CG4 – Questions α

OpenAI - ChatGPT4.
In what follows we attempt to address several basic questions about the onrushing progress with the current focus of artificial intelligence. There are several competing actors in this space. These include OpenAI, DeepMind, Anthropic, and Cohere. A number of other competitors are active in the artificial intelligence market place. But for purposes of brevity and because of the overlap we will limit focus on ChatGPT4 (CG4). Further, we focus on several salient questions that that raise questions of safety, risk and prospects.

Specifically, risks that involve or are:

• Interfacing - Synthesis. how will different groups interact with, respond to and be affected by it; might access modalities available to one group have positive or negative implications for other groups;

• Political/Competitive- Synthesis. how might different groups or actors gain or lose relative advantage; also, how might it be used as a tool of control;

• Evolutionary/Stratification - Synthesis. might new classifications of social categories emerge; were phenotypical bifurcations to emerge would or how would the manifest themselves;

• Epistemological - Synthesishow to reconcile ethical issues within a society, between societies; more specifically, might it provide solutions or results that are acceptable to the one group but unacceptable to the other group;
  
  

Synthesis.
Responding to these questions calls for some baseline information and insights about the issues that this new technology entails. We propose to suggest we look

• Terms are included to help clarify crucial elements and contextualize CG4;
• Sentiment is being expressed about it by knowledgeable observers;
• Theory of Operation of technology paradigm used to produce its results;
• Risks our approach has been to present a few commonly occurring risks, whether inherent or malicious as well as some theoretical risks that might emerge;
• Insights are offered to serve as takeoff points for subsequent discussion;

Terms and Basic Concepts.
CG4 has demonstrated capabilities that represent a significant leap forward in overall capability and versatility beyond what has gone before. In order to attempt an assessment prospective risks suggests reviewing recent impressions at a later date as more reporting and insights have come to light. CG4 has already demonstrated that new and unforeseen risks are tangible; in some instances novel and unforeseen capabilities have been reported. It is with this in mind that we attempt here to offer an initial profile or picture of the risks that we should expect to see with its broader use. By way of of addressing this increasingly expanding topic we offer our summary along the following plan of discourse:

Overview and Impressions.

• what has emerged so far; some initial impressions are listed;
• next are some caveats that have been derived from these impressions;

Theory of Operation.
For purposes of brevity a thumb nail sketch of how CG4 performs its actions is presented;

• included are some high level diagrams
• also links to several explanatory sources; these sources include articles and video content;

Risks.
Our thesis identifies three primary types of risks; these include:

• systemic these are inherent as a natural process of ongoing technological, sociological advance;
• malicious: who known actors categories are; how might they use this new capability;
• theoretical: or possible new uses that might heretofore not been possible;

Notes, References.
We list a few notable portrayals of qualitative technological or scientific leaps;

Overview and Summary so far. If we step back for a moment and summarize what some observers have had to say about this new capability then we might tentatively start with that:

• is based upon and is a refinement of its predecessor, the Chat GPT 3.5 system;
• has been developed using the generative predictive transformer (GPT) model;
• has been trained on a very large data set including textual material that can be found on the internet; unconfirmed rumors suggest that it has been trained on 1 trillion parameters;
• is capable of sustaining conversational interaction using text based input provided by a user;
• can provide contextually relevant and consistent responses;
• can link topics in a chronologically consistent manner and refer back to them in current prompt requests;
• is a Large Language Models that uses prediction as the basis of its actions;
• uses deep learning neural networks and very large training data sets;
• uses a SAAS model; like Google Search, Youtube or Morningstar Financial;