- Face Detection
With the help of machine learning methods, a trained machine is able to autonomously recognize faces and add the findings to file itself or to the DAM system. The data scientist uses photos during training and teaches the machine who is shown. After a short while - the number of training files for a person is usually around 20 samples - the machine will start to work on its own using previous experiences and assumptions to evaluate probabilities. So, whenever you add a new image to system, it automatically recognizes the person (if trained before) and adds this information.
- Image Recognition
Aside from intelligent text analysis through methods of machine learning, images and videos make a large part of content your might be dealing with. The problem with graphical data is, that an ordinary text crawler is only able to retrieve information stored in the file such as META data or the file name. The "dumb" program does not know what's actual meaning of the picture or what's displayed n the first place. Machine learning closes this gap by recognizing the valuable information and creating patterns for following data.
- Named Entities
With the Named Entities Cognitive Service you are able to automatically recognize the overall meaning of a text. The machine learning method scans the text, spots key terms and from a relation to each other detects the meaning of it. In this article, find out how the difference between a cat and a car is important and how machine learning might learn to differentiate.
- Related Words
While using machine learning methods with texts, the related words service allows you to automatically find connections between terms. Displayed like as a tag cloud, this cognitive service shows you not just synonyms but also elements that only might belong to the word at a second glance. Once trained, the machine digs through a large amount of data and creates a model allowing independent decisions whenever new input arrives or known is updated.
- Text Analogies
One of the main obstacles of modern text based search is the diversity of languages spoken around the globe. Different linguistical heritages, alphabets only used in single language families or meanings attached to the words that differ make it hard to find results without any prior translation. Until today, we usually restrict our search only to languages we are able to understand, ignoring a huge potential that remains hidden because we don't understand the content. Although English is the modern Lingua franca and the scientific community requires publications made in English, all the content aside from it is in a deep sleep. With the advent of machine learning methods, this problem will be led into a new direction, enhancing our knowledge broadly.