Constructing Culture in the Age of AI: Images, Art, and Social Impact
1. CLICK on the links below for articles and videos that are foundational to our course.
2. CLICK on the tools at the bottom to experiment with generative AI tools.
Overview:
What is an AI art generator?
AI impact: AI 2027 Paper
AI and copyright
AI and Energy Consumption
Unanswered Questions About AI
Does AI Count as Art?
What is an AI art generator?
AI impact: AI 2027 Paper
AI and copyright
AI and Energy Consumption
Unanswered Questions About AI
Does AI Count as Art?
Artists:
Ben Laposky
Lillian Schwartz
Vera Molnár
Harold Cohen
Rafael Lozano-Hemmer
Refik Anadol
Refik Anadol, coral reefs
Sougwen Chung
Stephanie Dinkins
Trevor Paglen
Ken Feingold
Tay
Mario Klingemann
Botto
Laurie Anderson
Alexander Reben
Rashaad Newsome
Wayne McGregor
Maja Petric
Anna Riddler
Alexandra Daisy Ginsberg
Ben Laposky
Lillian Schwartz
Vera Molnár
Harold Cohen
Rafael Lozano-Hemmer
Refik Anadol
Refik Anadol, coral reefs
Sougwen Chung
Stephanie Dinkins
Trevor Paglen
Ken Feingold
Tay
Mario Klingemann
Botto
Laurie Anderson
Alexander Reben
Rashaad Newsome
Wayne McGregor
Maja Petric
Anna Riddler
Alexandra Daisy Ginsberg
Foundational Concepts:
Algorithm: a set of defined, step-by-step instructions designed to perform a specific task or solve a particular problem. It is essentially a detailed recipe that tells a computer how to execute a procedure or solve a problem. Algorithms can vary in complexity, from simple calculations to sophisticated computational processes, and are fundamental to all aspects of computer programming and data processing. Their efficiency and effectiveness in solving problems are crucial, especially in fields like artificial intelligence, where they underpin the logic and decision-making capabilities of AI systems.
ie: in daily life a cooking recipe or tying your shoes. A series of repeatable steps that achieve a consistent outcome.
Artificial Intelligence (AI): A branch of computer science focused on creating systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Machine Learning (ML): A subset of AI that involves the development of algorithms that can learn and make predictions or decisions based on data, without being explicitly programmed. ie: “Based on your purchase you may also like to buy…”
Neural Networks: Computational models inspired by the human brain, consisting of interconnected nodes (neurons) that process information in layers to perform complex tasks like image and speech recognition.
Large Language Model (LLM): an advanced type of artificial intelligence model that processes, understands, and generates human language. It is "large" in the sense that it is trained on vast amounts of text data, often encompassing billions of words, phrases, sentences, and documents. This extensive training enables the model to perform a wide range of language-related tasks, such as translation, question answering, summarization, and text generation.
ie: Chat GPT or Bard
Text-to-Image Artificial Intelligence: refers to AI systems that can generate visual images from textual descriptions. This involves understanding the text input, often rich with descriptive language, and translating these descriptions into visual elements to create coherent and relevant images. These AI systems typically use advanced machine learning techniques, including Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), to process the text and synthesize images. The capability of text-to-image AI ranges from creating simple graphics based on text inputs to generating complex, photorealistic images from detailed descriptions. This technology has applications in various fields, including art and design, entertainment, and virtual reality.
Deep Learning: A type of machine learning involving neural networks with many layers (deep neural networks), which are particularly powerful for learning patterns in large amounts of data, such as images and videos.
Generative Adversarial Networks (GANs): A class of machine learning frameworks where two neural networks, a generator and a discriminator, are trained simultaneously. GANs are particularly effective in generating realistic images and artworks.
ie: in daily life a cooking recipe or tying your shoes. A series of repeatable steps that achieve a consistent outcome.
Artificial Intelligence (AI): A branch of computer science focused on creating systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Machine Learning (ML): A subset of AI that involves the development of algorithms that can learn and make predictions or decisions based on data, without being explicitly programmed. ie: “Based on your purchase you may also like to buy…”
Neural Networks: Computational models inspired by the human brain, consisting of interconnected nodes (neurons) that process information in layers to perform complex tasks like image and speech recognition.
Large Language Model (LLM): an advanced type of artificial intelligence model that processes, understands, and generates human language. It is "large" in the sense that it is trained on vast amounts of text data, often encompassing billions of words, phrases, sentences, and documents. This extensive training enables the model to perform a wide range of language-related tasks, such as translation, question answering, summarization, and text generation.
ie: Chat GPT or Bard
Text-to-Image Artificial Intelligence: refers to AI systems that can generate visual images from textual descriptions. This involves understanding the text input, often rich with descriptive language, and translating these descriptions into visual elements to create coherent and relevant images. These AI systems typically use advanced machine learning techniques, including Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), to process the text and synthesize images. The capability of text-to-image AI ranges from creating simple graphics based on text inputs to generating complex, photorealistic images from detailed descriptions. This technology has applications in various fields, including art and design, entertainment, and virtual reality.
Deep Learning: A type of machine learning involving neural networks with many layers (deep neural networks), which are particularly powerful for learning patterns in large amounts of data, such as images and videos.
Generative Adversarial Networks (GANs): A class of machine learning frameworks where two neural networks, a generator and a discriminator, are trained simultaneously. GANs are particularly effective in generating realistic images and artworks.
Generative AI Tools:
Large Language Models
Chat GPT
Claude
pi.ai
Bing Chat
Meta.ai
Text-to-image AI
DALLe
Laion
Stable Diffusion
Train an AI Model
Teachable Machine
Music
suno
Image Generator
Nvidia Canvas
Multi Tool Mixer AI
Artbreeder
Video AI
Runway
Large Language Models
Chat GPT
Claude
pi.ai
Bing Chat
Meta.ai
Text-to-image AI
DALLe
Laion
Stable Diffusion
Train an AI Model
Teachable Machine
Music
suno
Image Generator
Nvidia Canvas
Multi Tool Mixer AI
Artbreeder
Video AI
Runway