This ChatGPT Plugin Is Truly Groundbreaking



  • Author: James L.
  • Full Title: This ChatGPT Plugin Is Truly Groundbreaking
  • Document Note: OpenAI has developed ChatGPT, which will benefit from the Wolfram plugin, bridging the gap between natural language and computational structures. Wolfram Research, which was founded by Stephen Wolfram, aims to realize computational knowledge and numerical computation. Wolfram has produced several industry-leading and paradigm-changing technologies and innovations, including Wolfram Language, Mathematica, A New Kind of Science, and Wolfram|Alpha. These tools could be instrumental in bridging the gap between Conversational AI and computational knowledge.
  • URL: this-chatgpt-plugin-is-truly-groundbreaking-53fc8733328b


  • Wolfram Language is a symbolic programming language as opposed to, say, object-oriented languages. It integrates high level forms with advanced superfunctions, “making it possible to quickly express complex ideas in computational form.” (View Highlight)
  • The ruliad is the computational universe from which all potential realities or perceptions of realities are derived. From this computational “space” one might plot out humanity’s intellectual movements as belonging to a given sector or cross section. Wolfram defines it as:

    “the entangled limit of everything that is computationally possible: the result of following all possible computational rules in all possible ways (View Highlight)

  • Given that we are bounded entities of the ruliad, we find that our perceivable universe is a cross-section of that ruliad made available to us via our conscious awareness. Our conceptual understanding of reality might only occupy a limited slice of the ruliad. This slice expands as we traverse rulial space computationally and therefore intellectually. (View Highlight)
  • Suppose we have two sectors in rulial space: alpha and beta. If alpha occupies an area of rulial space that is disconnected from beta, the knowledge that is derivable from alpha is withheld from beta (and vice versa). Rulial space must exhibit a traceable lineage in order for knowledge to be acquired and actualized. You cannot simply hitch a ride to a distant area in rulial space and expect to find knowledge that is applicable and meaningful in your frame of comprehension (View Highlight)
  • Humans and AI are both computational entities within the ruliad. Yet, the rulial segments they occupy are (mostly) separate from one another. This separation becomes increasingly exaggerated with the advent of each new model or technique. We have a phrase for how we perceive this separation in rulial space. The complexity of AI and its true inner mechanics is explained away as a “black box.” (View Highlight)
  • The black box perspective is used when two conditions are satisfied: (1) the observable output of an AI system meets or exceeds the practical expectations of its developer, and (2) while the initial conditions can be described, the true computational behavior of the system exhibits complexity beyond the developer’s understanding (View Highlight)
  • As our models and techniques become more sophisticated, we become more reliant on their implementation at all levels of society. These systems become so pervasive and instrumental to our daily functioning, that we can no longer imagine a world without them. Do you see the potential terror in that? (View Highlight)
  • Humans and AI coexist in the space of all computational and algorithmic possibility known as the ruliad. They are, however, mostly separate from one another in this space. Wolfram goes as far as acknowledging an “AI civilization” separate from our own. This separation is what we refer to when we say AI is a black box. These systems exceed our expectations and simultaneously evade our understanding. (View Highlight)
  • we arrive at a civilization built upon a technological infrastructure that we fundamentally cannot understand (View Highlight)
  • The simplest, and perhaps most alarming, evidence we have for the arrival of a black box society is the unprecedented difficulty faced by lawmakers in navigating AI regulation. (View Highlight)
  • “Now, something like speed actually can pose a real risk for compliance with IHL. If human operators don’t have the actual ability to monitor and to intervene in processes, if they’re accelerated beyond human cognition, it means that they wouldn’t be able to prevent an unlawful or an unnecessary attack and that’s actually an IHL requirement.” (View Highlight)
  • This is shown in the fundamental inexpressibility of black box AI, and the inaptitude of our current legal frameworks to rise to the challenge. How are we supposed to combat this? Is it even possible to combat this? (View Highlight)
  • These approaches include but are not limited to: • Explainable AI (XAI)AI introspection and visualizationSemantic and symbolic representationConversational AI for AI understandingCollaborative interdisciplinary research (View Highlight)
  • Representation This approach leverages knowledge representation techniques, such as ontologies and semantic networks, to create structured, human-readable representations of AI concepts and modalities. These symbolic representations can help bridge the gap between natural language and computational structures. Sound familiar? (View Highlight)
  • Given the right configuration, a suite of tools such as those provided by Wolfram Research could bridge the gap between the computational complexities of black box AI, and the natural language comprehension that humans possess. (View Highlight)
  • What can be done about AI decisions occurring at a rate faster than human cognition? In this particular strand of semantic and symbolic representational approaches, Wolfram and GPT-4 (perhaps GPT-5) could be used for real-time, human-readable, monitoring and analysis of AI decisions. (View Highlight)
  • This method is bidirectional, meaning that we needn’t only use it to receive a readout from AI, but we can use it to better articulate our demands on AI systems at the outset. By taking on an approach that combines upstream and downstream intervention, we can better plan for and mitigate AWS issues. (View Highlight)
  • How to Get Ahead of the Curve for Wolfram Plugin (View Highlight)