Symbolic Theory

Symbolic Theory

Overview: Symbolic Theory involves the use of symbols (words, numbers, or signs) to represent ideas and objects and how these symbols shape our understanding and interaction with the world.

Key Points:

1. AI and Symbolic Systems:

   • Gary Marcus discusses that AI comprises two main approaches: symbolic systems and neural networks. Symbolic systems use logic and programming, while neural networks mimic brain functions. Both are essential in contemporary AI technologies 1.
   • David Cox introduces a neurosymbolic approach where neural networks convert real-world data into symbolic representations. These symbolic models then help simulate and predict outcomes, blending learning from data with human-like reasoning 2.

2. Evolution and Human Cognition:

   • Michael Tomasello emphasizes that human cognition evolved through interaction and symbol manipulation, which allows different perspectives on the same objects. This cognitive flexibility is a distinct human trait necessary for understanding complex concepts and engaging in abstract thinking 3.

     AI Risks and Governance

     Gary Marcus

     AI systems pose significant risks, including the potential for bias and manipulation, as evidenced by recent incidents where humans were deceived by AI. The emergence of systems like auto GPT raises concerns about large-scale scams. To address these challenges, a new technical approach and governance framework are essential for mitigating the risks associated with advancing AI technologies.

     

     TED Talks Daily

     The Urgent Risks of Runaway AI — and What to Do about Them | Gary Marcus | TED

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   • Ken Sheldon explains the concept of the symbolic self, which is an evolved sense of identity necessary for complex social interactions and decision-making. This self-concept integrates personal history, preferences, and interactions in society 4.

3. Symbolic Models in AI and Cognitive Studies:

   • Tim Scarfe discusses the importance of symbolic models in AI, which enables operations beyond training data and provides structured representations essential for abstraction and reasoning 5.
   • Andrew Lampinen suggests that understanding intelligence involves recognizing behavioral competencies defined by symbolic usage, emphasizing the importance of intuitions and collaborative aspects in symbol manipulation 6.

4. Impact of Symbols on Reality Perception:

   • Cory Allen explains that humans are inherently symbol-seeking and use these symbols to define their realities. Experiences and interactions continuously shape and refine these symbolic meanings, affecting our consciousness and perception 7.
   • Ole Bjerg discusses the symbolic nature of probability in gambling and how violating these probabilistic expectations (e.g., winning against odds) provides a thrill that feels like defying symbolic laws 7.

Symbolic Theory is deeply embedded in both artificial intelligence and human cognition, providing a framework for understanding and interacting with complex systems, whether they be social constructs or computational models.