Chapter 23

The Rise of Artificial Intelligence and Machine Learning

The decade 2010-2020 was characterized by an extraordinary acceleration in the field of artificial intelligence (AI) and machine learning (ML) . Thanks to a combination of technological and methodological factors, these disciplines made significant progress, moving from future promises to concrete realities with applications in various sectors.

23.1 Definition and Key Concepts:
Teaching Machines to Learn

Artificial intelligence (AI) is a field of computer science that deals with the design and development of systems capable of simulating human intelligence, such as the ability to learn, reason, solve problems and perceive the surrounding environment. machine learning (ML) is a subset of AI that focuses on developing algorithms that allow computers to learn from data without being explicitly programmed.

Some key concepts in machine learning include:

• Algorithms: Are sets of rules or instructions that a computer follows to solve a problem or perform a task. In ML, algorithms are designed to identify patterns and relationships in data.
• Data: They are the raw material of machine learning. Large amounts of data (often called “Big Data”) are used to “train” ML models.
• Training: It is the process through which an ML algorithm analyzes data and learns to make predictions or make decisions.
• Inference: It is the phase where the trained model is used to make predictions or decisions on new data that it has not seen during training.

There are different types of machine learning:

• Supervised Learning: The algorithm learns from labeled data, i.e. data in which the correct answer is already known. The goal is for the algorithm to learn to map inputs to the correct outputs.
• Unsupervised Learning: The algorithm learns from unlabeled data, looking for hidden patterns and structures in the data.
• Reinforcement Learning: The algorithm learns by interacting with an environment and receiving "rewards" or "punishments" based on its actions. The goal is for the algorithm to learn to make decisions that maximize reward over time.

23.2 The Key Factors of the Rise:
The Perfect Technological Storm

Several factors contributed to the rapid rise of AI and ML in the decade 2010-2020:

• Increased Computing Power (GPU): Advances in hardware, particularly in the development of increasingly powerful and efficient GPUs (Graphics Processing Units) , provided the computing power needed to train complex AI models, such as deep neural networks. GPUs, originally designed for video game graphics, turned out to be well-suited for the parallel computing operations required to train these models.

• Availability of Big Data: The explosion of data generated by the Internet, social media, sensors, mobile devices, and other sources provided the large amount of data needed to train effective machine learning models. Without sufficient data, even the most sophisticated algorithms struggle to learn meaningful patterns.
• Advances in Algorithms: Parallel to the increase in computing power and data availability, there were important advances in the development of AI and ML algorithms, particularly in the field of deep learning and neural networks .

23.3 Key Developments and Breakthroughs:
Milestones of Modern AI

The decade 2010–2020 was filled with key developments and discoveries that pushed the field of AI forward:

• Deep Learning and Neural Networks: deep learning is a branch of machine learning that uses neural networks with multiple layers (hence the term "deep") to analyze data. These networks are capable of learning complex representations of data, making them particularly effective for tasks such as image recognition, natural language processing, and speech recognition.
• Image Recognition: Image recognition capabilities made enormous progress thanks to deep learning. AI systems became able to identify objects, people and scenes in images with accuracy often exceeding that of humans. This led to applications such as facial recognition , object detection in video and technologies for autonomous driving .
• Natural Language Processing (NLP): Natural language processing (NLP) made great strides, allowing machines to understand, interpret and generate human speech. This led to significant improvements in machine translators , chatbots and virtual assistants .
• ​​ Voice Recognition: The accuracy of voice recognition systems improved significantly, making voice interactions with digital devices more fluid and reliable. This was fundamental to the success of virtual assistants and voice control systems.
• Reinforcement Learning: Reinforcement learning demonstrated its potential in areas such as gaming (e.g., DeepMind's AlphaGo program defeated the best human Go players) and robotics, where AI agents learned to perform optimal actions in an environment through trial and error and receiving feedback in the form of rewards.