Basic concepts on AIGC
  • About the course materials
  • General Course Format and Strategies
  • Introduction
  • Foundations for AIGC
    • Computers and content generation
    • A brief introduction to AI
      • What AI is?
      • What ML is?
      • What DL is?
      • Discriminative AI vs. Generative AI
  • Generative AI
    • Introduction to Generative AI
      • Going deeper into Generative AI models
  • Deep Neural Networks and content generation
    • Image classification
    • Autoencoders
    • GAN: Generative Adversarial networks
    • Transformers
    • Diffusion models
      • Basic foundations of SD
  • Current image generation techniques
    • GANs
  • Current text generation techniques
    • Basic concepts in NLP in Large Language Models (LLMs)
    • How chatGPT works
  • Prompt engineering
    • Prompts for LLM
    • Prompts for image generators
  • Current AI generative tools
    • Image generation tools
      • DALL-E 2
      • Midjourney
        • More experiments with Midjourney
        • Composition and previous pictures
        • Remixing
      • Stable diffusion
        • Dreambooth
        • Fine-tuning stable diffusion
      • Other solutions
      • Good prompts, img2img, inpainting, outpainting, composition
      • A complete range on new possibilities
    • Text generation tools
      • OpenAI GPT
        • GPT is something really wide
      • ChatGPT
        • Getting the most from chatGPT
      • Other transformers: HuggingFace
      • Other solutions
      • Making the most of LLM
        • Basic possibilities
        • Emergent abilities of LLM
    • Video, 3D, sound, and more
    • Current landscape of cutting-edge AI generative tools
  • Use cases
    • Generating code
    • How to create good prompts for image generation
    • How to generate text of quality
      • Summarizing, rephrasing, thesaurus, translating, correcting, learning languages, etc.
      • Creating/solving exams and tests
  • Final topics
    • AI art?
    • Is it possible to detect AI generated content?
    • Plagiarism and copyright
    • Ethics and bias
    • AI generative tools and education
    • The potential impact of AI generative tools on the job market
  • Glossary
    • Glossary of terms
  • References
    • Main references
    • Additional material
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  • Ok, but, what intelligence is?
  • And 'creativity'?
  1. Foundations for AIGC

A brief introduction to AI

Ok, but, what intelligence is?

There is no universally agreed upon definition of intelligence. Different fields and disciplines have their own definitions and concepts of intelligence, and there is ongoing debate and research in the field of artificial intelligence (AI) and cognitive science about what constitutes intelligence.

Some definitions of intelligence focus on cognitive abilities such as problem-solving, perception, autonomous reasoning, planning, and learning, while others include traits such as emotional intelligence or social intelligence.

Additionally, there is ongoing debate about whether certain characteristics, such as consciousness, are necessary for intelligence.

And 'creativity'?

Like intelligence, there is no universally agreed upon definition of creativity. Creativity is a complex and multidimensional construct, and different fields and disciplines have their own definitions and concepts of creativity.

In general, creativity is the ability to generate novel and valuable ideas, products, or solutions.

Some definitions of creativity focus on the process of coming up with new ideas, while others focus on the outcome or the product of that process. Additionally, there are different types of creativity, such as artistic creativity, scientific creativity, and social creativity. The field of psychology has attempted to understand the underlying processes of creativity by studying divergent thinking, convergent thinking, and the role of cognitive biases.

There is ongoing debate about whether computers can truly be creative. Some argue that computers can simulate creativity by using algorithms and data to generate new ideas or solutions, but that this is not the same as true human creativity, which involves things like emotion, intuition and consciousness. Others argue that computers can be creative in the sense that they can produce novel and valuable ideas that were not previously known.

There are some examples of computers being used to produce creative output, such as in the fields of art and music, where computers are used to generate new images, music, and other forms of media. There is also research being done on using AI to generate new scientific hypotheses, however, these are still in early stages and the outputs generated are not considered to be truly creative in the sense of humans.

In summary, while computers can be programmed to simulate certain aspects of creativity, the question of whether they can truly be creative is still a subject of ongoing debate and research.

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Last updated 2 years ago