FAQ

AI -> Artificial Intelligence ML -> Machine Learning While there are several good definitions for these terms as they have been in use over six decades, we use the following definitions from a practical perspective. Artificial Intelligence is about behaviors computer systems may exhibit that are commonly associated with human intelligence. We break the behaviors at a high level as follows: 1) Learn from data (yes everything is data to computers), 2) Organize what is learned from data, and 3) apply what is learned to solve problems. We recognize this is a very simplistic view. We will assure you that keeping it simple helps. Machine Learning is about the first behavior we mention — Learn from data through algorithms.

Generally they are termed as black box models as they don't explain their decision processes very well. We define black box models to exhibit the following: 1) They don't explain their decision processes 2) They don't accept course corrections through Advice 3) They don't communicate during decisioning steps and only provide final results

ML models are typically generated by specific algorithms through model training processes. Traditional ML models generated by ML algorithms such as scikit-learn, XGBoost, TensorFlow, etc., are generated as single software module. They behave in all-or-nothing manner. We define monolithic ML models to exhibit the following: 1) They are generated as single software module 2) They are managed as single software module 3) They are deployed as a single module, perhaps on a pipeline with other modules 4) They are replaced/upgraded as a single software module 5) Their accuracies are measured at model level even through they provide multiple decisions 6) In a multi model ecosystem, models communicate only at the end of their processing without visibility into changes in the ecosystem that may make their conclusions invalid

SHAP stands for SHapley Additive exPlanations and are used to explain the output of a machine learning model. They are termed as reactive because they explain the decisions after they are made by a model for a specific input. Typically, traditional ML models are built first and then Data Scientists curate a set of test inputs to interpret how well the ML model makes its decisions in a comprehensive manner. Thus, the explanations are entirely dependent on the curated datasets to be comprehensive.

SHAP explanations are specific to a model and associated inputs . Thus, if AI is built from multiple ML models, SHAP does not provide composite explanations at AI level. Many real life business problems require multiple ML models to collaborate. In such scenarios, businesses tend to create additional software to produce related datasets and combine SHAP explanations from multiple models to combine to explain AI. Thus, when ML models upgrade the explanation management applications must also be upgraded.

Yes. A few common considerations are important. 1) TeamingSpace will treat external black box ML models as external sources of data rather than decisions. Traditional ML model outcomes do not meet Open Decision Steps standard TeamingSpace has established. 2) It is feasible to use an external ML model's inputs and outputs to learn about the model where such approach is permitted. In this use case, TeamingSpace Open Decision Steps can be used to understand how your current black box model is making decisions.

Traditional ML algorithms such as scikit-learn, XGBoost, TensorFlow, etc., do not generate outcomes that adhere to Open Decision Steps, that TeamingSpace has established. However, TeamingSpace AI Platform's python environment does not prevent the use of other ML libraries. Traditional ML models are treated as external sources of data.

TeamingSpace ML algorithm outputs have been compared with traditional black box ML models and SHAP/LIME provided reactive model explanations for a variety of datasets. We anticipate clients using this option to validate if TeamingSpace ML algorithm outputs are acceptable. Additionally TeamingSpace Open Decision Steps are implemented in multiple formats to help with validations. The accuracy measures associated with Open Decision Steps from algorithmic learning process is verifiable through SQLs. Accuracy measures associated with Open Decision Steps from Agentic AI use is verifiable using Decision Path Analytics reports. TeamingSpace AI Platform will be releasing additional Knowledge Base verifications in subsequent releases.

We recognize the transformative nature of our Innovation. Thus, we have taken initiative in seeking feedback from experts in this field including reputed federal agencies, such as the National Science Foundation (NSF) and the National Aeronautics and Space Administration (NASA). We recommend the following to begin addressing your interests. Begin with a deep-dive of the Innovation. We will share TeamingSpace AI Platform details applied to a few use cases, demonstrate the workflows from persona, such as Data Scientists, Governance stakeholders, etc., and share our platform roadmap. This should give you sufficient details to formulate your next steps.

Great. We recognize that many organizations in dealing with current ML and AI technologies as best as they could, have a greater appreciation of platforms like TeamingSpace. We see a few paths. We will assist you in all these options. 1) Perform a basic technology proof-of-concept. You may pick one of your existing ML model and compare how the same can be managed in TeamingSpace AI Platform to evaluate Open Decision Standard outcomes. This proof-of-concept is limited where your current ML model outcomes are directly used without any Advice augmentation 2) Perform an advanced proof-of-concept. You may pick a business centric use case and use TeamingSpace AI Platform to evaluate your goals. This proof-of-concept goes beyond the basic technology proof-of-concept, where Advice augmentation can be validated 3) Engage with us to assist with your AI journey. We will bring our blueprints, frameworks and platform expertise and assist you in formulating adoption steps

One of the goals of Edico Research, Inc., has been to help clients with applying our AI Frameworks. Many of our AI framework stages were inspired from limitations of existing AI platforms and other real-life scenarios. We bring following considerations to help you: 1) Understanding your current AI platform and associated processes 2) AI Platform independent frameworks such as AI Competency Center 3) MLOps to AIOps transition 4) Adoption of Human Machine Teaming design themes 5) Adoption of Responsible AI principles

Yes. TeamingSpace AI Platform is accessible from marketplaces. If your current AI platform has access to marketplaces, such as AWS, Google, etc., integration is feasible with minimal interfaces. Python API will provide necessary options. If your current AI platform do not have access to marketplaces or do not support Python API a standalone version of the TeamingSpace AI platform can be integrated at multiple architecture layers.