7 A.I. Workers for WLB

Introduction Reinforcement Learning (RL) is a paradigm of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards . Unlike supervised learning, which learns from labeled examples, RL relies on trial-and-error: the agent explores actions and gradually learns a policy that maximizes cumulative reward. This framework, formalized in the context of Markov Decision Processes (MDPs), involves the agent observing a state, taking an action, and then transitioning to a new state while receiving a reward signal. Over time, the agent aims to learn an optimal policy (action strategy) that yields the highest long-term reward ( Reinforcement learning - Wikipedia ). Problems suited to RL often involve a trade-off between short-term and long-term rewards; an agent may need to sacrifice immediate payoff for a bigger future gain. RL has been applied successfully to a wide range of problems, from robot control and board...

Deep learning represents one of the most transformative advancements in the field of artificial intelligence (AI) and machine learning. By simulating complex neural structures in the human brain, deep learning has revolutionized how computers recognize images, process language, and perform tasks that were previously thought too complex for machines. Early Foundations of Deep Learning (1980s–1990s) While the foundational concepts of neural networks date back to the 1940s and 1950s, it wasn't until the mid-1980s that significant progress was made. The breakthrough came in 1986 when David Rumelhart, Geoffrey Hinton, and Ronald Williams introduced the backpropagation algorithm. This method allowed efficient training of neural networks with multiple layers, known as multi-layer perceptrons, enabling them to learn more complex patterns from data. Despite early enthusiasm, neural network research stagnated in the 1990s due to computational limitations and competition from alternative ...

The emergence of machine learning (ML) as a central subfield of artificial intelligence (AI) marks a significant turning point in the history of computer science. This transition reflects a broader shift from rule-based symbolic systems toward data-driven statistical learning, enabling machines to identify patterns, adapt behavior, and improve performance over time. The development of ML can be traced through three major phases: the deep learning revolution, advancements in reinforcement learning, and the proliferation of AI applications in the 21st century. Deep Learning Revolution The 2000s and early 2010s saw a surge in interest in artificial neural networks, particularly deep learning, which refers to training large neural networks with multiple hidden layers. This resurgence was driven by a convergence of three factors: the availability of large datasets (Big Data), the exponential increase in computing power (especially GPUs), and algorithmic innovations like ReLU activation f...

Machine learning has evolved from simple theoretical beginnings into a driving force of modern technology. Over the decades, progress in algorithm design, computational power, and data availability has enabled computers to learn from experience and improve at tasks without explicit programming. The following sections outline key developments in machine learning from its early conceptual foundation in the 1940s to the sophisticated techniques and applications of the 2020s. Early Foundations (1940s–1950s) In the 1940s, researchers began laying the groundwork for machine learning by drawing inspiration from biology and mathematics. In 1943, Warren McCulloch and Walter Pitts introduced a mathematical model of an artificial neuron, suggesting that networks of these simple units could mimic logical thought processes. This idea established the principle that complex behaviors might emerge from many interconnected simple elements, a foundational concept for neural networks. A few years later...