What Is Composite AI?
Composite AI, also known as multidisciplinary AI, is an approach to artificial intelligence (AI) that combines multiple AI techniques and technologies to create a more comprehensive and capable system. It essentially means using different strengths of various AI tools to address complex problems that a single technique might not be able to handle effectively.
How Can Different AI Techniques Be Combined?
There are several strategies for combining different AI techniques to create a composite AI system. The technique chosen for combining AI techniques depends on the problem and the desired outcome. The following are some of the ways in which a composite AI system can be created:
- Ensemble Learning: This approach involves training multiple individual AI models, each using different algorithms or techniques, on the same data. The predictions from these individual models are combined, often through averaging or voting, to produce a final output. This leverages the strengths of each model while mitigating their weaknesses.
- Pipelines: Here, different AI techniques are placed sequentially, where the output from one stage becomes the input for the next. For example, a system might use computer vision to identify objects in an image, followed by natural language processing (NLP) to generate a description of the scene.
- Hybrid Approaches: It involves integrating different AI techniques within a single model architecture. For instance, a model might combine convolutional neural networks for image recognition with recurrent neural networks for language processing to create a system that can understand and respond to visual questions.
- Multi-Agent Systems: This approach involves creating multiple, independent AI agents that can interact with each other and their environment to achieve a common goal. This can be particularly useful for complex tasks requiring collaboration and coordination between different AI entities.
What Are The Applications Of Composite AI?
Composite AI finds application in various domains, offering solutions that individual AI techniques might struggle with. Here are some examples:
- Personalised Medicine: In healthcare, composite AI can combine:
- Machine learning for analysing vast amounts of medical data like patient records and clinical trials.
- NLP to extract insights from medical literature and patient narratives.
- Knowledge graphs to capture relationships between diseases, symptoms, and treatments.
- This combination allows for:
- Personalised Treatment Plans: By analysing diverse data sources, AI can tailor treatments to individual patients, improving outcomes.
- Predictive Modelling: AI can predict potential health risks or disease progression, enabling preventive measures.
- Drug Discovery: Analysing vast datasets can help identify potential drug targets and accelerate the development of new treatments.
- Fraud Detection: Financial institutions leverage composite AI to combat fraudulent activities by combining:
- Machine learning algorithms to analyse transaction patterns and identify anomalies.
- NLP to detect suspicious language in emails or communication channels.
- Knowledge graphs to map relationships between individuals, entities, and transactions and identify fraudulent networks.
- This combined approach improves:
- Fraud Detection Accuracy: By analysing various data points, AI can identify complex fraud schemes that might go unnoticed with a single technique.
- Financial Losses: Early detection and prevention of fraudulent activities save financial institutions significant resources.
- Autonomous Vehicles: Self-driving cars rely heavily on composite AI, integrating various techniques like:
- Computer vision for real-time obstacle detection and path planning.
- Sensor fusion to combine data from cameras, LiDAR, and radar for a comprehensive understanding of the environment.
- Machine learning for decision making and control of the vehicle’s movement.
- This combination enables:
- Safer Navigation: By processing information from various sources, the car can react to complex situations and avoid collisions.
- Improved Driving Experience: AI can handle monotonous tasks like lane keeping and traffic light recognition, making driving more relaxing for passengers.
What Are The Advantages & Disadvantages Of Composite AI?
Advantages Of Composite AI
- Enhanced Problem Solving: By combining the strengths of AI techniques, composite AI can tackle complex problems that individual approaches might struggle with. This leads to more comprehensive and robust solutions.
- Improved Decision Making: By leveraging data and insights from different AI tools, composite AI systems can gain a deeper understanding of situations, leading to better-informed and more accurate decisions.
- Increased Adaptability: Composite AI systems can be more adaptable to changing environments and situations. Different AI techniques within the system can be adjusted or updated independently, allowing for continuous improvement and flexibility.
- Reduced Bias: Combining different AI techniques with diverse biases can help mitigate the overall bias present in individual models. This leads to more ethical AI solutions.
Disadvantages Of Composite AI
- Increased Complexity: Designing, developing, and maintaining composite AI systems can be more complex compared to single-technique approaches. It requires expertise in various AI domains and careful integration of different components.
- Higher Computational Resources: Combining multiple AI techniques often requires significant computational resources, such as more processing power and storage capacity.
- Potential For Unintended Consequences: Combining multiple AI models with complex interactions can lead to unintended consequences that are difficult to predict or troubleshoot.
