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Is an AGI even required to achieve similar results?


A Comprehensive Artificial Intelligence Services technical report model by Drexler, from 2019, seems useful to revisit at this time. Instead of focusing on the hypothetical scenario of a single superintelligent agent that surpasses human intelligence, we should, the report argues, consider the more realistic possibility of a diverse and interconnected network of AI systems that provide various services for different tasks and domains. They call this approach Comprehensive AI Services (CAIS).

The main advantages of CAIS are that it avoids some of the conceptual and technical difficulties of defining and measuring intelligence, and that it allows for a more fine-grained and flexible analysis of the potential benefits and risks of AI. 

It's also a good way of considering where we have arrived at, with AgentGPT's operating via Hugging Face or via AutoGPT for example. By connecting a range of Narrow AI tools to perform the tasks that they are optimised for, and having a 'manager' assign the allocation of these tasks, giving the correct prompts for each agent, this 'comprehensive' approach could provide similar results to an AGI? 

The authors of the technical report suggested that CAIS can help us better align AI systems with human values and goals, by enabling more human oversight and collaboration, and by fostering a culture of responsibility and accountability among AI developers and users. Which seems far more plausible than trying to do that with a monolithic AGI.

The authors conclude by outlining some of the open questions and challenges that CAIS poses for AI research and governance, such as how to ensure the reliability, security, and interoperability of AI services, how to balance the trade-offs between centralization and decentralization of AI systems, and how to promote ethical and social norms for AI use and development. These are questions that exist for all AI systems. 



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