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Neuro, Fuzzy-o, Logically Speaking
An Intro to 3 AI’s
@Axelisys
@EtharUK
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Artificial IntelligenceDefinition:
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
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Logic Systems“Do You Know If…”
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Logic Based SystemsExpert Systems/Axiomatic Systems – The Truth Tables
• Based on First Order Mathematical Logic Propositional Calculus/Logic, Predicate Calculus
• Used in Electronics (can be reduced to NAND or NOR ‘Gates’) • Foundations of Logic Programming• Expert/Diagnostic Systems
Input P Input Q Output
F F F
F T F
T F F
T T T
AND [Gate]
Input P Input Q Output
F F F
F T T
T F T
T T T
OR [Gate]
Input P Output
F T
T F
NOT [Gate]¬
Input P Output O Validity
F F T
F T T
T F F
T T T
IMPLIES
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Logic Based SystemsExpert Systems/Axiomatic Systems
• 3 Fundamental Rules• Double Negation – “A Not of a Not, is Not a Not”
• Modus [Ponendo] Ponens – “If entering an indoor swimming pool and I dive into it, then I get wet”
• Modus [Tollendo] Tollens – “If I am not wet, then I didn’t dive into a pool, or didn’t enter an indoor swimming pool”
)
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Logic Based SystemsWelcome to Prolog
• Prolog Programming • Operation:
1. Loading Rules and Facts into “Fact Database”
2. Query the Database
• Many Implementations• SWI-Prolog• SWISH - Online at:http://swish.swi-prolog.org/
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Fuzzy Logic“It’s not all that clear cut”
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Fuzzy SystemsDegrees of Membership
• …Because Straight “in” or “out” isn’t enough• (Not Brexit Related)
• “More” or “Less”• The ‘ers’
• Hard-er, Bett-er, Fast-er, Strong-er…
• Breaks First Order Logic• Double Negation• Modus Ponens• Modus Tollens, kinda
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Fuzzy SystemsDegrees of Membership and Discrete Decisions
• OR operations
• AND operations
• NOT (Negation) operations
Source: Heidelberg University
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Fuzzy SystemsSendai Subway Namboku Line
• Opened 1981• Uses Fuzzy Logic to Control Train
Speeds• “A bit more…”• “…A bit less”
• Basis of London’s Docklands Light Railway
Sendai Subway Namboku
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Artificial Neural NetworksThe Computing of the Mind
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[Artificial] Neural NetworksMaking Brains• BIMPA Inspiration• Simulates Operation of Neurons• Several Activation Functions
• Mimics Biological Action Potential
• Represented by Graphs • Nodes = Neurons• Arcs = Weights
• Supervised or Unsupervised Learning• Back-propagate “Errors” to Adjust
Weights
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ANN: Distributed Analogue Computers• Neuron are Chained Together• Mathematical Graph• Circles are Neurons = Vertices• Lines are Dendrites = Edges
• Used for:• Classification • Approximation • Regression• …
• Backpropagation learning
Source: NeuralNetworksAndDeepLearning.com
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ANN: Distributed Analogue Computers• Weights on Lines Multiply Input • Neurons have a biasing• Output of Each Neuron:
• Sum of all weighted (wn) inputs (xn)• Plus a bias (b)• Run through an activation function
• Akin to biological “Action Potential”
• Learns through “Backpropagation”• Partial Derivatives with respect to
• Weights• Biasing
Source: https://github.com/cdipaolo/goml/tree/master/perceptron
Heaviside Step Activation Function
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Demo: TensorFlow PlaygroundGoogle’s Deep Learning, Simplified
http://playground.tensorflow.org
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Thank YouQuestions and Answers
@EtharUK, @Axelisys