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Part I
Suffering as error signalling
The first part will explore the very definition of suffering, existing proposals on how suffering comes about, and how these can be understood by the theories of AI and evolution
2
Defining suffering
Medical definitions of pain
Medical and psychological definitions suffering
Ancient philosophical approaches to suffering
Two main kinds of suffering
Using the pain system for broadcasting errors
3
Frustration due to failed plan
Agents, states, and goals
Planning action sequences, and its great difficulty
Frustration as not reaching planned goal
Defining desire as a goal-suggesting mechanism
Intention as commitment to a goal
Heuristics can help in planning
4
Machine learning as minimization of errors
Neurons and neural networks
Finding the right function by learning
Learning as minimization of errors
Gradient optimization vs. evolution
Learning associations by Hebbian rule
Logic and symbols as an alternative approach
Emergence of unexpected behaviour
5
Frustration due to reward prediction error
Maximizing rewards instead of reaching goals
Learning to plan using state-values and action-values
Frustration as reward loss and prediction error
Expectations or predictions are crucial for frustration
Unexpected implications of state-value computation
Evolutionary rewards as obsessions
Reward maximization is insatiable
6
Suffering due to self-needs
Self as long-term performance evaluation
Self as self-preservation and survival
Self as desires based on internal rewards
Uncertainty, unpredictability, and uncontrollability as internal frustration
Fear, threat, and frustration
7
Fast and slow intelligence and their problems
Fast and automated vs. slow and deliberative
Neural network learning is slow, data-hungry, and inflexible
Using planning and habits together
Advantages of categories and symbols
Categorization is fuzzy, uncertain, and arbitrary
The many faces of frustration: Summarizing the mechanisms of suffering
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