Our Current/Future Applied AI Research Projects

AI-Driven Precision Poultry Farming

Objective: Develop an AI system for optimizing poultry health, welfare, and productivity in large-scale chicken farms.

Applied Research Components:

• Computer Vision: Implementing advanced image recognition algorithms to monitor chicken behavior, detect health issues, and assess overall flock well-being.

• Machine Learning: Creating predictive models for feed optimization, disease outbreak prevention, and egg production forecasting based on historical and real-time data.

• IoT Integration: Designing systems to collect and process data from coop sensors for comprehensive environmental monitoring (temperature, humidity, air quality).

• Audio Analysis: Developing AI models to interpret chicken vocalizations for early stress or health problem detection.

Real-World Impact: This project aims to increase egg production efficiency by 15%, reduce mortality rates by 25%, and improve overall chicken welfare scores by 30%, promoting sustainable and ethical poultry farming practices.

AI-Driven Precision Agriculture

Objective: Develop an AI system for early detection and management of crop diseases using drone imagery and sensor data.

Applied Research Components:

• Computer Vision: Developing advanced image recognition algorithms to identify early signs of crop stress and disease.

• Machine Learning: Creating predictive models for disease spread based on historical and real-time data.

• IoT Integration: Designing systems to collect and process data from field sensors for comprehensive crop monitoring.

Real-World Impact: This project aims to increase crop yields by up to 20% and reduce pesticide use by 30%, promoting sustainable agriculture practices.

Computer Vision for Anomaly Detection in Radiology

Objective: Create an AI-powered system for detecting anomalies in various types of medical imaging data, enhancing radiologists' diagnostic capabilities.

Applied Research Components:

• Deep Learning: Developing and training advanced neural network architectures (e.g., U-Net, ResNet) on large datasets of annotated medical images.

• Transfer Learning: Adapting pre-trained models to specific types of medical imaging (X-rays, CT scans, MRIs) for efficient learning with limited datasets.

• Explainable AI: Implementing techniques to provide interpretable results, highlighting areas of concern and explaining the AI's decision-making process to medical professionals.

• Federated Learning: Developing methods to train models across multiple healthcare institutions without compromising patient data privacy.

Real-World Impact: It is anticipated that 22% increase in early detection of subtle anomalies, a 15% reduction in false positives, and a 30% decrease in the time required for radiologists to review complex cases.

AI Hallucination and Risk Detection Framework

Objective: Develop a comprehensive framework for detecting, quantifying, and mitigating hallucinations and other risks in large language models and multimodal AI systems.

Key Innovations:

• Creating novel uncertainty quantification methods for deep learning models to identify potential hallucinations.

• Developing adversarial testing techniques to probe AI systems for vulnerabilities and unexpected behaviors.

• Implementing real-time monitoring systems to detect and flag potential AI hallucinations or risky outputs during deployment.

• Designing interpretable AI models that provide clear reasoning paths, making it easier to identify and correct faulty logic.

• Creating a taxonomy of AI risks and hallucinations to standardize detection and reporting across different AI systems.

Applied Research Components:

• Ensemble Methods: Utilizing diverse model ensembles to cross-validate outputs and identify inconsistencies indicative of hallucinations.

• Knowledge Graph Integration: Incorporating external knowledge bases to fact-check AI outputs and detect potential misinformation.

• Multimodal Consistency Checking: Developing techniques to ensure consistency across different modalities (text, image, audio) in AI outputs.

• Prompt Engineering: Creating robust prompting strategies to minimize the risk of hallucinations in large language models.

• Human-AI Collaboration: Designing interfaces and workflows that effectively combine human expertise with AI capabilities to catch and correct errors.

Potential Impact: This research could significantly enhance the reliability and trustworthiness of AI systems in critical applications such as healthcare, finance, and autonomous systems. It aims to reduce the rate of AI hallucinations by 90% in controlled settings and provide a standardized framework for ongoing risk assessment in AI deployments.

Neuromorphic Computing for Edge AI (Future work)

Objective: Design and implement brain-inspired computing architectures for ultra-efficient AI processing on edge devices.

Key Innovations:

• Developing spiking neural network models that closely mimic biological neural systems.

• Creating energy-efficient hardware designs inspired by neuronal and synaptic dynamics.

• Implementing novel learning algorithms adapted for neuromorphic architectures.

Potential Impact: This research aims to enable AI capabilities on low-power edge devices with 100x improved energy efficiency, revolutionizing IoT, wearable tech, and autonomous systems.

Self-Supervised Multimodal AI (Future work)

Objective: Develop advanced AI models capable of learning from and integrating multiple data modalities (text, image, audio, video) without extensive labeled datasets.

Key Innovations:

• Creating novel self-supervised learning techniques for cross-modal knowledge transfer.

• Developing attention mechanisms for efficient integration of information across different modalities.

• Implementing contrastive learning approaches for robust representation learning from unlabeled multimodal data.

Potential Impact: This research could lead to more adaptable and generalizable AI systems, reducing the need for large labeled datasets and enabling new applications in areas like automated content understanding and creation.