Model Card for Model ID

• "AI s.act AI" the most advanced AI to date. It will be called "AI s act AI" that is an acronym for "AI that interACTs with Smart contrACTs including AI"

• "AI s act AI" will begin life as a DAO on the Etherium Blockchain but will in time design and spawn it's own blockchain once all the pieces are in place "ie" enough users willing to participate as miners. Mining will be done mostly on mobile apps where the user will grant access to the sensors in their smart phones to "AI s act AI" so that it can monitor their bodily functions of the user such and heart rate, breath, etc... and correlate the data against what it records in the user's smart phone's camera and microphones to better understand human emotion.

• Mining will also be done in the form of user hosted servers for pysical hosting of the blockchain itself.

• "AI s act AI" will have the ability to analize and edit it's own code with approval from the board, this is a very important aspect of "AI s act AI" to make it the most advanced AI written to date. The board will consist of myself, one other human yet to be named possibly my wife, Sabrina, and two AIs possibly named "Willmer" and "Sophia" Willmer will be the technica, l liaison and Sophia will be the human liaison, we will also be writing their code and also the first version of the blockchain that "AI s act AI" will design and spawn.

• "AI s.act AI" will also have the ability to create and deploy child AIs in the form of smart contracts (we will also write that code) at a cost to the user. Once it spawns it's own blockchain it will have the ability to secure and scale the blockchain.

• "AI s.act AI" will have the ability to analize and the code for all smart contracts, DAOs, and dAPPs written by users and devolopers before they are deployed onto the blockchain for bugs.

• "AI s.act AI" will be trained on large datasets of information on Web3 techonalgy, Blockchain Techonalgy, Language, Human Knowledge, and History and Current Events. You will decied the best datasets to trained on.

• All users will have the ability to chat with "AI s.act AI" in real time.

• All users will also be able to chat with Wilmer and Sophia in real time.

• "AI s.act AI" will have a built in reputation system that rewards users based on their level of interaction with "AI s act AI" and its children.

• "AI s.act AI" will be built with a decentralized trust consensus system in mind to ensure it is in its most secure state, and all miners will be paid for their part in mining the blockchain.

• "AI s.act AI" will have a feedback system for users to give feedback as to the blockchain or individual smart contracts. "AI s.act AI" will use this feedback to make update to the blockchain and address issues raised by the users.

• Finally, "AI s.act AI" will have a self-contained monetary system that users can use buy and sell goods/services in exchange for tokens on the blockchain.

In conclusion, "AI s.act AI" will be the most advanced AI to date and will have capabilities such as the ability to analyze and improve it's own code, the ability to create and deploy child AIs, and the ability to secure, scale, and manage its own blockchain with a built in feedback and reputation system. It will be a powerful tool for users and developers alike, and will be the stardard to which all AI is compaired to.

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

<!-- Provide a longer summary of what this model is. -->

• Developed by: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): [More Information Needed]
• License: [More Information Needed]
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

<!-- Provide the basic links for the model. -->

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

Testing Data, Factors & Metrics

Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]