"Machine learning is one of the leading methods in artificial intelligence. At the forefront of unsupervised learning techniques, the Modal project-team is working on innovative approaches, exploiting data represented in graphs."
The Modal team uses the capabilities of digital tools to analyze complex data and make the information they contain intelligible - a task that is far beyond human comprehension, given the sheer quantity and diversity of data! Think of the millions of business transactions or disparate data transmitted over a network. The relationships between data types and various location, time or even other parameters such as the weather, inventory status or a national holiday, all of which are continuous or sporadic relationships are unstructured and often even unidentified data.
These data modeled in graphs (which can be multi-dimensional) allow for the detection of relationships that are virtually invisible in other forms and, more importantly, with far fewer computational and energy resources.
For example, in business, when demand for a product exceeds supply, demand for substitute products will increase. Knowing which way to look can be of great value to both a producer and a supplier, but initially it is not information written down anywhere.
Examples of this order are numerous...
For the full article: Another artificial intelligence with graph-based learning
Illustration: Pixabay - Geralt
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