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Affected files: Money/Assets/Financial Instruments.md Money/Assets/Security.md Money/Markets/Markets.md Politcs/Now.md STEM/AI/Neural Networks/CNN/Examples.md STEM/AI/Neural Networks/CNN/FCN/FCN.md STEM/AI/Neural Networks/CNN/FCN/FlowNet.md STEM/AI/Neural Networks/CNN/FCN/Highway Networks.md STEM/AI/Neural Networks/CNN/FCN/ResNet.md STEM/AI/Neural Networks/CNN/FCN/Skip Connections.md STEM/AI/Neural Networks/CNN/FCN/Super-Resolution.md STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/CNN/GAN/StackGAN.md STEM/AI/Neural Networks/CNN/Inception Layer.md STEM/AI/Neural Networks/CNN/Interpretation.md STEM/AI/Neural Networks/CNN/Max Pooling.md STEM/AI/Neural Networks/CNN/Normalisation.md STEM/AI/Neural Networks/CNN/UpConv.md STEM/AI/Neural Networks/CV/Layer Structure.md STEM/AI/Neural Networks/MLP/MLP.md STEM/AI/Neural Networks/Neural Networks.md STEM/AI/Neural Networks/RNN/LSTM.md STEM/AI/Neural Networks/RNN/RNN.md STEM/AI/Neural Networks/RNN/VQA.md STEM/AI/Neural Networks/SLP/Least Mean Square.md STEM/AI/Neural Networks/SLP/Perceptron Convergence.md STEM/AI/Neural Networks/SLP/SLP.md STEM/AI/Neural Networks/Transformers/LLM.md STEM/AI/Neural Networks/Transformers/Transformers.md STEM/AI/Properties.md STEM/CS/Language Binding.md STEM/Light.md STEM/Maths/Tensor.md STEM/Quantum/Orbitals.md STEM/Quantum/Schrödinger.md STEM/Quantum/Standard Model.md STEM/Quantum/Wave Function.md Tattoo/Music.md Tattoo/Plans.md Tattoo/Sources.md
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Three Key Components
- Representation
- Declarative & Procedural Neural Networks#Knowledge
- Typically human-readable symbols
- Reasoning
- Ability to solve problems
- Express and solve range of problems and types
- Make explicit and implicit information known to it
- Control mechanism to decide which operations to use if and when, when a solution has been found
- Learning
An AI system must be able to
- Store knowledge
- Apply knowledge to solve problems
- Acquire new knowledge through experience
Expert Systems
- Usually easier to obtain compiled experience from experts than duplicate experience that made them experts for network
Information Processing
Inductive
- General patterns and rules determined from data and experience
- Similarity-based learning
Deductive
- General rules are used to determine specific facts
- Proof of a theorem
Explanation-based learning uses both
Classical AI vs Neural Nets
Level of Explanation
- Classical has emphasis on building symbolic representations
- Models cognition as sequential processing of symbolic representations
- Properties+Capabilities emphasis on parallel distributed processing models
- Models assume information processing takes place through interactions of large numbers of neurons
Processing style
- Classical processing is sequential
- Von Neumann Machine
- Properties+Capabilities use parallelism everywhere
- Source of flexibility
- Robust
Representational Structure
- Classical emphasises language of thought
- Symbolic representation has quasi-linguistic structure
- New symbols created from compositionality
- Properties+Capabilities have problem describing nature and structure of representation
Symbolic AI is the formal manipulation of a language of algorithms and data representations in a top-down fashion
Neural nets bottom-up
