User:Darwin2049/ChatGPT4-Sandbox

(2023.04.23@16:30; sharper, more focused, succinct; shorter, cut to the chase;

Overview. In the following we try to analyze and contextualize the current known facts surrounding the OpenAI ChatGPT4 (CG4) system.

• CG4: we offer a summary of how OpenAI describes it; put simply, what is CG4?
• Impressions: our focus then moves to examine what some voices of concern are saying;
• Impact: we then shift focus to what is being said about it; we note that in some cases people express astonishment and wonder; in other case they express fear and concern;
• Scenarios: here we focus on how CG4 might be used be used in expected and unexpected ways;

What is CG4? CG4 is a narrow artificial intelligence system, is rumored to have been trained on one trillion parameters; it:

• 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;
• 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;

Impressions (observations made by informed individuals)

• Some things that people have said about it:
  • possess no consciousness, sentience, intentionality, motivation, feelings or self reflectivity;
  • is a narrow artificial intelligence;
  • is available to a worldwide 24/7 audience;
  • can debug and write, correct and provide explanatory documentation to code;
  • explain its responses;
  • write music and poems;
  • summarize convoluted documents or stories;
  • score in the 90% level on the SAT, Bar and Medical Exams;
  • provide answers to homework;
  • self critiques and improves own responses;
  • provide explanations to difficult abstract questions;
  • calibrate its response style to resemble known news presenters or narrators;
  • provides convincingly accurate responses to Turing Test questions;

• Positive.
  • has demonstrated performance that suggests that it can pass the Turing Test;
  • CG4 derivative such as a CG5 could exhibit artificial general intelligence (AGI) capability;
  • emergent capabilities;
  • spatial, mathematical reasoning;

• Concerns.
  • knowledge gaps: inability to provide meaningful or intelligent responses on certain topics;
  • deception: might be capable to evade human control, replicate and devise independent agenda to pursue;
  • intentionality: possibility of agenda actions being hazardous or inimical to human welfare;
  • economic disruption: places jobs at risk because it can now perform some tasks previously defined within a job description;
  • emergence: unforeseen, uncontrollable capabilities; Bengali self taught;
  • “hallucinations”: solution, answers not grounded in real world;

Impact.

Speculations. The range of possible areas that CG4 is very broad; in fact, society wide; therefore we will try to maintain focus by presenting a few possible scenarios of how its capabilities might be used;

• Influence, Persuasion;
• Novel capabilities;

Notes

References

Deep Learning Models. There are many variations on the deep learning network architecture models current today. 

GPT3 (OpenAI)

Generative Pre-Trained Transformers. A transformer is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input (which includes the recursive output) data. It is used primarily in the fields of natural language processing (NLP) and computer vision (CV).

The top ten types of deep learning models include:

• multilayer perceptrons (MLPs)
• radial basis function networks (RBFNs)
• convolutional neural networks (CNNs)
• recurrent neural networks (RNNs)
• long short-term memory networks (LSTMs)
• restricted boltzmann machines (RBMs)
• self organizing maps (SOMs)
• generative adversarial networks (GANs)
• autoencoders deep learning algorithm
• deep belief networks