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Artificial Intelligence

What is artificial intelligence?

Defining "Artificial Intelligence" (AI) can be challenging. In most cases, it refers to simple to highly complex algorithms that automatically address specific questions within IT systems. Software systems can recognize patterns in images or sounds. Sounds can then be translated into speech, converted into text, perform functions (such as dialing a phone number), or answer questions. From identified patterns in data, it is also possible to make predictions.

In Digital Asset Management, patterns need to be recognized, so Artificial Intelligence is responsible for object recognition in photos. TESSA DAM, for instance, can recognize whether a photo contains a pen, a toy, a tool, a light, or a screw. This extends to recognizing faces, famous landmarks, or objects like Mila Kunis, the town hall in Volkach, Fabergé eggs, or Picasso paintings. In the frontend, users can search for these without the need for manual tagging by employees. Artificial Intelligence in this context is multilingual, more precise, and comprehensive compared to human intelligence. As a result, more or less complex but repetitive tasks are automated by a machine, TESSA DAM. Artificial Intelligence also enables the search for similar images.

What types of artificial intelligence are there?

Artificial intelligence works with algorithms, and the complexity of these algorithms can vary significantly. In marketing technology, there are simple algorithms that help employees with highly repetitive tasks. For example, in a Google advertising account, it's possible to turn keywords on or off in specific situations, making decisions based on factors like time of day, pricing, or click frequency. A bit more complex is when the system has to determine whether one advertising asset is better than another. In this case, significance levels need to be achieved. However, this is still a relatively simple form of artificial intelligence.

It becomes more challenging for computers to recognize patterns, which can come in various forms. Semantically, these patterns can involve elements in images, such as objects like cars, food, animals, and more. Patterns can also be written or spoken words. When it comes to spoken words, it's necessary to translate sound modulation into written words. These written words have a specific meaning for us, who can read, but for computers, they are patterns that can be used to establish relationships. In all these cases, training is required for computers. In simplified terms, you import 200 photos showing only a dog and inform the computer that there's a dog in those photos. Then, you present the computer with photos of various two- and four-legged creatures and ask it to identify the ones with a dog. If the computer can successfully complete the task in 9 out of 10 cases, it has achieved 90% accuracy. This is referred to as machine learning. In the described form, it is supervised learning, where the computer can rely on previously classified material by humans. In a more complex scenario, the computer collects its own learning material, for example, by searching for noun lists on Google, importing images from image searches, and later checking them against other photos.

These methods are used in various fields, including machine translation, language assistance systems, robotics, autonomous driving, optimization of marketing campaigns, and more.

What role does Artificial Intelligence play in marketing?

When using the term Artificial Intelligence in the context of marketing, the concept of Marketing Automation quickly comes into play. Marketing Automation primarily involves CRM activities in a broader sense. This means it deals with how leads can be generated automatically, when and with what likelihood mailings should be sent to which leads or customers. Bots are meant to become more user-friendly and take on initial communication steps. Automated monitoring takes place at each stage, allowing the systems to improve themselves based on simple algorithms. The goal is to decode language to provide accurate responses to customer inquiries. In addition to this, content needs to be automatically assembled correctly in terms of language, which involves basic tools like Google's GPT-3. Based on this technology, a range of tools has been developed to generate texts automatically, such as for blogs or SEO purposes. Email subject generation works quite well with these tools. You can find many tools based on GPT-3 that are used in Conversational Commerce at https://gpt3demo.com/--1.

Going a step further, Artificial Intelligence greatly aids in evaluating customer activities and optimizing websites. This mainly involves processes related to monitoring and market research.

What artificial intelligence do DAMs use?

In Digital Asset Management, an essential task is establishing the connection between assets and products. This is typically achieved through a product ID or product number, which involves very simple algorithms. Some DAM providers refer to this as Artificial Intelligence, but in the case of such basic fixed algorithms, it should not be labeled as AI.

Another crucial role of DAM systems is to enhance the discoverability of contained assets. Besides product IDs, assets are linked with data from PIM systems. Moreover, in the realm of Artificial Intelligence, assets can be automatically enriched with metadata. This metadata is obtained through AI, for instance, using the Google Cloud Vision API to ensure object recognition in photos and tagging them. This includes well-known objects like famous landmarks such as the Brandenburg Gate, paintings, or the mentioned Fabergé eggs. This process is multilingual and greatly aids in locating action or ambiance photos for advertising purposes. Face recognition is also possible through AI. What's easier for AI is associating similar assets. This is achieved, for instance, using color spaces found in photos. Additionally, photos with similar objects are linked together.

One crucial point not to be forgotten is that photos and videos are increasingly generated by computers using artificial intelligence. This involves 3D objects and textures, such as graphics applied to the surfaces of objects. These need to be provided in DAM systems and associated with respective objects. This will also become an important task for DAM systems like TESSA DAM in the future.

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