Collaborative Intelligence
Research work on the notion of Human - AI Collaborative Intelligence.
Human - AI Collaborative Intelligence Basics
Collaborative Intelligence (CI) is a human-machine partnership that combines the strengths of both humans and AI to achieve better results than either could on their own. CI systems are designed to be co-creative, meaning humans, and AI work together to solve problems and make decisions. On the other hand, Industry 5.0 is a new industrial revolution focused on human-centric manufacturing. In this new era, AI can augment human capabilities rather than replace them. CI is essential for Industry 5.0 because it allows humans and AI to work together effectively.
Human - AI Interaction Paradigms
In classic AI systems, the human operator (often a data scientist) is involved in targeted steps of the AI lifecycle to perform specific tasks:
- Data labelling: People often label data for ML models. This is especially important for tasks that are difficult or time-consuming for machines, such as identifying objects in images or extracting text from documents.
- Model training: People can help to train ML models. This can be done by providing feedback to the model during training or by manually adjusting the model's parameters.
- Model deployment: People can also deploy ML models in the real world. This can involve monitoring the model's performance and adjusting as needed.
The above can be considered a basic paradigm of CI. But CI for Industry 5.0 goes beyond data labelling and model deployment, fostering actual human -AI collaboration in hybrid teams, to achieve:
- Machine learning with human feedback: This is a type of CI where humans provide feedback to AI systems to help them learn and improve. For example, humans might be asked to label data or correct errors in the AI system's output. The clearest example of this category is the HITL solution.
- Co-creation: This type of collaborative intelligence is where humans and AI systems work together to create new products or services. For example, humans might provide AI systems with their ideas and feedback, while AI systems provide humans with access to new data and insights. The clearest example of this category would be the emerging Large Language Models (LLMs)
- Augmented intelligence: This type of CI uses AI systems to augment human capabilities. For example, AI systems might provide real-time assistance to doctors, help pilots fly planes or any other related activity.
Several advantages are associated with successfully integrating the CI paradigm in current practices, such as:
- Improved Accuracy: Human expertise can help correct AI mistakes and enhancing overall system performance.
- Ethical Decision-making: Humans can provide context, empathy, and ethical judgment in complex situations where AI alone may struggle.
- Adaptability and Flexibility: Human in the Loop (HITL) allows AI systems to learn and adapt quickly, incorporating new information and addressing unforeseen scenarios.
- Data Quality Assurance: Humans can validate and curate datasets, ensuring high-quality inputs for AI training and avoiding biases.
Challenges of Collaborative Intelligence
There are still some important challenges to overcome in the context of a CI system and in hybrid human-AI teaming environments:
- Cost and Scalability: Integrating humans can increase operational expenses, and scalability may become challenging.
- Training and Expertise: Ensuring human contributors have the necessary training and expertise to provide accurate input to AI systems.
- Workflow Integration: Establishing seamless workflows between humans and AI systems, minimising delays and bottlenecks.
- Balancing Human-AI Interaction: Striking the right balance between human involvement and AI automation, optimising system performance.
Tools and Solutions
| Tool Name | Purpose | Description | Link |
|---|---|---|---|
| Orchestra | Example | Example | Example |
| Teaming AI | Example | Example | Example |
Other Info
- Research by Software Competence Centre Hagenberg - SCCH