This essay introduces randomization as a powerful tool that can significantly enhance algorithm performance. For instance, randomized algorithms excel in adversarial environments, often outperforming deterministic algorithms in worst-case scenarios. Furthermore, their success probability can be amplified through simple strategies like repetition and majority voting. The study focuses on integrating randomization into deep neural networks, particularly transformer models. The researchers demonstrate, for the first time, that randomized algorithms can be learned and embedded in transformers using purely data-driven and goal-oriented approaches. The essay begins by analyzing known adversarial objectives where randomized algorithms have a clear advantage over deterministic ones. It then shows how common optimization techniques, such as gradient descent or evolutionary strategies, can effectively learn the parameters of transformer models, leveraging the randomness provided to the system. To highlight the broad applicability of randomization in enhancing neural networks, the study explores three conceptual tasks: associative recall, graph coloring, and navigating grid worlds as an agent. Notably, the experiments reveal that randomization not only increases robustness against adversarial attacks but also leads to significant performance improvements due to the inherent randomness in neural network computations and predictions. In the associative recall task, the model was tested on its ability to retrieve specific memories from a large dataset. The randomized approach proved more effective in handling noisy or ambiguous inputs, leading to higher accuracy and reliability compared to deterministic methods. For graph coloring, which involves assigning colors to nodes in a graph so that no two adjacent nodes share the same color, the model leveraged randomization to explore a wider range of potential solutions, resulting in better coloring outcomes. Finally, in the grid world navigation task, the agent used randomization to improve exploration, successfully finding optimal paths under various conditions more frequently than with deterministic strategies. These findings underscore the potential of randomization in improving the performance and resilience of transformer models in a variety of contexts, from memory recall to complex problem-solving tasks. The researchers suggest that this approach could pave the way for more robust and adaptable AI systems, capable of handling real-world challenges with greater efficiency and reliability.