Did you realize something? When analysts and media write about artificial intelligence (AI), most of them unfortunately only talk about machine learning. In doing so, they mention AI and machine learning in the same breath and thus equal AI with one single technology. This is wrong and a concerning progress.
In particular, it is confusing the market during a time when 58 percent of organizations worldwide (according to Forrester) are still researching AI. However, AI is more than just machine learning and consists of several different components that provide intelligent solutions.
Machine learning is not equal to AI
First of all, machines do not understand. This is by far the biggest misconception while discussing AI, in particular in the context of virtual private assistants like Amazon Alexa or Apple Siri. Machines match data to predefined data patterns of understanding. Thus, understanding is a question of the size of a data pool, because the more data is matched to something we can understand the more “understanding” a machine seems to have.
The biggest issue is that AI research has been an oscillating system between several techniques. Whenever one does not do “the job completely,” people get frustrated and turn to another one. And right now, the market is of the opinion that organizations do have tons of data that can be utilized together with machine learning algorithms. And since some machine learning use cases succeeded, machine learning is hyped by the media.
However, machine learning just helps to identify patterns within data sets and thus tries to make predictions based on existing data. It is most important to check the plausibility and correctness of the results since you can always find something in endless sets of data. And that’s also one of the drawbacks if you consider machine learning as a single concept. Machine learning needs lots of sample data or data in general to learn and be able to find valuable information respectively results in patterns. […]