Management Consulting - since 1993

Barbaros Akerman

Akerman Group



In terms of their frequency of use, the terms “artificial intelligence” (AI) and “machine learning” (ML) have now reached the status of buzzwords – and this frequency is reflected in the number of interpretations. Accordingly, it is worth establishing a common understanding:

Artificial intelligence (AI): AI refers to the intelligence of non-living objects – that is, machines that are equipped with a broad-reaching analytic powers. This power gives rise to various – typically human – abilities such as perception, learning ability, reasoning, planning and the derivation of decisions. Depending on whether these abilities can only function in a particular context or can also be adapted to new contexts, a differentiation is made between weak and strong AI systems.

Machine learning (ML): Within the framework of ML, computers are equipped to learn without first being explicitly programmed with specific details. This means that a computer will automatically recognise patterns and laws in the data provided. Upon completion of the learning phase, the experience gained is generalised – that is, converted into knowledge that can then be applied to new data sets by way of transfer. Accordingly, ML is essential for the realisation of artificial intelligence, since in essence, intelligence is nothing but learning. Just as people learn to communicate, recognise certain patterns (for example, in the form of grammar), or take into account rules when driving, machines can be trained to take over the related tasks independently. Since AI systems are generally based on ML, we often hear the two terms being used synonymously. For our purposes, we understand ML as a necessary prerequisite for AI.

The principle of AI is nothing new and the first theoretical concepts are already several decades old. However, it is only now that the technical prerequisites (in terms of sufficient hardware performance) exist for its potential to be exploited on an industrial scale and thus used to achieve improvements in performance, especially through automation.