… and work together on a combined machine learning and knowledge engineering based AI and hybrid intelligence!
The AAAI-MAKE 2022 symposium aims to bring together researchers and practitioners from machine learning and knowledge engineering to reflect how combining the two fields can contribute to hybrid intelligence systems.
In such hybrid architectures, agents that deploy different types of AI work together to solve problems where separate approaches are not providing satisfactory outcomes, such as concerning explainability and data efficiency. Explainability is required for augmenting human intelligence in the loop of AI, and data efficiency (learning from small datasets) is required in many domains where data availability is limited. Hybrid approaches that combine machine learning with the use of logic can explain conclusions and increase data efficiency.
AAAI-MAKE 2022 • AAAI Spring Symposium Series
March 21–23, 2022 @ Stanford University, Palo Alto, California, USA
Submission due: 29th of November 2021 (extended) Notification of authors: 5th of January 2022 (extended) Registration: 18th of February 2022 Camera-ready: 18th of February 2022 Registration for non-authors (FCFS): 4th of March 2022 Symposium: 21-23 of March 2022
- April 14, 2022: Proceedings published.
- March 21-23, 2022: Symposium
- March 7, 2022: first version of the program is available
- February 3, 2022: we are pleased to announce that Natasha Noy (AAAI Fellow; Google Research) will be our keynote speaker.
- February 3, 2022: decision and confirmation on in-person event at Stanford.
- January 18, 2022: Registration for authors is due on 18 February.
- January 5, 2022: 28 submissions have been accepted.
- September 14, 2021: WikiCFP.
- September 10, 2021: EasyChair Smart CFP.
- August 31, 2021: Call for Participation (CfP).
- August 25, 2021: Symposium proposal accepted.
- July 16, 2021: Symposium proposal submitted.
Many current AI solutions rely on machine learning approaches – with great success. Machine learning helps to solve complex tasks based on real-world data. It is most suitable for building AI systems when knowledge is not known or knowledge is tacit. While machine learning is now able to master data-intensive learning tasks, challenges remain. Many tasks require large amounts of training data, especially tasks where events to be predicted are rare. Often, machine output serves merely as a basis for decisions, which humans finally make.
On the other hand, knowledge engineering and knowledge-based systems, which make expert knowledge explicit and accessible, are often based on logic and can explain their conclusions. These systems typically require a higher initial effort during development than systems that use machine learning approaches. However, symbolic machine learning and ontology learning approaches are promising for reducing the effort of knowledge engineering.
Combining machine learning and knowledge engineering opens new possibilities for the reorganization of knowledge work at the interface of humans and machines, intending to bundle the complementary strengths. Knowledge workers without strong AI expertise can contribute to hybrid teams where humans and machines work synergistically to achieve common goals better in collaboration than separately. More efforts need to be made to democratize the combination of machine learning and knowledge engineering to unleash the complementary strengths.
Because of their complementary strengths and weaknesses, there is an ongoing demand in business to integrate knowledge engineering and machine learning for hybrid intelligence systems supporting complex business scenarios and knowledge work. Focusing on only one aspect will not exploit the full potential of AI.
This symposium aims to bring together researchers and practitioners of machine learning, knowledge engineering, and hybrid intelligence from various research centers, companies, and academia. The participants aim to work together on hybrid AI that is explainable, responsible, adaptive, collaborative, and grounded in both data and domain knowledge. Participants shall benefit from each other to avoid pitfalls on the one hand and provide the ground for synergetic co-operations to identify the most promising areas for better results.