Random quasi intersections method was introduced. The number of such intersections grows exponentially with the increasing amount of pattern features, so that a non-polynomial problem in some machine learning applications emerges. However, the paper experimentally shows that randomness allows finding solutions to some visual machine learning tasks using a random quasi intersection-based fast procedure delivering 100% accuracy. Also, the paper discusses implementation of instant learning, which is, unlike deep learning, a noniterative procedure. The inspiration comes from search methods and neuroscience. After decades of computing only one method was found able to deal efficiently with big data, - this is indexing, which is at the heart of both Google-search and large-scale DNA processing. On the other hand, it is known from neuroscience that the brain memorizes combinations of sensory inputs and interprets them as patterns. The paper discusses how to best index the combinations of pattern features, so that both encoding and decoding of patterns is robust and efficient.