|Organisation:||Amsterdam University of Applied Sciences & The Asimov Institute|
Sixty years ago a group of eminent researchers gathered at Darthmouth College for a conference during which they coined the term artificial intelligence. They outlined a number of aspects of intelligence that they thought could be made specific enough to be simulated with a computer. Creativity was one of these aspects. However, over the next decades creativity proved to be a rather elusive concept that was much more difficult to formalize than 'automation' or 'self-improvement'. Due to the spectacular advancements in simulating these other aspects of intelligence, the question whether machines can be creative faded into the background in favor of more practical questions and applications.
Recent years have seen a number of breakthroughs that indicate a renewed interest in the possibilities of creative machines. Game-playing agents developed by Google Deepmind are capable of learning surprisingly original solutions. Deep learning allows anyone to create paintings in the style of famous artists. Other computational tools are now used to inspire and guide human designers. In the wake of these (and many other) technological developments, the processes that underlie creativity have come under increased scientific and philosophical scrutiny as they may (or may not) distinguish us from machines. In this talk, we will look at examples of computational creativity, consider to what extent they qualify as being creative, and conjecture what additional steps would be necessary for artificial creativity.
Stefan Leijnen is a lecturer and researcher of artificial intelligence at the Amsterdam University of Applied Sciences. His primary research interest is in understanding how novelty arises from dynamical systems, particularly in neural networks. More generally, he is interested in generative computational methods, deep learning and epistomology. He has also recently founded the Asimov Institute, an independent research institute aiming to develop technological solutions for creativity and control of machine learning systems.