Benchmarking Arabic AI with Large Language Models

With large Foundation Models (FMs), language technologies (AI in general) are entering a new paradigm: eliminating the need for developing large-scale task-specific datasets and supporting a variety of tasks through set-ups ranging from zero-shot to few-shot learning. However, understanding FMs capabilities requires a systematic benchmarking effort by comparing FMs performance with the state-of-the-art (SOTA) task-specific models. With that goal, past work focused on the English language and included a few efforts with multiple languages. Our study contributes to ongoing research by evaluating FMs performance for standard Arabic NLP and Speech processing, including a range of tasks from sequence tagging to content classification across diverse domains. We start with zero-shot learning using GPT-3.5-turbo, Whisper, and USM, addressing 33 unique tasks using 59 publicly available datasets resulting in 96 test setups. For a few tasks, FMs performs on par or exceeds the performance of the SOTA models but for the majority it under-performs. Given the importance of prompt for the FMs performance, we discuss our prompt strategies in detail and elaborate on our findings. Our future work on Arabic AI will explore few-shot prompting, expand the range of tasks, and investigate additional open-source models.Comment: Foundation Models, Large Language Models, Arabic NLP, Arabic Speech, Arabic AI, , CHatGPT Evaluation, USM Evaluation, Whisper Evaluatio

AirEye: UAV-Based Intelligent DRL Mobile Target Visitation

From traffic monitoring to livestock tracking, and military reconnaissance to marine discovery, unmanned aerial vehicles (UAVs) are indispensable. Its dependence on a battery for power supply limits the flight time to visit all planned locations. Consequently, target visitation needs to be smart and minimize the mechanical energy. We propose to develop the AirEye UAV-based smart platform that can perform target visitation in the shortest time possible without knowing targets' exact locations, but with a known probabilistic distribution. We show how to integrate a UAV with the proper hardware to control it and execute commands from an on-ground command and control station. A pre-built machine learning model was modified to detect and identify targets, along with a reinforcement learning (RL) model to autonomously navigate the drone and ensure that all targets are visited while consuming minimal energy. We propose a drone energy model that can be used to estimate the total energy consumed by the drone in a complex scenario. We then use this energy model to compare the total energy consumed by the proposed RL-based technique in comparison with two other heuristic strategies, namely, random, and zigzag motion

AI-based UAV navigation framework with digital twin technology for mobile target visitation

Unmanned Air Vehicles (UAVs), i.e. drones, have become a key enabler technology of many reconnaissance applications in different fields, such as military, maritime, and transportation. UAVs offer several benefits, such as affordability and flexibility in deployment. However, their limited flight time due to energy consumption is one of the key limitations. Therefore, it is crucial to ensure that UAVs can complete the mission while consuming the least energy possible. In this paper, we propose a novel framework for UAV smart navigation to minimize the time and energy of planning mobile targets visitation. We develop a Deep Reinforcement Learning (DRL) approach to allow the drone to learn the targets' mobility pattern and build its least energy scanning strategy accordingly. We conduct an initial evaluation of the system and our proposed DRL model policy using simulation. Then, to overcome the time-consuming exploration phase of DRL, we develop a Digital Twin (DT) environment of 3D physics-based simulator, which can be used to train the DRL agent efficiently. We also developed a testbed based on hardware integration with the parrot ANAFI drone to verify the feasibility of the proposed methodology. Our findings confirm that the DRL-based agent can achieve performance close to that of a benchmark policy. Moreover, the testbed experiment validates the practicality of utilizing the DT environment for DRL exploration. 2023 Elsevier LtdThis publication was supported by Industrial Grant No. QUEX-CENG-SPC-2023 . The findings achieved herein are solely the responsibility of the authors.Scopu