Implementation of background mathematical knowledge

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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.

As both of the aforementioned propositions yield impactful gains in efficiency, we decided to find a ‘golden mean’, and optimize the creation ORA AI in way that gives us the opportunity to capitalize on the most powerful ‘learning’ method. ORA’s machine learning algorithm merges with the prior expert knowledge, which allows it to approach the optimal solution faster than the competing algorithms. Moreover, the implemented method is proved to function as a more effective precaution against fundamental errors caused by misinterpretation of the structure of the data by conventional learning algorithms.

Adaptive Machine Learning

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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.

In machine learning (ML), there are two central approaches with respect to data processing – ‘open-loop’ and ‘closed-loop’ ML. ‘Open-loop’ learning, which serves as the most commonly used method, develops its applicable insights during discontinuous analysis, where the solutions are based on segmented data sets collected off-line. As a result, there’s no steady stream of data into the learning process, which consequently hinders the opportunity to improve the on-going evaluation. In the event of considerable competitive landscape change, the proposed actions may turn out to be outdated, and it may take some time to re-evaluate the model. ‘Closed-loop’ ML, on the other hand, referred to as Adaptive Machine Learning (AML), is designed to process oncoming data in real-time, which allows it to constantly track the occurring deviations, and build up its knowledge base to drive better future solutions. In addition, it is able to monitor the results of the proposed actions and implement any necessary corrections.
The development of Adaptive Machine Learning engine allows ORA AI to have a real-time interaction with the competitive environment that it operates in, always being able to provide its users with best possible operational strategy.

Working on small data sets

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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).

Knowledge Gradient

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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…

KG takes into consideration the marginal ‘value’ of the information derived from an evaluated data sample, which can be interpreted as an information’s degree of relevance. The algorithm performs a sequential analysis in which each subsequently assessed alternative dataset depends on the result of the previous point-analysis. As a result, it yields considerable impact on the final solution. Thus, the ascribed optimal learning phenomenon takes form of a sequential problem in which all of the single data explorations are tied to each other based on the informational benefit they provide. Simply, KG can be compared to a type of cartography style with which one can construct a map leading through the supplied data, and whose ultimate point pictures the most efficient ad campaign strategy. Due to the essential guidance, the optimal performance level can be achieved in much smaller amount of time.

Learning to Learn

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‘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.