ARTIFICIAL INTELLIGENCE - DCAT

ARTIFICIAL INTELLIGENCE - DCAT

1. ADOPTION

   • Strong VS Weak AI
     • Hypothetical
     • Narrow Focus
     • One Task
     • Common
   • Business
     • Advantage
     • Difficult
     • Impossible
     • Quality Control
     • Reduce Costs
     • High Labour Intensive
     • Creativity
     • Efficiency

2. MECHANISMS

   • Machine Learning
     • Algorithm
     • Lots of Data
     • Patterns
     • Predictive
     • Real World
   • Neural Networks
     • Nodes
     • Output Layer
     • Input Layer
     • Hidden Layers
     • Calculation
     • Probability
     • Patterns
     • Refinement

3. EFFECTIVENESS

   • Turing Test
     • Alan Turing
     • 1950s
     • Not Relevant
     • Effectiveness
     • Chatbots
     • Machine VS Person
   • F1 Score
     • Precision
       • How many of the positive cases the AI predicted were actually positive? False Positives
     • Recall
       • How many of the positive cases did the AI actually find? False Negatives
     • Harmonic Mean
     • Balance
   • Confusion Matrix
     • True / False
     • Actual
     • Predicted
     • Positive
     • Negative
     • Score
     • Graph
   • ROC > AUC
     • True / False
     • Sensitivity
     • Positive Rate
     • Classification

4. COMMON ISSUES

   • AI BIAS
     • Effectiveness
     • Narrow Training Data
     • Variety
     • AI Errors
     • Relevance
   • Incorrect / Incomplete Data
     • Incorrect Classification
     • Wrong Catagory
     • Missing Data
     • Confusion
     • Data Scrubbing
   • Expensive Hardware
     • Disadvantage
     • Small Businesses
     • Expensive Equipment
     • High Requirements for AI

5. IMPACT

   • Polices
     • Privacy
     • Security
     • Ethical AI Use
     • User Support
     • Transparency
     • Accountability
     • Training
   • Hallucinations
     • Incorrect
     • Fabricated
     • Misleading
     • Erroneous
     • Incorrect Pattern Recognition
     • Training
     • Generative
   • Ethics
     • Misinformation
     • Accountability
     • Bias
     • Discrimination
     • Privacy
     • Dependence on AI
     • Human Autonomy and Control
   • Future Proofing
     • Improved Training Data
     • Regular Evaluation
     • Educating Users
     • Sustainabilty
     • Adaptive Learning

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