The European Commission recently issued a formula for identifying Artificial Intelligence Systems:
Machine-based system
Designed to operate with varying levels of autonomy
- Some degree of independence of actions from human involvement
- Some inference capacity
- BUT broad: Includes a system that requires manually provided inputs to generate an output by itself
That may exhibit adaptiveness after deployment
- Self-learning capabilities, allowing the behavior of the system to change while in use
- BUT only “may”
And that, for explicit or implicit objectives
- Implicit objectives are deduced from the behavior or underlying assumptions of the system
- Objectives may be different from the intended purpose (Purpose is externally oriented; objectives are internal).
Infers, from the input it receives, how to generate outputs
- Includes:
- Deriving outputs through AI techniques enabling inferencing e.g.: machine learning approaches, and logic- and knowledge-based approaches
- Machine learning approaches
- Supervised learning: Learn from labeled data e.g email spam detection
- Unsupervised learning: Learn from unlabeled labeled data. e.g. AI systems used for drug discovery by pharmaceutical companies
- Self-supervised learning: learn from unlabeled data in a supervised fashion, using the data itself to create labels. e.g. learn to predict the next token in a sentence
- Reinforcement learning: Learn from data collected from own experience through a ‘reward’ function. e.g. personalised content recommendations in search engines
- Deep learning: Utilize layered architectures (neural networks) for representation learning.
- Logic- and knowledge-based approaches: E.g. early generation expert systems intended for medical diagnosis
- Excludes:
- Automatically execute based on rules defined solely by natural persons
- E.g. satellite telecommunication system to optimize bandwidth allocation and resource management
- Basic data processing
- Systems based on classical heuristics: E.g. a chess program assessing board positions
- Simple prediction systems: E.g. using the average temperature of last week for predicting tomorrow’s temperature
Such as predictions, content, recommendations, or decisions
- Predictions: E.g.: AI systems deployed in self-driving cars are designed to make real-time predictions in an extremely complex and dynamic environment
- Content: E.g. text, images, videos, music and other forms of output.
- Recommendations: E.g. candidate to hire in a recruitment system
- Decisions: Conclusions or choices made by a system
That can influence physical or virtual environments
- The influence of an AI system may be both to tangible, physical objects (e.g. robot arm) and to virtual environments, including digital spaces, data flows, and software ecosystems.
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