160
Chapter 11: Artificial Intelligence
There are roughly two different techniques: reason-based AI, which reasons based on symbols or rules
programmed in the system. Instinctively, the reader can sense that the human has a bit more control of the
rules. A second technique is learning-based AI. These are systems that use large amounts of data to construct
a mathematical model to recognize correlations. This auto-construction can be done in a supervised or
unsupervised manner. Supervised means that humans determine the categories of data themselves, e.g., these
are cats these are dogs, now learn to recognize them. Unsupervised, and deep learning, a sub-category of
machine learning that belongs to this, is not given any labels. The developer throws data into the system, and
by itself, it recognizes patterns and categories in the data. The big advantage is that the system recognizes
things that a human might not have thought about. On the other hand, it might also generate categories that
are problematic from a moral point of view. When people talk about black box systems, they predominantly talk
about deep learning. So, it is incorrect that all AI is not transparent it is mainly this category where it is difficult
to determine how a decision was made. This is not the case for all AI systems. Sometimes, basic algorithms and
statistics are also called AI. Robotics, also known as ‘embodied AI,’ is shown to be half in the box, half out of the
box. Robots can work using AI but also can operate using simpler algorithms. The drawing overlaps all these
types of AI because there are also hybrid forms of AI.
Basic
algorithm
Advanced
statistics
Simplified
Reasoning-based AI
(symbolic/classic)
Learning-based AI
(machine learning)
Deep
learning
Robotics
(hardware)
AI
interpretation issues in a legal context.17 Regard-
ing AI’s intelligent nature, no scientific consensus
on the meaning of ‘intelligence’ exists.18 As society
tends to strip older AI techniques of their ‘intel-
ligent’ status, the term poses a large moving edge.
In terms of learning ability, learning is generally
understood as ‘acquiring knowledge or new skills.’
If learning knowledge is sufficient, many digital
products qualify as AI as soon as they acquire
input data. If learning a new skill is necessary, the
meaning of the term narrows significantly.
Today, it is uncertain what is and what is not
covered by the term AI. Therefore, this chapter
focuses on characteristics commonly associated
with AI and their regulatory implications.
Machine Learning
A characteristic of machine learning devices
is that they can change based on training data
(samples to fit a machine learning model),
without being programmed explicitly. In contrast,
other AI technologies learn without training
data, such as through genetic programming19
or reasoning. For example, semantic computing
learns through semantic networks (a knowledge
base that represents semantic relations between
concepts in a network). Through reason, deduc-
tion, and inference, the AI may evolve or adapt
during use. There are different perspectives to the
aspect of change.
Global Versus Local Change
During global change, the manufacturer or
health institution trains a machine learning
model that is part of a device, i.e., ‘the global
model.’ After the validation and conformity
assessment, if applicable, the device is deployed
Figure 11-1. A Simplified Diagram of Different AI Techniques
Chapter 11: Artificial Intelligence
There are roughly two different techniques: reason-based AI, which reasons based on symbols or rules
programmed in the system. Instinctively, the reader can sense that the human has a bit more control of the
rules. A second technique is learning-based AI. These are systems that use large amounts of data to construct
a mathematical model to recognize correlations. This auto-construction can be done in a supervised or
unsupervised manner. Supervised means that humans determine the categories of data themselves, e.g., these
are cats these are dogs, now learn to recognize them. Unsupervised, and deep learning, a sub-category of
machine learning that belongs to this, is not given any labels. The developer throws data into the system, and
by itself, it recognizes patterns and categories in the data. The big advantage is that the system recognizes
things that a human might not have thought about. On the other hand, it might also generate categories that
are problematic from a moral point of view. When people talk about black box systems, they predominantly talk
about deep learning. So, it is incorrect that all AI is not transparent it is mainly this category where it is difficult
to determine how a decision was made. This is not the case for all AI systems. Sometimes, basic algorithms and
statistics are also called AI. Robotics, also known as ‘embodied AI,’ is shown to be half in the box, half out of the
box. Robots can work using AI but also can operate using simpler algorithms. The drawing overlaps all these
types of AI because there are also hybrid forms of AI.
Basic
algorithm
Advanced
statistics
Simplified
Reasoning-based AI
(symbolic/classic)
Learning-based AI
(machine learning)
Deep
learning
Robotics
(hardware)
AI
interpretation issues in a legal context.17 Regard-
ing AI’s intelligent nature, no scientific consensus
on the meaning of ‘intelligence’ exists.18 As society
tends to strip older AI techniques of their ‘intel-
ligent’ status, the term poses a large moving edge.
In terms of learning ability, learning is generally
understood as ‘acquiring knowledge or new skills.’
If learning knowledge is sufficient, many digital
products qualify as AI as soon as they acquire
input data. If learning a new skill is necessary, the
meaning of the term narrows significantly.
Today, it is uncertain what is and what is not
covered by the term AI. Therefore, this chapter
focuses on characteristics commonly associated
with AI and their regulatory implications.
Machine Learning
A characteristic of machine learning devices
is that they can change based on training data
(samples to fit a machine learning model),
without being programmed explicitly. In contrast,
other AI technologies learn without training
data, such as through genetic programming19
or reasoning. For example, semantic computing
learns through semantic networks (a knowledge
base that represents semantic relations between
concepts in a network). Through reason, deduc-
tion, and inference, the AI may evolve or adapt
during use. There are different perspectives to the
aspect of change.
Global Versus Local Change
During global change, the manufacturer or
health institution trains a machine learning
model that is part of a device, i.e., ‘the global
model.’ After the validation and conformity
assessment, if applicable, the device is deployed
Figure 11-1. A Simplified Diagram of Different AI Techniques