* AI Summit Sees Reinforcement Learning Take Flight, from Turing to DeepSeek
Rewritten Article:
Called the "Nobel Prize of Computing," the Turing Award recently went to Andrew Barto and Richard Sutton for advancing the field of reinforcement learning, guiding machines to learn from rewards and experiences. This concept has roots in childhood learning as proposed by Alan Turing, the father of theoretical computer science.
Back in the 20th century, Turing championed the idea that all mental operations could be computed, and that machines could learn like children - through a system of rewards and punishments. Barto and Sutton have built upon these ideas, creating algorithms that enable machines to learn effectively through trial-and-error and delayed rewards – gaining rewards not instantly but over a series of actions.
Reinforcement learning, one of three main machine learning paradigms, is distinct from supervised and unsupervised learning. In reinforcement learning, computers don’t have predefined instructions but instead discover which actions produce the most reward by experimenting. Sutton and Barto explain this in their book "Reinforcement Learning: An Introduction."
Supervised learning involves teaching a computer program to identify the correct example from the examples it's shown. However, obtaining examples for all situations in new, uncharted territory is often impractical – hence the need for reinforcement learning.
Unsupervised learning, lacking labeled examples, is excellent at finding patterns and relationships between data. But it doesn't solve the reinforcement learning problem of maximizing a reward. Sutton and Barto consider reinforcement learning a third, separate machine learning paradigm.
Machine learning phenomenon Arthur Samuel coined the term "machine learning" in 1959. Teaching a computer to play checkers, Samuel laid the groundwork for reinforcement learning. But computational power limitations held back further progress back then.
The marriage of GPU-powered artificial neural networks (deep learning) with reinforcement learning algorithms developed by Barto, Sutton and others brought major strides to practical reinforcement learning applications in the last 15 years. Google's DeepMind showcased this combination with its AlphaGo program, which beat human Go champions in 2016 and 2017.
In 2022, AI pioneer Andrew Ng argued that reinforcement learning algorithms, which worked in simulation, didn't guarantee success in real-world applications. Fast-forward to 2025, DeepSeek's data-efficient approach addressed these challenges, demonstrating how reinforcement learning could be applied beyond theoretical models.
Barto and Sutton's work, based on separate research efforts, formed a unified perspective on modern reinforcement learning. However, practical solutions, engineering tweaks, and overcoming implementation challenges are critical to advancing AI applications in real-world environments.
Despite successes, the problem of learning from interactions to achieve goals still remains far from being completely solved. The AI community should remember Barto and Sutton's modesty and their reminder that machines may never fully replicate human intelligence, as we learn and grow from our interactions with others.
AI proponents promising the emergence of human-like or superintelligent machines dismiss Turing's warning that "an isolated machine cannot develop intellectual power." Turing emphasized the significance of human interactions and culture in the development of human intelligence – a lesson that AI researchers should not forget as they continue to pursue the future of artificial intelligence.
- Richard Sutton and Andrew Barto, recipients of the Turing Award, have built upon Alan Turing's conceptual ideas from the 20th century, where Turing proposed that machines could learn like children through a system of rewards and punishments.
- Surprisingly, despite the success of reinforcement learning algorithms, AI pioneer Andrew Ng pointed out in 2022 that these algorithms, which work in simulation, may not guarantee success in real-world applications.
- The unified perspective on modern reinforcement learning by Andrew Barto and Richard Sutton serves as a reminder that while machines can learn effectively through trial-and-error and delayed rewards, they may never fully replicate human intelligence, as we learn and grow from our interactions with others.
