Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:

Resume:

1.- AI programming: Early AI focused on writing programs for specific tasks, but lacked generality and robustness outside anticipated scenarios.

2.- Methodological shift: AI research moved from writing programs for ill-defined problems to designing algorithms for well-defined mathematical tasks.

3.- Learners vs. Solvers: Two main approaches in AI - learners (e.g. deep learning) and solvers (e.g. classical planners).

4.- Deep learners: Neural networks with adjustable parameters, trained to minimize error functions on tasks like image recognition.

5.- Deep reinforcement learning: Neural networks trained to make decisions in dynamic environments, achieving superhuman performance in games.

6.- Solvers: Algorithms that map inputs to outputs based on explicit models, like classical planners or SAT solvers.

7.- Generality of solvers: Solvers work across various domains without training, but may require significant computation time for each input.

8.- Problem relaxation: Simplifying complex problems to make them tractable, then using solutions to guide solving the original problem.

9.- Monotonic relaxation: A planning technique that makes action effects monotonic, enabling efficient solution of simplified problems.

10.- Goal recognition: Using planners to infer an agent's goal from observed behavior, applying Bayes' rule and cost considerations.

11.- Generalized planning: Creating strategies that work across multiple problem instances, not just solving individual cases.

12.- IW1 algorithm: A breadth-first search variant that prunes states not making new features true, enabling efficient exploration.

13.- Online planning for Atari: Using planning algorithms to play Atari games directly from screen pixels, competing with deep learning approaches.

14.- System 1 and System 2: Dual-process theory of cognition, with fast, intuitive System 1 and slow, deliberative System 2.

15.- Parallels to AI: Learners resemble System 1 (fast, intuitive), while solvers resemble System 2 (slow, deliberative).

16.- Integration challenge: Combining learners and solvers to tackle more complex problems, similar to human cognition.

17.- AlphaZero: An example of integrating learning and planning, using Monte Carlo tree search to guide reinforcement learning.

18.- Representation bottlenecks: Challenges in representing and solving seemingly simple problems like Blocks World for arbitrary instances.

19.- State variable learning: The need to infer problem state variables from streams of actions and observations.

20.- Feature learning: Developing methods to learn useful general features for planning and model learning.

21.- Abstract representations: Learning finite abstractions of problems to enable general planning for arbitrary-sized inputs.

22.- AI impact: Despite not achieving human-level intelligence, AI can have significant positive or negative societal effects.

23.- Asilomar AI Principles: Guidelines for beneficial AI development, though challenging to enforce in practice.

24.- Societal alignment: The need to align not just AI, but also technology, politics, and economics with human values.

25.- System 1 targeting: Modern society often targets intuitive thinking (System 1) rather than reasoned analysis (System 2).

26.- Compute power impact: Increasing computational resources alone may not solve all AI challenges, especially for novel problems.

27.- Trust in AI: Difficulty in trusting black-box AI systems for critical applications like self-driving cars.

28.- Model learning challenges: The need for better techniques to learn accurate models and plan with imperfect models.

29.- Goal specification: The challenge of expressing complex goals, especially for problems typically solved by System 1 thinking.

30.- Ethical considerations: The importance of addressing ethical dilemmas in AI, including scenarios we lack data for or wish to avoid.

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