Können KI-Algorithmen grosse Datenbestände klassifizieren und kategorisieren, ohne jede Vorgabe und Kenntnis, um welche Art von Daten es sich überhaupt handelt ?
ETH-Forscher sind auf der Spur und erste Resultate liegen vor (s.u.).
Falls man diesen Ansatz verallgemeinert, wird das Semantik Web somit „automatisiert“ und es lässt sich nun eine dritte Phase in der Entwicklung von Methoden zur computergestützten Daten-Kategorierung historisch erkennen:
- Phase: Wissensorganisation durch wenige Experten (strukturiere Datenbanken)
- Phase: Community-basierte Wissensorganisation (Web 2.0, Social Tagging)
- Phase: KI-Big-Data-basierte Wissensorganisation (Cognitive Computing)
Folgend einige Auszüge aus dem Forschungsbericht der ETH Zurich:
“Theoretical physicists deliberately misled intelligent machines, and thus refined the process of machine learning. They created a new method that allows computers to categorize data — even when humans have no idea what this categorization might look like.
[…]
When computers independently identify bodies of water and their outlines in satellite images, or beat the world’s best professional players at the board game Go, then adaptive algorithms are working in the background. Programmers supply these algorithms with known examples in a training phase: images of bodies of water and land, or sequences of Go moves that have led to success or failure in tournaments.
Similarly to how our brain nerve cells produce new networks during learning processes, the special algorithms adapt in the learning phase based on the examples presented to them. This continues until they are able to differentiate bodies of water from land in unknown photos, or successful sequences of moves from unsuccessful ones.”
Journal Reference:
Evert P. L. van Nieuwenburg, Ye-Hua Liu, Sebastian D. Huber. Learning phase transitions by confusion. Nature Physics, 2017; DOI: http://dx.doi.org/10.1038/nphys4037
Source: ETH Zurich. “Theoretical physicists deliberately misled intelligent machines.” ScienceDaily. ScienceDaily, 13 February 2017.
https://www.sciencedaily.com/releases/2017/02/170213131356.htm