Les 8/11 AI regulation deel 1
PPT ANDRIES Machine Learning with Neural Networks
1. Introduction to Neural Networks
Machine learning has recently made significant progress thanks to the use of artificial
neural networks (ANNs), often referred to as deep learning. Although neural
networks are not a new technology (initial ideas date back to the 1970s and 1980s), their
effectiveness has greatly improved due to increased computing power.
2. Basic Structure of a Neural Network
A neural network consists of:
• Input layer: Receives data as input (e.g., image pixels with RGB values).
• Hidden layers: Form complex connections between neurons where calculations
take place.
• Output layer: Provides a prediction or result (e.g., “dog” when the network
recognizes an image of a dog).
Each connection between neurons has a weight, indicating its importance. The network
learns by adjusting these weights to improve its predictions over time.
3. Learning Process of a Neural Network
The learning process is iterative:
1. Making a prediction: The network generates a prediction based on the current
weights.
2. Calculating error: The error is determined by comparing the prediction with the
actual outcome.
3. Adjusting weights: The weights are updated in the direction of the correct
outcome. This process is guided by a loss function, which minimizes the difference
between the prediction and the actual result.
4. Examples of Applications
• Image recognition: Identifying objects in images, such as cars, trees, or
pedestrians in the case of self-driving cars.
• Speech and music recognition: Generating music, such as creating new
compositions in the style of Beethoven or contemporary artists like Drake.
• Text processing: Answering questions or translating texts.
• Weather prediction: Forecasting weather patterns to better handle the effects of
climate change.
• Energy management: Making better predictions of energy production and
consumption, which can accelerate the transition to green energy.
5. Complexity and Scale of Networks
Although a simple network can already have many connections, modern neural networks
contain billions to trillions of connections. This allows them to learn very complex
patterns, such as the behavior of objects in real-life situations (e.g., a child walking
behind a building and reappearing later).
, 6. Importance and Future Perspectives
The potential of neural networks goes far beyond simple pattern recognition. They can
contribute to:
• Safety (self-driving cars)
• Environment (energy efficiency and weather forecasting)
• Art and entertainment (music and art creation)
Their ability to analyze vast amounts of data and learn from experiences offers
tremendous opportunities to solve societal and technological challenges.
1. Artificial Intelligence vs. Machine Learning
• Machine Learning (ML) is a subset of Artificial Intelligence (AI). The key
distinction is that AI has a purpose or goal, while ML focuses on recognizing patterns
and making predictions (e.g., identifying whether an object is a cat or a dog).
• AI encompasses more complex systems, like robots or virtual agents, which take
actions toward achieving a specific goal, such as making coffee.
• AI Agents are entities that interact with their environment and learn through
experiences, making decisions to maximize an objective. The goal is essential for an
agent to function—without a goal, the agent would not act.
2. The Role of Agents
• Definition of an Agent: An agent is an entity that takes actions within an
environment based on observations. These interactions, typically through trial and
error, help the agent achieve its goal or maximize its objective.
• Example: An agent tasked with making coffee may initially fail, but through trial
and error, it will learn the right actions to take and eventually complete the task.
• In AI, this objective is quantified with a score (e.g., +10 for successful coffee-
making and -10 for failure).
3. Superintelligence and AI in Games
• AI’s progress in games demonstrates superintelligence, where AI surpasses
human performance in specific tasks.
• Deep Blue (1996) is an example of AI defeating a world champion in chess. It
showcased AI’s capability in a strategy-based, rule-bound environment.
• AlphaGo (2016), developed by Google DeepMind, defeated the 18-time world
champion of Go, a much more complex game than chess, both mathematically and
intuitively. The number of possible game states in Go exceeds the number of atoms in
the universe, making it impossible for traditional AI to calculate the optimal move.
Instead, AlphaGo learned to play intuitively, surprising experts with innovative
strategies like Move 37, which was considered an unorthodox approach by human
players.
4. Creativity and Innovation in AI
• AI has the potential to introduce creativity beyond human capabilities. Just like
Einstein revolutionized science or quantum mechanics were discovered unexpectedly,
AI could provide new insights and strategies that humans might never conceive.
