Unlocking the Possibilities of Artificial Intelligence: Exploring Self-Directed Thought and Agency

In this article, we explore the idea of artificial intelligence (AI) and how it relates to the concept of self-directed thought and agency. We look at the work done by AI researchers on understanding larger models, and how their technique works for GPT-4. We also discuss the possibility of using AI to create goal-directed agents or A-Life, which was last explored in the 1970s. We look at examples of existing AI systems that are better than humans and compare them to GPT-4’s reasoning capabilities. Finally, we consider what it would take for an AI system to have true self-directed thought or agency and whether that is possible without having physical experiences in space and time.

AI has become increasingly popular over recent years as a tool for automating tasks such as search engines, calculators, compilers, word processors, Wikipedia searches etc., with many believing they can be used far beyond just these specific tasks. Researchers have been focusing on making systems that process questions passively through “mind wandering” techniques rather than setting terminal preferences or instrumental goals to achieve those preferences as an autonomous agent would do. This has led many people question whether machines can ever truly possess self directed thought or agency despite being able to answer questions correctly as well if not better than humans in some cases.

However there is still a lot of research being done into understanding larger models with more complex structures composed of multiple neurons which may be essential for creating an AGI with true reasoning skills like humans possess; such as deducing causal relationships from observations rather than merely providing answers from previously acquired knowledge like current AI systems do now. The paper discussed outlines one example where they focused on individual neurons within certain layers but found it difficult when attempting to explain later layers due to their complexity; additionally they noted that this technique was already very computationally intensive suggesting a need for further algorithmic changes before we move onto something more powerful like AGI capable of extrapolative thinking rather than just descriptive reducing action like current AI systems are doing today e.g ChatGPT scoring 97% in casual discovery but only 86% in causality tests which suggests there is still room for improvement before creating something truly revolutionary like an AGI capable of human level reasoning skills even if its much easier task compared some problems being solved by current research teams today.. It will take a lot more research into understanding larger models with complex structures composed multiple neurons before we get close enough tp achieving this goal; however if successful then perhaps then could open up possibilities previously unimaginable leading us towards building something closer resembling our own intelligence without having any physical experience . Only time will tell what can be achieved but until then let us enjoy all the useful applications available from existing machine learning techniques!

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