Pioneering application of an open, privacy-focused digital assistant in manufacturing. The COALA Digital Intelligent Assistant (DIA) core software will base on the privacy-focused open assistant Mycroft, addressing industry-grade security, privacy, and ethics needs.
Introduction of digital-assistant-mediated prescriptive quality analytics using manufacturing resource data. The PREVENTION (PREscriptiVE aNalyTIcs for quality optimizatiON) service of COALA will address Prescriptive Quality Analytics.
Proof-of-concept for the effective augmentation of prescriptive quality analytics via a voice-first interaction.The DIA will use voice as a primary medium (voice-first) to receive user input and provide responses – this speeds up the interaction, is hands-free, and in general an advantage over, oftentimes, overloaded graphical interfaces.
Pioneering use of an explanation engine with a focus on manufacturing analytics. Fundamental research and the prototype development of the so-called “WHY engine” will enable the assistant to explain its responses (e.g. predictions and advices).
Introduction of a novel performance measurement procedure (“X”-WHY test) for the explicability of AI-systems, such as digital assistants in manufacturing. It identifies how many WHY questions the user expects to ask in a scenario in a row. Then, the test measures how many questions in a row the assistant is capable of answering.
Joint application of a Machine Avatar (Digital Twin). This feature is able to track and manage the information gathered by each IoT-connected machine with a Product Avatar, able to track the lifecycle of each product.
Availability of machine and product information through avatars in sharable environment, independent of the data format and totally product-agnostic.
A cognitive advisor with dedicated human-assisted AI methods for enabling transfer of tacit knowledge of experts to novice workers.
Introduction of a new didactic concept to teach workers AI competencies.
Pioneering use of digital assistant for on-the-job training with a voice-first interface.
Availability of a new concept for tacit knowledge transfer based on small training datasets.
Change management process to prepare the adoption of digital assistants at work.
Pioneering use of an explanation engine with a focus on on-the-job training.
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