To investigate whether “Intelligence is the capacity of an information-processing system to adapt to its environment while operating with insufficient knowledge and resources", we look at utilising the non axiomatic reasoning system (NARS) for speech recognition. This article presents NUTS: raNdom dimensionality redUction non axiomaTic reasoning few Shot learner for perception. NUTS consists of naive dimensionaility reduction, some pre-processing, and then non axiomatic reasoning (NARS). With only 2 training examples per class, NUTS achieves 64% accuracy, with 20 examples per class we achieve 90% accuracy, on the 35 word standard command dataset.
Paper: https://link.springer.com/chapter/10.1007/978-3-031-33469-6_31
With 2 Examples per class, we achive 64% accuracy, at 20 examples per class we achieve 90% accuracy (embedding space dimensions = 5).
This is achieved with a low dimensional embedding space (5 dimensions), which reduces compute (examples per class = 4).
Confusion matrix (embedding space dimensions = 5, examples per class = 4)
cd ~/projects git clone https://github.com/dwanev/NUTS
- NUTS uses open NARS for applications (ONA). Which can be found here: https://github.com/opennars/OpenNARS-for-Applications. ONA is installations instructions can be found on the webpage above, but in brief:
cd ~/projects
git clone https://github.com/opennars/OpenNARS-for-Applications
cd OpenNARS-for-Applications
./build.sh
- Set up an environment variable so the NUTS can find ONE
export ONA_PATH_TO_NAR=~/projects/OpenNARS-for-Applications
conda create -n nuts python=3.9 conda activate nuts
cd ~/projects/NUTS
conda activate nuts
pip install -r requirements.txt
python3 main_confusion_matrix_performance.py
python3 main_experiment_vary_AIKR_limit.py
python3 main_experiment_vary_dimensions.py
python3 main_experiment_vary_examples_per_class.py
cd projects/OpenNARS-for-Applications
conda activate open_nars
./NAR shell
If you find this useful please cite:
@InProceedings{10.1007/978-3-031-33469-6_31,
author="van der Sluis, Dwane",
editor="Hammer, Patrick
and Alirezaie, Marjan
and Stranneg{\aa}rd, Claes",
title="NUTS, NARS, and Speech",
booktitle="Artificial General Intelligence",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="307--316",
abstract="To investigate whether Intelligence is the capacity of an information-processing system to adapt to its environment while operating with insufficient knowledge and resources [29], we look at utilising the non axiomatic reasoning system (NARS) for speech recognition. This article presents NUTS: raNdom dimensionality redUction non axiomaTic reasoning few Shot learner for perception. NUTS consists of naive dimensionaility reduction, some pre-processing, and then non axiomatic reasoning (NARS). With only 2 training examples NUTS performs similarly to the Whisper Tiny model for discrete word identification.",
isbn="978-3-031-33469-6"
}