Artificial intelligence: what consequences for PIM, MDM and DAM?

Progress in AI is impressive. There are still limits but it is already possible to exploit them for product information management solutions, reference data, or in digital wealth management systems.

AI can do anything

Artificial intelligence, the media love it! And they talk a lot about it, including the powers that are given to him. And to convince the most reluctant, she managed to beat the man in the game of Go . Such an exploit was considered impossible for a machine. To calculate all the possibilities of game would be too long and the game of Go requires experiment and intuition … But the AlphaGo advances in Deep Learning won.

Thanks to this major technological breakthrough, we discover that artificial intelligence researches cancers, drives vehicles, dialogue in natural language … Artificial intelligence also makes coffee and is even the chef of the restaurant .

With such progress, we are made to believe that artificial intelligence is already capable of anything like programming in the place of man , composing music or creating works of art . Literature and sci-fi films make us aware that the uprising of machines is getting ready. Skynet will take power. Humanity is in danger because it loses control of these machines that are so superior to it.

Artificial intelligence does not think

Yet artificial intelligence does not think, at least the one that works today. AI is just a system of very advanced algorithms that are defined and controlled by humans. Artificial intelligence is able to exploit gigantic amounts of data (big data) to extract statistics and complex mathematical formulas that allow it to recognize and reproduce. And that’s the difference with Man. The AI ​​is, so far, not able to know and produce on its own. And such autonomy is not for tomorrow.
In a recent interview, Luc Julia, one of the designers of assistant Siri (Apple), recalls that ” artificial intelligence does not exist ” as he explains in a book of the same title. There is still a long way to go for artificial intelligence to become a real intelligence capable of consciousness, emotions, instinct, to work in several fields, to learn quickly by itself …

Recent advances in the field of machine learning and deep learning are based on a “learning” performed by the machine. This learning requires very large amounts of data and many iterations to be able to function. Google’s DeepMind victory over professional players at Starcraft II is a good example: it took the equivalent of nearly 200 years of training to reach this level of play . A gigantic time that shows that humans learn, happily, much faster. And they are able to capitalize on their experience to use it on new topics (to learn a new video game) while artificial intelligences are still specialized on very precisely defined tasks.

PIM / MDM / DAM and artificial intelligence

Today, product information management solutions (GIP or PIM), reference data (MDM) and digital asset management systems (DAM) can centralize large amounts of information (technical characteristics, logistics, marketing, tariffs …). The goal is to increase the quality of information and publish it effectively across different channels.

Using artificial intelligence in these solutions promises to solve all the problems encountered during the implementation of these software tools. Indeed, these systems can organize the information with a high level of finesse using many metadata. The quality of the information comes first and foremost from the fact that the users provide: a wrong price, an incoherent image, a duplicate form or incomplete properties … all these problems are caused by an incorrect data entry. 

With AI, we could have an intelligent system able to read all this information and identify what is wrong, correct errors and, better yet, capture the data.

The latest technologies already allow some of these actions. Today, the system can analyze your images and compare them to your product descriptions, translate texts automatically, analyze data and compare them to derive statistical rules and identify elements that do not respect them.
To properly perform such actions, it will be necessary to rely on existing “knowledge bases” (such as “Google Translate”, an artificial intelligence system that has already learned to translate into many languages) or develop your own database. knowledge. In this case, the task is complex because, for this database to be exploitable, it must contain a very large volume of data of very good quality . Not everyone has tens or hundreds of thousands of homogeneous product sheets of consistent quality to train their system:

  • If the quality of the data is uncertain or too fluctuating, the learning will be too and it will not produce good results,
  • If the amount of data is insufficient, the learning can not succeed,
  • If all the elements to be treated are too heterogeneous, no “statistical trend” will emerge properly.

Thus, the development of training games and control is a very delicate task. It requires the help of specialists (the famous data scientists). This is the most time-consuming task in machine learning projects .

As for the ability of machines to enter data or choose the right images, it all depends on the desired objective:

  • Prepare long descriptions from different characteristics or automatically associate images with the right products based on their metadata: this is already possible with “traditional” algorithms. What the AI ​​could do in addition is, from thousands of product sheets, analyze how the human has done to deduce the rules to apply. The utility is lower because users are usually able to formulate these rules directly.
  • Write appropriate marketing texts or choose the most rewarding image to present a product: such actions are subjective and rely on intuition. You have to have “the intelligence of situations”. The current artificial intelligence does not have it.

Exploiting progress in AI in the area of ​​PIM / MDM / DAM is already possible. There are still limits but they are pushed a little more each day.