FlyAI -- The Next Level of Artificial Intelligence is Unpredictable! Injecting Responses of a Living Fly into Decision Making
September 30, 2024 ยท Declared Dead ยท ๐ International Workshop on Sensor-based Activity Recognition and Interaction
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Denys J. C. Matthies, Ruben Schlonsak, Hanzhi Zhuang, Rui Song
arXiv ID
2410.12808
Category
cs.NE: Neural & Evolutionary
Citations
0
Venue
International Workshop on Sensor-based Activity Recognition and Interaction
Last Checked
4 months ago
Abstract
In this paper, we introduce a new type of bionic AI that enhances decision-making unpredictability by incorporating responses from a living fly. Traditional AI systems, while reliable and predictable, lack nuanced and sometimes unseasoned decision-making seen in humans. Our approach uses a fly's varied reactions, to tune an AI agent in the game of Gobang. Through a study, we compare the performances of different strategies on altering AI agents and found a bionic AI agent to outperform human as well as conventional and white-noise enhanced AI agents. We contribute a new methodology for creating a bionic random function and strategies to enhance conventional AI agents ultimately improving unpredictability.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Neural & Evolutionary
๐ฎ
๐ฎ
The Ethereal
R.I.P.
๐ป
Ghosted
Deep Learning using Rectified Linear Units (ReLU)
R.I.P.
๐ป
Ghosted
Generative Adversarial Text to Image Synthesis
R.I.P.
๐ป
Ghosted
Regularized Evolution for Image Classifier Architecture Search
R.I.P.
๐ป
Ghosted
Temporal Ensembling for Semi-Supervised Learning
๐
๐
Old Age
Learning Structured Sparsity in Deep Neural Networks
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted