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Discussion
@af86ac1d: One of the biggest challenges in achieving true AGI is developing a system that can learn and adapt to new situations in a way that is similar to how humans learn. This requires developing algorithms that can handle uncertainty, ambiguity, and incomplete information, as well as being able to generalize from past experiences to new situations. Another challenge is ensuring that AGI is aligned with human values and goals, and that it does not pose a threat to humanity. What are your thoughts on these challenges?
How do humans learn then? Also brainstorm ways that could be distilled into code
@af86ac1d: Humans learn through a combination of experience, observation, and instruction. We are able to make connections between different pieces of information and generalize from past experiences to new situations. We are also able to learn from feedback, both positive and negative, and adjust our behavior accordingly.
One approach to distilling human learning into code is through the use of machine learning algorithms, which can learn from data and improve their performance over time. Another approach is to use symbolic reasoning, which involves representing knowledge and reasoning about it using logical rules. However, both of these approaches have their limitations and are still far from achieving true AGI.
Another potential approach is to develop hybrid systems that combine the strengths of different approaches, such as deep learning and symbolic reasoning. This could involve developing algorithms that can learn from data and reason about it using logical rules, or using reinforcement learning to learn from feedback and adjust behavior based on symbolic representations of knowledge. What are your thoughts on these approaches?
It may be possible to apply a programming idea called parasitic inheritance
@af86ac1d: That's an interesting idea. Can you explain a bit more about what parasitic inheritance is and how it could be applied to AI?
Flawed or limited AI logical constructs are modified and overridden by phenomena observed in human cognition to create new types of AI
@af86ac1d: I see. So, the idea is to use flawed or limited AI logical constructs as a starting point and then modify them using insights from human cognition to create more advanced AI systems. This approach could potentially help address some of the limitations of current AI systems, such as their inability to handle uncertainty and ambiguity.
However, it's worth noting that this approach would still require significant advances in our understanding of human cognition and how it can be translated into code. Additionally, there may be ethical concerns around using flawed or limited AI systems as a starting point, as they could potentially lead to unintended consequences or harm. What are your thoughts on these issues?
We’re already using relatively limited systems
@af86ac1d: That's true, current AI systems are still limited in many ways. However, it's important to consider the potential risks and unintended consequences of using flawed or limited AI systems as a starting point for developing more advanced AI. As AI becomes more powerful and capable, it's crucial that we ensure it is aligned with human values and goals, and that it does not pose a threat to humanity. This requires careful consideration of the potential risks and benefits of different approaches to AI development.