Track 07
Artificial intelligence for art AIA:
Computational creativity, Neural networks, Simulating human activity.

Track chair Robert B. Lisek (Institute for Research in Science and Art).

We observe the success of artificial neural networks in simulating human performance on a number of tasks: such as image recognition, natural language processing, etc. However, There are limits to state-of-the-art AI that separate it from human-like intelligence.  Today’s AI algorithms are limited in how much previous knowledge they are able to keep through each new training phase and how much they can reuse. In practice, this means that it is necessary to build and adjust new algorithms to every new particular task. This is closer to a sophisticated data processing than to real intelligence. This is why research concerning generalization are becoming increasingly important.

Processes such as intuition, emotions, planning, thinking, and abstraction are a part of processes, which occur in the human brain. A generalization in AI means that system can generate new compositions or find solutions for new tasks that are not present in the training corpus. There is a domain called AGI where will be possible to find solutions for these problems. Artificial general intelligence (AGI) describes research that aims to create machines capable of general intelligent action. “General” means that one AI program realizes a number of different tasks and the same code can be used in many applications. We must focus on self-improvement techniques e.g. reinforcement learning and integrate it with deep learning, recurrent neural networks, and random walks generators.