Videos

COALA Why Engine Prototype for Explainable AI in Manufacturing Industry

This video shows how to acess and use the Why Engine Prototype. Why Engine is a new, experimental solution component,that will allow the COALA Digital Intelligent Assistant (DIA) to answer “why” questions concerning advices and predictions provided by the DIA. Explainability will increase trust in the applied AI-based functions.

More details about the Why Engine Prototype are available here.

COALA Didactic Concept – Implementation in the Textile Production Use Case

This video shows a process-oriented didactic concept for the training and further education through using a voice-based assistant, the COALA Digital Intelligent Assistant.

The aim is to present a structured element for implementation within internal training and further education processes to meet the requirements of the respective use cases. This video gives an overview of implementation of COALA didactic concept in the textile production use case.

COALA Digital Intelligent Assistant for On-the-job Training of Textile Workers

The COALA textile use case focusses on the implementation of COALA Digital Intelligent Assistant (DIA) to support the machine operators, who use their skills and knowledge to prepare and supervise machine operations under strict consideration of the product quality.

This video demonstrates how COALA DIA helps operators during on the-job training by suggestions relevant learning nuggets.

COALA Digital Intelligent Assistant with Cognitive Advisor feature for Production Line Operators

The Diversey liquid production use case in the COALA Project focusses on the implementation of COALA Digital Intelligent Assistant (DIA) to support production line operators towards optimum configuration of the production line for each product.

This video demonstrates a simulation of COALA’s cognitive adivsor feature to support operators and an example application of COALA cognitive assistant in the liquid production line.

COALA at AI-Cafe – Applying Digital Intelligent Assistants in Manufacturing

16 Feb 2022, presentation of COALA at AI-Cafe by Stefan Wellsandt from BIBA. It mainly addresses topics such as how digital intelligent assistants could support workers in factories as well as their challenges, disadvantages and limitations.

The AI-Cafés are being organised and hosted by Grassroots Arts as part of the H2020 project – AI4media activities.

COALA Voice – enabled Digital Intelligent Assistant for Manufacturing – Second Prototype Demo

Oct 2021, a second demo video shows further implementation of the COALA Digital Intelligent Assistant core software at BIBA’s research shop floor.

The first demonstrator (see video below) proved the technical feasibility of COALA’s core infrastructure. This second demo adds security features, multi-language support, and several smaller improvements. The white goods use case and Augmented Manufacturing Analytics feature are used as demonstration scenario.

Human AI Collaboration in Quality Control with Augmented Manufacturing Analytics

5 – 9 Sept 2021, APMS 2021 conference on AI for Sustainable and Resilient Production Systems within the session “Human-centered Artificial Intelligence in Smart Manufacturing for the Operator 4.0“.

Presentation of “Human-AI Collaboration in Quality Control with Augmented Manufacturing Analytics” paper, co-authored by Alexandros Bousdekis (ICCS), Stefan Wellsandt (BIBA), Enrica Bosani (WHR), Katerina Lepenioti (ICCS), Dimitris Apostolou (ICCS), Karl Hribernik (BIBA), and Gregoris Mentzas (ICCS).

Anatomy of a Digital Assistant

5 – 9 Sept 2021, APMS 2021 conference on AI for Sustainable and Resilient Production Systems within the session “Human-centered Artificial Intelligence in Smart Manufacturing for the Operator 4.0“.

Why is it helpful to have a digital assistant in manufacturing? Stefan Wellsandt from BIBA presents an overview of a preliminary catalog of these benefits in manufacturing. The paper “Anatomy of a Digial Assistant” is co-authored by Stefan Wellsandt, Karl Hribernik and Klaus-Dieter Thoben (BIBA).

Data-driven Collaborative Human-AI Decision Making

1 – 3 Sept 2021, 20th IFIP Conference e-Business, e-Services, and e-Society I3E2021 on “AI & Analytics Decision Making”.

Presentation of the paper entitled “Data-driven Collaborative Human-AI Decision Making”. A framework by taking account human experience using interactive reinforcement learning algorithms and a concrete approach for data-driven human-AI collaboration were presented by Gregoris Mentzas from ICCS.

COALA Voice – enabled Digital Intelligent Assistant for Manufacturing – First Prototype Demo

July 2021, a demo video shows the first implementation of the COALA Digital Intelligent Assistant core software at BIBA’s research shop floor.

This demo bases on a so-called “conversational agent team” between the voice-enabled digital assistant Mycroft, a privacy-focused open assistant, and a connected chatbot built with the Rasa framework. Bot agents complement each other and make COALA’s core infrastructure flexible and powerful in terms of Natural Language Understanding and Dialog Management.

COALA Digital Intelligent Assistant in Quality Control of White Goods

20-21 May 2021, 2nd Smart Manufacturing Summit.

Enrica Bosani, Manufacturing R&D Project Manager of Whirlpool Corporation, presented potential benefits of implementing the COALA solution in quality control of white goods. The Whirlpool use case is focused on the implementation of the COALA Digital Intelligent Assistant (DIA) to support the activity in the Zero Hour Testing (ZHT) laboratory of the Microwave and Oven (MWO) factory.

COALA – A Trustworthy Voice-Enabled Digital Assistant for the Manufacturing Industry

Introduction video of COALA project and its offered solution.

COALA aims to develop a human-centred Digital Intelligent Assistant that provides a more proactive and pragmatic approach to support operative situations characterized by cognitive load, time pressure, and little or zero tolerance for quality issues. The COALA solution will transform how workers perform their jobs and it allows companies to maintain or increase the quality of their production processes and their products.