Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

Resume:

1.-The workshop bridges AI and cognitive science, exploring their shared history and potential for collaboration.

2.-Play and exploration in children supports learning by helping build better models of the world.

3.-Children's play is sophisticated from the start, integrating social cues and reasoning counterfactually.

4.-Experiments show children's exploratory play quantitatively tracks the discriminability of contrasts.

5.-Children's play involves making up arbitrary problems and inventing solutions to bootstrap new ideas and plans.

6.-The hard problem in cognition is thinking and generating new hypotheses, not just learning.

7.-Problems contain information that constrains the search space for solutions even before having the answers.

8.-Children violate rational action principles in play to create novel goals by hacking their utility functions.

9.-AI researchers should understand development, caregiving, diversity of cognition and purposes beyond task performance from cognitive science.

10.-Cognitive scientists should understand the variety of tools and approaches in AI beyond just neural networks.

11.-Natural experiments are proposed to understand innate knowledge, approximations, modularity, spatial reasoning in humans for informing AI.

12.-Leslie Kaelbling discusses pursuing shared principles between AI and natural intelligence while acknowledging differences.

13.-Young children and RL agents are compared exploring the same virtual environment to study efficient exploration.

14.-Autism-inspired AI looks at how atypical visual and social reasoning abilities can inform cognitive modeling.

15.-Visual spatial reasoning is modeled using visual imagery operations in a hierarchical process inspired by cognitive theories.

16.-Public self-consciousness modeled using an imaginary listener improves consistency in dialogue agents without additional annotation.

17.-Brain-inspired replay combining generative feedback and context-dependent gating reduces catastrophic forgetting in neural networks.

18.-Global-local networks using sparse depth cues learn 3D scene perception in a baby-like way through vision and interaction.

19.-A Kalman filter applied to the successor representation enables flexible value computation and transition revaluation.

20.-Eigenvectors of the SR matrix support hierarchical representation and temporally abstract exploration in a grid cell model.

21.-The hippocampus supports key cognitive functions difficult for AI - episodic memory, model-based planning, task-switching, imagination.

22.-Standard RL optimality criteria give way to natural decision making under resource constraints in a cognitive model.

23.-Sketch-object correspondence relies on high-level visual features and social reasoning to communicate relevant information efficiently.

24.-Learning object-sketch mappings that expose analogical structure is a challenge for current visual reasoning systems.

25.-Entropy regularization emerges in signaling games, leading to efficient communication that transmits only necessary information.

26.-Product-Kanerva memories enable compression, binding, relational learning and selective reconstruction of objects in a neural network.

27.-Continuous relaxation of graph matching enables knowledge transfer in analogical reasoning, qualitatively matching human asymmetries.

28.-Marr's levels of analysis help disentangle assumptions and focus debates at the right level in AI research.

29.-Mouse visual cortex responses are best predicted by models capturing edges/segments rather than supervised object recognition features.

30.-Acoustic word embeddings exhibit properties like onset bias and encode phonetic features similar to human percepts.

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