Our Experience at IntelliSys 2021
This year our team had the privilege of attending the prestigious intelligent systems (IntelliSys) conference, which was held virtually, to present our work “Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data”. In this work, we discussed how different interactions between users within a Super-App provide a new and novel source of information to predict borrower behavior and the implications on financial inclusion.
IntelliSys is a conference focused on the areas of intelligent systems and artificial intelligence (AI) and how it applies to the real world. During this year’s conference, we had the opportunity to learn from researchers around the world and a wide range of applications, from deep learning and natural language processing to smart health. Next, we will present, what we consider personally, the highlights of the conference.
What is knowledge?
The conference began with Keynote, Should You Believe Wikipedia? by Dr. Amy Bruckman, wherefrom an epistemological perspective the author presents the meaning of knowledge, how it is built and how it affects us AI professionals. In this way, the author presents that knowledge is a collaborative achievement, a justified true belief, and to achieve knowledge appropriately we need to embody epistemic virtues: “curiosity, intellectual autonomy, intellectual humility, attention, intellectual care, intellectual thoroughness, open-mindedness, intellectual courage, and intellectual tenacity ”. Regarding the analogy of Should You Believe Wikipedia? The answer is that it depends on the Wikipedia page, if its popular is arguably most accurate and if it’s less popular is less reliable since the content is a construction by social consensus. Finally, the message of the presentation is that this philosophy is useful to AI as we can better do our work as intelligent systems designers if we leverage our understanding of truth and knowledge.
Brain-Inspired Computation
Afterward came Dr. Nikola Kasabov with his presentation Brain-Inspired Computation for Intelligent Systems where the use of some principles of the human brain is argued to create artificial intelligence that is more efficient and transparent. The objective is to create brain-inspired methods that enable knowledge transfer between humans and machines, for example transferring our brain signals to a machine that interprets it in a natural way, for example, that understands that we want a cup of coffee. Hence, the authors introduce spike neural networks (SNN) or also called neuromorphic systems, and how these allow the extraction of knowledge from learned data and the monitoring of new data over time and space, specifically NeuCube an open-source and scalable architecture. These architectures allow for explainable knowledge transfer between humans and machines through building brain-inspired Brain-Computer Interfaces (BI-BCI), for example, visual signals are used to identify moving objects, obtaining a fast reaction of the system compared to other traditional neural networks.
General Overview
It was interesting to learn about completely new applications of AI. Particularly in terms of data science and deep learning, it was interesting to see new algorithms and advances in neural networks, and a good articulation to generate intelligent systems that facilitate daily life processes. Also, the conference had several presentations with philosophical approaches that allow us to reflect on the impact of AI and how we can improve practices to generate more reliable AI.
Overall the conference was an excellent experience and we look forward to returning to IntellySys 2022 to see incredible progress in AI methodologies and applications. Make sure to follow us, we will continue to post abstracts from different conferences and research!
References
Bruckman, A. (2022). Should You Believe Wikipedia?: Online Communities and the Construction of Knowledge. Cambridge: Cambridge University Press.
Kasabov, N. K. (2014). NeuCube: A spiking neural network architecture for mapping, learning, and understanding of Spatio-temporal brain data. Neural Networks, 52, 62–76.
Kapoor, S., & Bhatia, R. (2020). Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys).. Volume 3 (Vol. 2492). Springer Nature. https://link.springer.com/book/10.1007/978-3-030-82193-7
