One of the most striking examples comes from the research that Navlakha made in his laboratory last year. Navlakha created an olfactor-based similarity exploration algorithm and applied it to the processing of image datasets. He and his team found that their algorithms are far superior to traditional non-biological methods in their performance, and sometimes their dimensionality reduction can reach 2-3 times.

The experimental results of Nowotny and Navlakha show that basic untrained networks are already available for performing classification calculations and other similar tasks. Systems built in such coding schemes can also perform subsequent learning tasks more easily. In the work currently under review, Navlakha used a similar, olfactory-based approach to novelty detection: after training with thousands of similar objects it successfully identifying new related objects.

Nowotny is studying how to use the olfactory system to treat the mixture and it is a challenge that machine learning technology faces in this type of application. Nowotny and his team found that people do not deliberately separate the odors when sniffing, the odor recognition between coffee and croissants is done in a very rapid alternating manner.

This insight is equally important for artificial intelligence technology. For example, it is often difficult to separate multiple simultaneous conversations in a noisy environment at a reception. If there are multiple talkers in the room, then artificial intelligence can process it by switching the sound signal to a very small time window. And if the system recognizes a voice from a certain talker, it may try to suppress input from other talkers. Through such an alternation, the neural network will be able to smoothly analyze the conversation content.