Understanding the architecture and training process
- Understanding the architecture and training process of generative adversarial networks
- Generative adversarial networks applications and success stories
- Deep dive into generative adversarial networks
- How generative adversarial networks work
- Real-world examples
- Applications of generative adversarial networks
- Examples of generative adversarial networks in use
- Challenges and ongoing research in generative adversarial networks
- Mode collapse
- Training instability
- Computational cost
- Ethical concerns
- Future of generative adversarial networks
- New network architectures
- Rationale for new network architectures
- New training algorithms
- Examples of new training algorithms for generative adversarial networks
- Rationale for new training algorithms
- Examples of how new training algorithms are being used in practice
- New objective functions
- Examples of new objective functions for generative adversarial networks
- Rationale for new objective functions
- Examples of how new objective functions are being used in practice
- Ethical guidelines
- Conclusion
- 8. Building and Deploying Generative Models
- Introduction
- Structure
- Objectives
- Practical guide to developing generative models
- Generative adversarial networks
- Variational autoencoders
- Deploying generative models
- Examples of generative model deployment
- Generative adversarial networks deployment on AWS using CLI
- SageMaker Studio
- AWS Console
- AWS SDKs and APIs
- Deploying a variational autoencoder on AWS AI platform
- Example of deployment script
- Deploying variational autoencoder on AWS SageMaker via console
- Deploying a generative adversarial network
- Deploying GAN on Google Cloud AI platform
- Example of deploying a GAN on Google Cloud using the CLI
- Deploying a variational autoencoder on Google Cloud AI Platform using the CLI
- Deploying a generative adversarial network on Microsoft Azure
- Deploying a variational autoencoder on Microsoft Azure
- AI services and tools
- AWS: Amazon SageMaker
- Value proposition of Amazon SageMaker
- Key features
- Use cases
- Examples of how Amazon SageMaker is used
- Google Cloud Platform: AI Platform (Unified)
- Value proposition of Google AI Platform (Unified)
- Key features
- Use cases
- Examples of how Google AI Platform (Unified) is used
- Microsoft Azure: Azure Machine Learning
- Value proposition of Microsoft Azure Machine Learning
- Key features
- Use cases
- Examples of how Microsoft Azure Machine Learning is used
- Deployment considerations and best practices
- Considerations
- Compute resources
- Training
- Deployment
- Model size
- Model latency
- Model accuracy
- Model fairness
- Best practices
- Overcoming common challenges in implementation
- Training data
- Model architecture
- Training process
- Model evaluation
- Deployment
- Conclusion
- Index
