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Technical Review and Our Answers© |
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Technical Review and Our Answers Table of Contents: • Introduction • Our Project • Technical Review • Coming Soon Introduction: This page is our answers to GitHub's Technical Review. We also posted GitHub's Executive Summary as a quick introduction and we posted the entire doc as a PDF page link following the executive summary. According to GitHub's Technical Review doc, they listed the following topics which we will be addressing each one: 1. Buzzword density without definitions 2. The document repeatedly invokes the same four-item toolkit at almost every stage: a. "ML Engines" b. "AI Added Intelligence Engines" c. "AI Search Tools" d. "Templates Banks" 3. The "Big Data → Values Tables → Long Integer Matrices" pipeline is technically broken 4. The AI/ML project lifecycle is incomplete and partially inverted 5. "Testing is a carbon-copy of Development" is incorrect 6. Multi-agent system claims are unsupported 7. "Development becomes fill-in-the-blank" is wishful 8. The deployment admission contradicts the value proposition 9. "Minimize-human-in-the-loop" is presented as a feature; it is a risk 10. Documentation quality 11. Proprietary methodology with no external grounding 12. What Would Have to Change for This to Become Credible 13. Feasibility Verdict We will start our answers by covering a number of concepts which are also part of our answers. Technical Review: AI Automation and Intelligence Project Roadmap Reviewer: Independent technical review (AI/ML) Executive Summary The proposal reads as a high-level conceptual narrative built around proprietary terminology rather than an executable technical roadmap. It strings together buzzwords ("ML Engines," "Added Intelligence Engines," "AI Search Tools," "Templates Banks," "Long Integer Matrices," "Traversing Trees") that are either non-standard or used in ways that depart from their accepted technical meaning. None are defined in implementation terms. Several core design ideas — most notably the claim that Big Data can be accessed once, distilled into "Values Tables," then converted to "Long Integer Matrices" for all downstream use - reflect a misunderstanding of how data, features, and model training actually work in modern AI/ML systems. The proposal also contains structural gaps that are disqualifying for any serious project plan: no evaluation methodology, no metrics, no data labeling/ground truth strategy, no compute or cost estimate, no timeline, no team composition, no risk treatment, and an explicit admission that the team cannot deliver the deployment stage. My recommendation is at the bottom of this document. The short version: not feasible as written. Technical Review AI Automation and Intelligence Project Roadmap Reviewer: Independent technical review (AI/ML) Concepts Reviews: Redefining Intelligence or AI as a Tool: We have been architecting-designing-developing intelligent systems for years. Our Intelligent systems were developed since AI was nothing but an academic subject taught in universities. Sadly, we were calling ML and AI differently than what they are called today. In short, our thinking, approaches and tools are very much as the current ML and AL. For example, we built our Business Programming System which is the exactly the Machine Learning of today. Our Business Programming System searches data to find ways to help businesses make better decisions. The currently, ML and AI approaches are based on training AI Model-Agent using labeling and Deep Learning and Large Language Models (LLMs). Our Approaches to ML and AI are quite different than the current AI approaches mentioned. First, our ML focuses on data and performs the job of over 40 different types of analysts' jobs or tasks. In short, our ML performs data analysis and helps with decision-making. We defined and categorized each human intelligent characteristic as one task. For example, Planning is one task. Each task would be handled by a software program which we call an Intelligent Engine. We are structuring AI as a collection of Intelligent Software Engines. Our Added Intelligence Engines approach is the process of adding Intelligent Engines to our AI Model-Agent simulate human intelligence. The only way our audience can see what we mean by our Added Intelligence Engines approach is to follow our definition of different level of intelligence or category: 1. Planning 2. Understanding 2A. Parse 2B. Compare 2C. Search 3. Performs abstract thinking 3A. Closed-box thinking 4. Solves problems 5. Critical