User:Darwin2049/ChatGPT4-Sandbox

(2023.04.23@16:30; sharper, more focused, succinct; shorter, cut to the chase; (2023.04.24@13:30; keep focus on deep learning; what it is, can do; maintain short, preferably bullet point descriptions for discussion purposes; relegate observations, discussions to notes, references section;

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 ChatGPT 3 (CG3) 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;
  • “hallucinations”: solution, answers not grounded in real world;

Impact. There is an ongoing worldwide debate about the impact that CG4-type capabilities can have. Thus far there are voices that are cautioning against its further development. Geopolitical realities however may neutralize these concerns. Should a major nation-state demonstrate heretofore unexpected capabilities with this new technology then all other participants may find themselves in a catch-up mode.

In a recent statement, the president of Russia, Vladimir Putin noted that whoever is able to achieve the high ground in this technology race will possess a sustainable lead in affairs of state. Therefore some middle ground might need to be found to preclude a preemption on the part of one or another authoritarian regime and the risks associated with an unrestrained artificial intelligence system. That this is the case can be seen by the downside risks that have already occurred as a result of the accidental (or deliberate) release of the STUXNET intrusion tool.

An core feature of these deep learning systems is that they utilize large data sets for training purposes. Environments where large data sets are available will have an inherent advantage. At least one such case comes to mind and that is the PRC. It uses a system called WeChat for a remarkable variety of purposes. Individuals interact with each other as well as engage in commercial activities on a daily basis. A data set of this magnitude must offer a considerable advantage to any efforts that the CCP might have in mind.

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. One can envision using CG4 to generate a narrative story. Business leaders, particularly at the executive level such as a CEO or members of a board of directors have as one of their required skills the ability to weave a narrative vision of the company's mission. This narrative is more successful when it is structured in such a way that the various stakeholders can see themselves and their roles articulated in these narratives. The skill of persuasion through storytelling is therefore vital. What CG4 has shown itself capable of is the creation of remarkable story narratives that can be endlessly honed and embellished upon.

• Novel capabilities;

Notes

References

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

GPT3 (OpenAI) This system is understood to have 175 billion parameters;

Midjourney

Stable Diffusion

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