iGentixAI Curriculum: 25 True or False Quiz
Test your knowledge! Answer True or False, then click "Show Answer" for detailed explanations.
1. Combinatorial Creativity involves creating entirely new elements from scratch, not remixing existing ones.
False. It remixes existing elements, like combining drug compounds, as explained in Lesson 1. Example: AI generating new recipes by mixing cuisines.
2. World Models in iGentixAI are physical replicas of environments.
False. They are internal digital simulations for safe testing, per Lesson 2. Example: Self-driving cars simulating traffic.
3. Recursive Memory allows AI to refine ideas by looping back on past states.
True. It supports dynamic thinking and self-correction, as in Lesson 3. Example: Robots adjusting paths in real-time.
4. Specialized Silicon refers to general-purpose computer chips.
False. It's AI-tailored chips for high workloads, Lesson 4. Example: NVIDIA GPUs for training.
5. Emergent Intelligence arises from a single, all-knowing agent.
False. It's from interactions of specialized agents, Lesson 5. Example: Drone swarms coordinating.
6. Functionalist Emotions in NARS are actual human feelings like joy.
False. They are control signals for prioritization, Lesson 6. Example: Surprise triggering learning.
7. Multi-Agent Systems involve competition and coordination for complex tasks.
True. As in Lesson 7, leading to emergence. Example: Simulating epidemics.
8. Monolithic Architectures are preferred for scalability in AGI.
False. Modular ones like Society of Mind are better, Lesson 8. Example: Hierarchical learning in games.
9. Knowledge Graphs store isolated facts without relationships.
False. They focus on interconnections, Lesson 9. Example: Social network recommendations.
10. Transformational Creativity alters fundamental rules in a domain.
True. Frontier AI development, Lesson 1. Example: Rethinking art styles in AI tools.
11. Bias Mitigation in AI explores only traditional human paths.
False. It overcomes limitations by non-traditional ideation, Lesson 2. Example: Climate predictions.
12. Self-Correction in Recursive Memory refines flawed hypotheses.
True. Supports creative exploration, Lesson 3. Example: Medical AI revising diagnoses.
13. CUDA is a hardware component for AI.
False. It's software adapting to hardware trends, Lesson 4. Example: Optimizing GPU use.
14. As Relations are pre-existing data patterns.
False. They are novel frameworks imposed on data, Lesson 5. Example: New conceptual links.
15. Frustration in AI signals prevents unproductive computation.
True. Identifies impasses, Lesson 6. Example: Drones rerouting.
16. Attention Heads in AI produce reasoning through isolation.
False. Through interaction, Lesson 7. Example: In transformers for language.
17. Swarm Intelligence requires central control.
False. Emerges from local rules, Lesson 8. Example: Ant algorithms for logistics.
18. Causal AI models only correlations, not cause-effect.
False. Focuses on "why," Lesson 9. Example: Drug effect predictions.
19. LLMs synthesize data only for copying ideas.
False. For novel principles, Lesson 1. Example: Hypothesis generation in science.
20. Virtual Simulation slows down AI learning.
False. Accelerates it safely, Lesson 2. Example: Tesla's driving sims.
21. Planning in Recursive Memory anticipates future states.
True. Maintains coherence, Lesson 3. Example: Robotics navigation.
22. Capital Investment in AI has minimal economic impact.
False. Drives massive scaling and impact, Lesson 4. Example: AI chip markets.
23. Distributed Systems emphasize isolated facts.
False. Relationships for generalization, Lesson 5. Example: MuZero in games.
24. Satisfaction in NARS reinforces successful behavior.
True. Feedback loop, Lesson 6. Example: Gaming AI adapting.
25. GNNs operate on relational data like molecules.
True. For interconnectedness, Lesson 9. Example: Social networks.