Despite the indisputable notion of benefit that Artificial Intelligence (AI) can bring in society and in any industrial activity, humans typically have little insight about AI itself and even less concerning the knowledge on how AI systems make any decisions or predictions due to the so-called “black-box effect”. Many of the machine learning/deep learning algorithms are opaque and not possible to be examined after their execution to understand how and why a decision has been made. In this context, and to increase trust in AI systems, XMANAI aims at rendering humans (especially business experts from the manufacturing domain) capable of fully understanding how decisions have been reached and what has influenced them.

XMANAI aims at placing the indisputable power of Explainable AI at the service of manufacturing and human progress, carving out a “human-centric”, trustful approach that is respectful of European values and principles, and adopting the mentality that “our AI is only as good as we are”. XMANAI will help the manufacturing value chain to shift towards the amplifying AI era by coupling (hybrid and graph) AI “glass box” models that are explainable to a “human-in-the-loop” and produce value-based explanations, with complex AI assets (data and models) management-sharing-security technologies to multiply the latent data value in a trusted manner, and targeted manufacturing apps to solve concrete manufacturing problems with high impact.

Artificial Intelligence for Manufacturing

XMANAI aims at unleashing the power of Explainable AI (XAI) in manufacturing by establishing trust to business experts in what they nowadays regard as black-box algorithms’ results and by bringing forward what the interplay between data, models and human experts should look like for more informed decision making. XMANAI aspires to become one of the flagship and reference Industrial Explainable AI Platforms in manufacturing with a user- driven, industry-led mentality and a market-oriented approach to address the inherent AI-related hurdles in a realistic and tangible manner.
With XMANAI, Business Experts are able to extract manufacturing-relevant data from the manufacturer’s backend systems via appropriately defined APIs. Through different data mapping and data semantic annotation techniques, they can attach significant added value to their data and increase data quality, all while safety is ensured via the XMANAI attribute-based access control policy framework. Business experts can utilize the services of XMANAI for further expansion of their business operations sharing and trading their data and creating semi-automatic contracts with any stakeholder in their data value chain who is interested in acquiring either the specific data asset, its associated AI model or the derived data intelligence. 

XMANAI progresses beyond the state-of-the art delivering solid methods in both AI and manufacturing that allow data scientists to build, train and validate a catalogue of hybrid and graph-based, interoperable Explainable AI models for different manufacturing problems, that inspire trust, augment human cognition and comply with ethics principles. Data scientists and manufacturing business experts will work together in XMANAI in order to train and validate the baseline AI models while detecting and mitigating bias in training datasets, leading to a portfolio of trained AI models that address core manufacturing problems. XMANAI will effectively consolidate and securely manage the lifecycle of all AI-related assets, semi-automating the management process and alleviating underlying data- and model-related challenges while establishing seamless collaboration among data scientists, data engineers and business experts.
XMANAI will deliver a novel Explainable AI platform fully aligned with the manufacturing needs and idiosyncrasy, acting as a single reference point of access both for AI and for manufacturing value chain stakeholders that allow a seamless interoperation with on-premise environments through open standard- based APIs. It seeks to integrate and further advance existing data-driven technologies, tools and libraries that accelerate the trusted AI model lifecycle management, turning the AI journey into multi-stakeholder value. 

As a research and innovation action project, XMANAI is expected to integrate into its final platform a plethora of tools and methods coming from open-source initiatives, past and ongoing projects, as well as other services that are highly operational. A non-exhaustive list includes:

Data Collection Services – A bundle of services and tools that concern the batch and real-time data and model ingestion as well as data/model/experiment/results listening, encompassing data collection, mapping, linking and provenance aspects.
Data Storage Services – A bundle of services to handle the scalable secure data, models, features, experiments and results storage, along with their metadata, and indexing at the core platform and the on-premise environments of stakeholders.
Secure Asset Sharing Services – A bundle of blockchain-powered services to record the data licenses, manage the data contracts along their lifecycle, broadcast the data / models / intelligence to authenticated users and provide the AI marketplace for AI-related data and baseline/trained AI models.
Data Manipulation Services – A bundle of services that allow for interactive data exploration, data manipulation, and graph management, while enforcing the different access policies at real-time.
AI Model Lifecycle Management Services – A bundle of AI model lifecycle management services for building, fine tuning, collaborating over and evaluating, deploying and executing hybrid and graph AI models, that span basic, machine learning and deep learning algorithms (for descriptive/predictive/prescriptive analytics).
AI Insight Services – A bundle of AI services to provide insights on AI models and their results through interactive visualizations and explanations on the results/features/model, as well as a collaboration space to bring together data scientists, data engineers and business experts to collaborate over building, fine tuning and validating the AI models
Data Governance Services – A bundle of services to manage the data/model/experiments exchanges from / to the core platform, handle the data/models/results provenance, apply advanced access control policies over the data/models/features/results while managing the core manufacturing data model.
Management Services – A set of services concerning resources orchestration and the delivery of notifications in a non- intrusive manner
XMANAI Core Platform – The core, centralized XMANAI platform, integrating all data-driven services bundles to be developed during the project and the master controller for communication with the on-premise environments and the secure execution clusters.
XMANAI On-Premise Environments – The on-premise environments to be installed in the stakeholders’ premises for increased end-to-end security, locally processing various data ingestion-manipulation-AI model execution jobs (through on-premise workers) and storage of different assets (data/models/features/experiments/results) in trusted data container.
XMANAI Manufacturing Apps – The Manufacturing Apps to be designed bringing different technologies to the factories, and to leverage the AI trained models to solve concrete manufacturing problems.
XMANAI Open APIs – The Open APIs to be designed in accordance with the latest API practices to ensure interoperability of the XMANAI platform with external platforms (particularly AI4EU and manufacturing operational systems).
XMANAI Baseline and Trained AI Models – A catalogue of the supported hybrid and graph machine learning and deep learning algorithms that are offered as: (a) a baseline implementation, and (b) a trained and validated configuration for the demonstrators’ purposes.
Manufacturing Data Model & Knowledge Graphs – An extensible manufacturing data model and knowledge graphs building on existing manufacturing semantic models and ontologies.