Thinking 5A. The ability to assess new possibilities 5B. Decide whether they match a plan 6. Gives Choices 7. Communicates 8. Self-Awareness 9. Reasoning in Learning 10. Metacognition - Thinking about Thinking 11. Training 12. Retraining 13. Self-Correcting 14. Hallucinations 15. Creativity 16. Adaptability 17. Perception 18. Emotional Intelligence and Moral Reasoning Each level or category defines human intelligence characteristics. Each of these characteristics would require a software program which we call an Engine. Each Added Intelligence Engine would be integrated in any software system to add such intelligent characteristics to a software. They would help build an AI system which we have control over how it would perform. Not to mention, as we discover more intelligent characteristics, these intelligent characteristics would be integrated with easy without rewriting or redoing the software system or code. First, we need to define this list in term of human intelligence and AI. The following table is our attempt:
How to add Intelligence to software programs? We recommend that each intelligent category should be addressed separately and independently. We need to: 1. Break each category into Zeros and Ones or simpler or subcategories 2. Build one or more engines to create, execute and handle such category 3. The Added Intelligence Engines must parse Data Matrix Record to figure out which Matrices are needed 4. Use Data matrices in their decisions on how to apply their intelligence 5. Test each thoroughly 6. Integrate it into the system and test it further Dynamic Adding Intelligence Engines: Our approach of adding intelligence engines to a software system would give our Switch-Case AI Model-Agent the ability to dynamically increase the software system with additional intelligence categories. Such dynamic approach has the ability to adopt to any new intelligence, technologies, learning, ... etc. It also can help our system adjust to different environment, culture, or major changes in businesses and their customers. Hallucinations Engine(s): We are architecting-design our system with ability to check if the running situation or the case at hand has possible Hallucinations. We need to brainstorm such architect-design-development-testing. At the present moment, we will need to search all possible cases of Hallucinations that can take place. Planning Engine(s) Example: Quick Example-Scenario of Adding the Planning Engine(s): - (this is a rough draft) We need to cover the following aspect of our system: Our Machine Learning: Our Intelligent Machine Learning has the following structure: • Our Zeros and Ones Concept and Components • Pattern Building Matrices • Build Precision Scale • Fine Tune Pattern using Dynamic Business Rules • Library of Patterns (Using History and Lessons Learned) • Data Preparation • Pattern Discovery • Optimization • Build Reports • Data Visualizations • Developing Machine Leading Code • Testing Our Zeros and Ones Concept and Components The basic concept of any computer is the binary bit (0,1). Computer Science was able to turn this binary 0s and 1s into a revolution of technologies that we are using today. With the same thinking, we use the concept of 0s and 1s to build search patterns. We would also use Dynamic Business Rules as guidance in building the search patterns. We build from Zeros and Ones a Byte, then use bytes to build a word and use words to build patterns. The best way to make our concept more clear is by present Donald toy as an example. More deatils see the following link: • Machine Learning Our Machine Learning Engines: What is an Engine? Based on Information Technologies background, an engine may have different meanings. Our Engine Definition: • An Engine is a running software (application, class, OS call) which performs one task and only one task • A Process is a running software which uses one or more engine • A Process may perform one or more task • Engines are used for building loose coupled system and transparencies • Updating one engines may not require updating any code in the system • A tree of running engines can be developed to perform multiple of tasks in a required sequence • Engines give options and diversities Machine Learning Analysis Tier: Our Machine Learning View: Our Machine Learning (ML) View is that ML would perform the jobs of many data and system analysts. In short, our ML is an independent intelligent data and system Powerhouse. Our ML' jobs or tasks would include all the possible data handling-processes. The Analysis List Tasks-Processes Table presents the needed analysis processes which our ML would perform.
