Before Artificial Intelligence Takes Off, A Common Language Is Needed

In an environment of advancing market trends, technological developments, and regulatory provisions, pragmatic and easily implementable solutions are more valuable than ever. Excitingly, artificial intelligence is maturing to a point where it can offer some powerful options for businesses.

Before getting ahead of ourselves, however, it's important to (re)examine what artificial intelligence can and should be used for. It is often thought that AI is a replacement—even a partial replacement—for human intelligence. This is the wrong approach. While humans are capable of (for example) emotional and social intelligence, computers are per their design restricted to logical mathematical intelligence.

However, in this latter area they are extremely powerful: with respect to the (speedy) processing of (large) datasets as well as to memory and data storage, they undeniably outperform human beings. For those facets of intelligence that only humans can handle, like creativity, leadership, and consciousness, however, computers require supervision and support. For example, oversight and matters of accountability are firmly human activities.

Data, data, data

At a recent workshop, experts Marc Hemmerling (of the ABBL) and David Hagen (of the CSSF) explained that most AI issues, at their core, regard data and data access. The European Commission's agenda, unsurprisingly, follows the same problematic areas. Given that the power of an AI system rises and falls with the quality of the data it is fed with, the regulator's stance is clear: the responsibility for obtaining the right level of data quality remains with the institution deploying the AI system.

One company travelling the artificial intelligence road is PayPal. At the same workshop, Claire Alexandre (of PayPal) talked about how the company greatly benefits from AI. Given that PayPal currently has 267 million active users and operates in 20 languages, the power and scalability of AI are crucial. Furthermore, since more and more payment transactions are done via social media, handling workflows in real-time is a must.

However, Claire Alexandre explained, this is also the speed at which fraudsters work, and the primary field of AI applications for PayPal is in fact fraud prevention. Here, a key to success is for the AI system to look at stories rather than individual data points, a task well-suited for machine learning systems. At PayPal, AI has thus become a core element for managing risks with the...