Creating solutions that can learn without being explicitly programmed. Constructed algorithms can make predictions or decisions by building a model from sample input data. Machine learning is usually employed in tasks where classical solutions are infeasible or difficult to design.

A core objective for machine learning is to generalize from experience, build a general model based on learning data set to produce reliable and repeatable results and uncover insights in previously unseen examples.


Creating algorithms for optimization inspired by biological evolution. A population of solutions is improved from generation to generation by rewarding desired specimens and introducing small random changes reflecting natural selection and mutation. In result population gradually evolves to increase in fitness.

Evolutionary computation techniques produce highly optimized solutions for a wide range of problems. Many variants and extensions exists tailored for specific family of problems and data structures.


Creating virtual scenarios that simulate a realistic experience. The immersive environment similar to real world ensures lifelike experience that simulates physical presence in virtual reality.

Augmenting real world environment by computer generated sensory information. Overlaid digital components are perceived as an immersive aspect of the real environment and enhance physical world. With augmented reality the surrounding real world becomes interactive.


Creating decentralized, distributed ledgers using blockchain technology. Transactions between two parties are efficiently recorded in verifiable and permanent way as a list of linked blocks secured with cryptography.

Blockchain is inherently resistant to modification of the data and is typically managed by collective network. Technology invented primarily for use in the cryptocurrencies can be integrated into multiple areas and has great potential.