In part one, we shared two examples illustrating how new technologies can take years or even decades to reach their full transformational potential. To avoid the same pitfall with digital transformation, we believe companies need to…
AI is getting more powerful every day, and we witness its presence in our daily lives solving tougher and tougher business and consumer problems. If with great power comes great responsibility, then with great AI capabilities comes great complexity.…
Azure Purview, which is currently in preview, is a long-awaited tool from Microsoft that is designed to provide a solution for centralized data governance for an organization’s entire environment, including…
Deep Reinforcement Learning (DRL) techniques have allowed deep neural networks (DNN) based AI to move from theoretical concepts in the research lab to real-life, business solutions. Although tools such as the Microsoft Project Bonsai toolchain have tremendously streamlined, accelerated, and simplified the design, training, and deployment of Autonomous Systems AI agents, it does not mean they suddenly became simple…
The traditional AI training method is rarely applicable in real-life business use cases simply because it requires an amount of data seldom available to businesses. Using the self-training mechanism of deep reinforcement learning,…
Before diving deep into the concept of using simulators to solve Reinforcement Learning (RL) real-world problems, let’s understand the basics of RL. What is Reinforcement Learning? Reinforcement Learning (RL) is an…
In Deep Reinforcement Learning (DRL), an agent needs to interact with the environment (either physical or simulated) by performing actions to obtain rewards. The agent’s goal is to maximize its rewards and learns by adjusting its policy (the agent’s strategy) based on…
What is Reinforcement Learning? Deep Reinforcement Learning (DRL) or simply Reinforcement Learning (RL) is an area of machine learning that focuses on the training and decision-making abilities of AI agents.…