Analysis List Tasks-Processes Table We can state with confidence that no human can perform all the listed processes or steps mentioned above, but our Machine Learning would be able to perform all the tasks (included in the Analysis List Tasks-Processes Table) with astonishing speed and accuracy. Our ML Processes-Analysis and Data Classification Our ML processes-analysis would also perform Data Classification. Our ML engines create data Matrices Pools for other ML engines. These data pools would include Data Classification Matrices also. What is the main job of ML Analysis Engines Tier? To help our readers and audience see our ML main job is, we need to present Large Language Model (LLM). Large Language Model (LLM): A large language model (LLM) is a type of artificial intelligence (AI) that can understand, process, and generate human language. LLMs are trained on massive amounts of data, which allows them to perform natural language processing (NLP) tasks. LLMs are trained on vast amounts of text data, which can be broadly categorized into unstructured and labeled data. They learn patterns and relationships within this data to understand and generate human-like text. With the same concept of Large Language Model (LLM), our ML Analysis Engines create data matrices pool to help our Added Intelligence Engines Tier (Decision + Executing + Handler) performs their job. What is the difference between our Machine Learning Analysis Tier and Large Language Model (LLM)? Large Language Model (LLM) is trained on the data, but our Machine Learning Analysis engines learn from the data and create the ML Data Matrices Pool for our ML Added Intelligence Engines Tier (Decision + Executing + Handler). In short, ML Analysis Engines perform all the processes within the Analysis List Tasks-Processes Table plus all the cross-reference of these output matrices pool. These analysis engines help prepare all the need data for our ML Added Intelligence Engines Tier to perform their tasks. Our Main Tools: We are using our ML tools (engines) plus AI Search Tools to parse and convert Big Data into our manageable Data Matrices. Our ML engines are the core power in the Big Data parsing and conversions and AI Search Tools are supporting search tools. We do need to use AI Search Tools such ChatGPT or any AI Search Tools for value matching and errors correction. AI Search Tools: These AI Search Tools have very impressive text and graphic analysis which we are harnessing their power. Our main power when it comes to developing any AI system is our Machine Learning Engines and "not AI Search Tools." These AI Search Tools are very handy when it comes text and graphics. Therefore, we use These AI Search Tools when in comes to image analysis, voice-to-text messages or any text-image messages comparisons. Note: It is easier, convenient and cost effective to use these AI Search Tools instead of building them. Developing them would be costly effort which would require tremendous efforts, time and testing. Therefore, AI Search Tools is a fare better choice than developing these AI Search Tools. Our project goal is to build automation and intelligent system harnessing the power of AI and Machine Learning (ML). Consequently, we need to build the system’s structure. Our structure must be AI-based, practical, modifiable, reusable, cloud-based and uses templates. We also use Development Banks. Therefore, we need to define a number of terms in the following sections. Templates: Templates provide a reusable framework that saves time, ensures brand consistency, and minimizes errors. They eliminate the need to start from scratch, allowing you to focus purely on content while maintaining a polished, professional standard across all your work. System Development Templates Banks: System development banks (SDBs) and Multilateral Development Banks (MDBs) leverage pre-built templates to standardize operations. These templates accelerate digital transformation, ensure regulatory compliance, and enable institutions to scale sustainable infrastructure projects. Our Templates Banks: Our Templates Banks are our depositories of standard templates which are used by most known system. For example, we would be collecting every possible template used by top software vendors. We would be using AI system to parse and create the best practice templates which would reflect AI choice of the best templates. We would also create different template banks for different requirement and different systems usage. For example, there would be: 1. Project Requirement Templates Bank (addressing different projects) 2. Business Analysis Templates Bank (addressing different businesses) 3. System Analysis Templates Bank 4. Data Structure Analysis Templates Bank 5. Architect-Design Templates Bank 6. ML analysis templates bank 7. Dictionary Templates Bank 8. Business Token Templates Bank 9. Testing Processes Templates Bank The primary objectives of our bank templates are to streamline and boost AI development workflows and efficiency. They act as standardized frameworks to help reduce development risks, time, accelerate automate-development and handle complex data. XX XX XX |
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