Organizations are rapidly gaining access to sophisticated artificial intelligence techniques as a platform for accelerating innovation and solving complex business problems. We push the limits of what is possible to stay competitive, as do all companies. We face similar challenges, such as a steep learning curve and a limited window of opportunity, which means we can only conduct one or two large experiments to improve our competitive efficiency.
Reinforcement learning, unlike other forms of machine learning, employs algorithms (which are often used to train AI agents or bots) that do not rely solely on historical data sets, labeled or unlabeled, to learn to make a prediction or perform a task. They learn by trial and error, as do most humans. The technology has advanced in recent years to the point that it is now highly scalable and capable of optimizing decision-making in complex and dynamic environments. In addition to speeding up and enhancing design, reinforced training is increasingly integrated into a wide variety of complex applications: product recommendation in systems where customer’s actions and expectations shift rapidly; time-series prevision under highly dynamic conditions.
These essential practices are being adopted by an increasing number of companies, and those that do appear to report the greatest bottom-line effects from AI. Our experience working with hundreds of clients on analytics and AI over the years has shown that they often think differently about AI. Rather than being used haphazardly, Artificial Intelligence has become ingrained in the collective consciousness.