When it comes to algorithm formation, the area of Artificial Intelligence is shaped by two opposite schools of thought. According to one view, in order to generate the most efficient ‘learner’, one should strive to construct a machine learning engine with almost no use of prior expert knowledge. In this way, the ‘new knowledge’ is derived in a pure ‘trial and error’ process based on large volumes of data. In contrast, the second approach supports the partial implementation of already formulated sets of rules and assumptions into the learning algorithms, ahead of running any experiments with the ingested data.

In general, ‘learning’ may be defined as the process of evolution of one’s knowledge, or assumptions based on the combination of past experience and results of recent behavior. The dynamic nature of our varying surroundings demands that in order for our perceptions and assumptions to always reach their optimal level, they have to be in the process of continuous, real-time adjustment. In simple terms, we’re in a learning process at all times. In contrast to human learning, however, in the area of machine learning-based business applications this does not always hold true.

Thanks to its proprietary technology, ORA AI is able to reach its optimal strategy solutions even in the event of being supplied with small data sets, distinguishing itself from the machine learning algorithms present at more generic AI platforms (e.g. Google Smart Bidding), where the optimal levels are predominantly achieved through deep-learning based methods. As a result, ORA AI is capable of driving outstanding performance growth for users with limited channel performance (e.g. for advertisers who have not yet achieved substantial number of conversions).

So how does ORA AI technology manage to choose the optimal bidding policy in such efficient way?
What is the secret to its functional superiority?
It too is faced with a choice between an almost infinite number of possible strategy combinations in the process of examining the present and predicting the future state of the platform’s competitive environment. What element of its ML-powered engine allow its algorithms to quickly identify the ultimate real-time method while being exposed to even small data sets?

Defined as ‘Optimal Learning’, the challenge of acquiring the desired information from the supplied data in the most efficient way is addressed by ORA AI with the use of its Knowledge Gradient (KG) approach. In simple terms, KG is a method that defines a way in which an algorithm evaluates the data on its way to discovering the piece of information that will guide its final policy choice. In this case…

‘Learning to Learn’ is considered as one of the most powerful concepts in the field of Machine Learning. Its functionality lays in the idea of ‘taking one step back’ in the process of ‘learning’, and subsequently deciding on how and when to ‘learn’ in order to maximize desired outcome. ‘Learning to Learn’ algorithm decides on when to exploit currently possessed knowledge about the environment in which it is functioning in order to maximize immediate gains, and when to perform actions that lead to obtaining more information about that same environment. Through the use of very sophisticated mathematics, ORA AI is able to actively ‘learn on the go’ about how to interact with the environment, thus collecting the new information about the environment at as little ‘opportunity cost’ as possible. Namely, it minimizes the loss of immediate returns on its way of achieving the long term profitability. It all happens ‘online’, that is, while ORA AI is working and earning money within the environment. As a result, ORA AI always has the most up-to-date information at hand to make the most profitable decision.