You must control AI for ethical reasons and to mitigate risks, but most of all to grant efficiency.
The increasingly pervasive presence of artificial intelligence makes us feel that we are facing a full-blown revolution, one of those moments that define the evolutionary line of technology in the next 10 to 20 years. It is not the first time this has happened – it happened with the advent of personal computers, with the explosion of the Internet, with the spread of smartphones or with the development of IoT technologies.
Often these ‘revolutions’ happen when different elements come together to make possible what was considered mere theory. Much of what we consider innovation is not the result of a single invention but of the convergence of technologies and techniques that combine to create something completely new that was already in the realm of the possible but for reasons more practical than theoretical could not be exploited.
Large computing power, until a few years ago the privilege of very few, is now available on a large scale; at the same time, huge amounts of data are being produced from heterogeneous sources that can track virtually any process (or behavior). This combination of factors has allowed long known techniques (algorithms, what we call artificial intelligence) to express their full potential.
The availability of amounts of data unthinkable a decade ago has also enabled the creation of models capable of behaving like human beings who are able to interact with us through natural language (generative artificial intelligence).
In short, given that all the conditions have been met for artificial intelligence to become a technology within the reach, if not of everyone, then certainly of many, it is easy to understand the consequent explosion of a new market and the development of tools to facilitate the adoption of artificial intelligence by companies in any sector.
The Paradox of AI Efficiency
On paper, then, we have a new technology with revolutionary features, but we have to ask ourselves what is the main driver for such adoption. In other words, what do those who decide to incorporate tools that make use of AI into their processes expect to achieve? Obviously, the answer to this question can be very complex because the reasons may be different; but if one had to choose one that represents a synthesis of many, it would probably be ‘efficiency’. Whether in production lines, in services, in a company’s internal processes, adding efficiency means achieving one or more of these things: greater speed, lower costs, better services.
AI is therefore the ideal solution for – for instance – automating a process of data collection and sorting that before such adoption was done manually by human staff, gaining efficiency (lower costs and higher speed), or again, a predictive algorithm capable of optimizing the performance of an industrial production line through anomaly detection and avoiding stoppages certainly adds efficiency (again, lower costs and better services).
If this is true, however, we may be faced with a paradox. Let us imagine that we are developing an AI-based application that allows us to add efficiency to our processes; typically this is something measurable – the number of ‘human’ hours spent on a task, the amount of product or waste, etc. – and we will then be able to figure out the actual value of our project and the impact on our profit and loss account. But where is the paradox then? The point is that very often, when developing an AI-based project, we tend to forget one of the main characteristics of artificial intelligence, namely its changeability. AI does not behave in the same way as the traditional software we are accustomed to. In fact, a good testing strategy is not enough to be sure that what we bring into production is bug-free, because the algorithms, once in production, are exposed to variables that also significantly modify their performance: the data come from the real world and the real world is constantly changing.
It means that the efficiency we have achieved is constantly ‘at risk’. Here then is the paradox: by not adopting a constant monitoring strategy and the techniques to be able to intervene in good time, we risk losing all or part of that efficiency that we have pursued and perhaps achieved in the development phase.
The importance of AI Monitoring
Monitoring artificial intelligence by means of tools capable of measuring its performance in production, of intercepting anomalies or changes in the flow of incoming data is therefore not only a matter of ethics or risk reduction, but above all a guarantee to maintain efficiency, the primary objective we had set ourselves.
This is why, after an initial ‘gold rush’ phase in which a plethora of tools designed to work with generative AI models emerged, in order to exploit them in the simplest way possible, the issue of control and monitoring is becoming, if not central, then at least on the typical horizon of those who are preparing to ground a strategy for the systematic adoption of AI in their processes.
The diffusion of such tools also requires a cultural paradigm shift, i.e. the ability to see AI applications not only for their immediate result but in the context of their overall life cycle – which also includes the changes associated with operation in production.
Radicalbit’s platform was born with precisely these concepts in mind, and was developed with the idea of providing an agile and scalable tool to monitor and control models in production: in other words, to ensure the efficiency of our model.
Explore the GitHub repository or our website!
