Task 2 Flashcards
(47 cards)
What is creativity, and how does it differ across domains?
Creativity varies across domains (e.g., sports, arts, science), but all forms share common aspects such as novelty, innovation, and problem-solving.
What is Computational Creativity (CC)?
CC is an AI field that studies how computers can generate creative outputs by simulating human-like creative processes through algorithms and data structures.
What are some key questions that Computational Creativity aims to answer?
Where does creativity reside (process, product, or creator)?
How does creativity relate to expertise and domain knowledge?
How is creativity judged and measured?
How do collective behaviors contribute to creativity?
What are the four key criteria that define creative solutions?
Novelty & Usefulness – The solution must be new and beneficial.
Rejection of Previous Ideas – It must break conventional thought patterns.
Motivation & Persistence – It requires intense effort and perseverance.
Problem Clarification – It often transforms a vague problem into a clear one.
What is the Investment Theory of Creativity?
This theory suggests that a creative agent (human or machine) must recognize and justify the unexpected value of its outputs, rather than just generating content randomly.
Why is intentionality important in creativity?
Mere generation of outputs (like AI producing random images) is not enough; creativity requires awareness of quality, novelty, and impact.
What is pastiche in computational creativity?
Pastiche is the imitation of artistic styles without true originality. AI-generated art often falls into this category, as it replicates patterns without innovating.
What is the goal of CC beyond pastiche?
To develop AI that transcends imitation and demonstrates human-like creativity by taking risks, self-critiquing, and learning from failures.
What is exploratory creativity?
It involves searching within an existing conceptual space, finding new pathways or unconventional connections (e.g., a chess player inventing a new opening strategy).
What is transformational creativity?
It alters the conceptual space itself, leading to paradigm shifts (e.g., Newtonian physics to Einstein’s relativity).
What components make up a neural network in AI?
Processing units (nodes) – Simulate neurons.
Activation states – Nodes activate when they cross a threshold.
Connections between nodes – Can be excitatory or inhibitory.
Input/Output rules – Define how nodes process information.
Learning rules – Like Hebbian learning, where co-activated nodes strengthen their connections.
How do neural networks store and retrieve information?
They act as content-addressable memories, retrieving patterns based on partial inputs (e.g., seeing part of a cat’s face activates the “cat” concept).
What are the four stages of creativity?
Preparation – Gathering knowledge & relevant information.
Incubation – The problem is set aside, allowing subconscious processing.
Illumination – The “aha” moment when a solution appears.
Verification – Refining and testing the idea for feasibility.
How does attention affect creativity?
Focused Attention – Helps analyze details but limits new ideas.
Defocused Attention – Allows more remote associations, increasing creativity.
What is the relationship between intelligence and creativity?
Creativity correlates with intelligence only up to an IQ of 120, beyond which the correlation disappears.
What brain networks support creative thought?
Default Mode Network (DMN) – Generates spontaneous, self-generated thoughts (e.g., imagination, mind-wandering).
Control Network (CN) – Evaluates and refines creative ideas through cognitive control.
Salience Network (SN) – Helps switch between idea generation (DMN) and idea evaluation (CN).
How do these networks interact during creativity?
During idea generation, DMN is more active.
During evaluation & refinement, CN becomes more involved.
Highly creative individuals show greater connectivity between DMN and CN.
Why does AI struggle with true creativity?
AI lacks:
Intentionality (it does not “care” about its outputs).
Emotional reasoning (it cannot weigh moral, aesthetic, or emotional value).
Independent thought (it depends on human-defined inputs).
What is Creativity 4.0?
A new model where AI, human programmers, expert evaluators, and domain knowledge interact to drive AI-assisted creativity.
How does evolutionary computation contribute to AI creativity?
It simulates natural selection using:
Random variations (mutation) – Generates new possibilities.
Selective retention – Keeps the most promising solutions.
Crossover (recombination) – Mixes elements from different solutions.
Unlike traditional AI that hill-climbs toward a known solution, evolutionary computation allows:
Exploration of a vast solution space (parallel searches).
Discovery of unexpected solutions (avoids local optima).
Adaptability to complex creative tasks (mimicking human insight).
How can AI creativity evolve in the future?
AI must:
Move beyond pastiche and learn to assess value.
Integrate more human-like cognitive flexibility (e.g., balancing divergent and convergent thinking).
Improve self-awareness & evaluation mechanisms to filter outputs meaningfully.
What are the main perspectives on where creativity resides?
Creativity can be found in:
The creator (personality, intelligence, motivation).
The process (mental steps, cognitive mechanisms).
The product (originality, usefulness, impact).
A combination of all three.
How does creativity relate to expertise?
Expertise provides foundational knowledge, but excessive expertise can sometimes limit creativity by reinforcing conventional thinking.