PPT ANDRIES Machine Learning with Neural Networks
1. Introduction to Neural Networks
Machine learning has recently made significant progress thanks to the use of artificial
neural networks (ANNs), often referred to as deep learning. Although neural
networks are not a new technology (initial ideas date back to the 1970s and 1980s), their
effectiveness has greatly improved due to increased computing power.
2. Basic Structure of a Neural Network
A neural network consists of:
• Input layer: Receives data as input (e.g., image pixels with RGB values).
• Hidden layers: Form complex connections between neurons where calculations
take place.
• Output layer: Provides a prediction or result (e.g., “dog” when the network
recognizes an image of a dog).
Each connection between neurons has a weight, indicating its importance. The network
learns by adjusting these weights to improve its predictions over time.
3. Learning Process of a Neural Network
The learning process is iterative:
1. Making a prediction: The network generates a prediction based on the current
weights.
2. Calculating error: The error is determined by comparing the prediction with the
actual outcome.
3. Adjusting weights: The weights are updated in the direction of the correct
outcome. This process is guided by a loss function, which minimizes the difference
between the prediction and the actual result.
4. Examples of Applications
• Image recognition: Identifying objects in images, such as cars, trees, or
pedestrians in the case of self-driving cars.
• Speech and music recognition: Generating music, such as creating new
compositions in the style of Beethoven or contemporary artists like Drake.
• Text processing: Answering questions or translating texts.
• Weather prediction: Forecasting weather patterns to better handle the effects of
climate change.
• Energy management: Making better predictions of energy production and
consumption, which can accelerate the transition to green energy.
5. Complexity and Scale of Networks
Although a simple network can already have many connections, modern neural networks
contain billions to trillions of connections. This allows them to learn very complex
patterns, such as the behavior of objects in real-life situations (e.g., a child walking
behind a building and reappearing later).
, 6. Importance and Future Perspectives
The potential of neural networks goes far beyond simple pattern recognition. They can
contribute to:
• Safety (self-driving cars)
• Environment (energy efficiency and weather forecasting)
• Art and entertainment (music and art creation)
Their ability to analyze vast amounts of data and learn from experiences offers
tremendous opportunities to solve societal and technological challenges.
1. Artificial Intelligence vs. Machine Learning
• Machine Learning (ML) is a subset of Artificial Intelligence (AI). The key
distinction is that AI has a purpose or goal, while ML focuses on recognizing patterns
and making predictions (e.g., identifying whether an object is a cat or a dog).
• AI encompasses more complex systems, like robots or virtual agents, which take
actions toward achieving a specific goal, such as making coffee.
• AI Agents are entities that interact with their environment and learn through
experiences, making decisions to maximize an objective. The goal is essential for an
agent to function—without a goal, the agent would not act.
2. The Role of Agents
• Definition of an Agent: An agent is an entity that takes actions within an
environment based on observations. These interactions, typically through trial and
error, help the agent achieve its goal or maximize its objective.
• Example: An agent tasked with making coffee may initially fail, but through trial
and error, it will learn the right actions to take and eventually complete the task.
• In AI, this objective is quantified with a score (e.g., +10 for successful coffee-
making and -10 for failure).
3. Superintelligence and AI in Games
• AI’s progress in games demonstrates superintelligence, where AI surpasses
human performance in specific tasks.
• Deep Blue (1996) is an example of AI defeating a world champion in chess. It
showcased AI’s capability in a strategy-based, rule-bound environment.
• AlphaGo (2016), developed by Google DeepMind, defeated the 18-time world
champion of Go, a much more complex game than chess, both mathematically and
intuitively. The number of possible game states in Go exceeds the number of atoms in
the universe, making it impossible for traditional AI to calculate the optimal move.
Instead, AlphaGo learned to play intuitively, surprising experts with innovative
strategies like Move 37, which was considered an unorthodox approach by human
players.
4. Creativity and Innovation in AI
• AI has the potential to introduce creativity beyond human capabilities. Just like
Einstein revolutionized science or quantum mechanics were discovered unexpectedly,
AI could provide new insights and strategies that humans might never conceive.
