ai.notebooks

⚡ Some notebooks/notes concerning Artificial intelligence

[ ⚠️ ] Paths and resources are noted in the end.
[ 📎 ] This is a Personal effort / research. If you wanna Help. Don't hesitate to Email-me: KMx404@protonmail.com

|__ ai.notebooks
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   |__ Basic introductions
   |  |__ The definition & Components
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   |__ Reasoning, problem solving 
   |  |__ developing earlier algorithms 
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🍃 research factors:

• Basic introduction (2/3)
  
• Reasoning, problem solving (1/3)
  
• Knowledge representation (0/3)
  
• Planning (0/3)
  
• Learning (0/3)
  
• Natural language processing (0/3)
  
• Perception (0/3)
  
• Motion and manipulation (0/3)
  
• Social intelligence (0/3)
  
• General intelligence (0/3)
  
• Cybernetics and brain simulation (0/3)
  
• Statistical (0/3)
  
• Integrating the approaches (0/3)
  
• The limits of artificial general intelligence (0/3)
  
• Ethical machines (0/3)
  
• Machine consciousness, sentience and mind (0/3)
  
• Superintelligence (0/3)
  
• Risks on narrow AI (0/3)
  
• Risks of general AI (0/3)
  

[Wikipedia] [Basic introduction]  

Artificial intelligence (AI), sometimes called machine intelligence is intelligence demonstrated by machines unlike the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

So in other words we can describe AI as a system ability to correctly interpret external data, to learn from such data and to use those learnings to achieve specific goals and tasks throguh flexible adaptation.

Reasoning, problem solving

Early researchers developed algorithms that imitaed step-by-step reasoning that humans use when they solve puzzles or make logical deductions. BY the late 1980s and 1990s, AI research had develped methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.

These algorthms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgements.

Knwoledge representation

Over time humans tried to come up with methods to represent knowledge using something we call «Knowledge engineering» its mainly sets of methods and attempts to gather explicit knowledge possessed by experts in some narrow domain.