The institute's primary interest is the comparison of
roadmaps towards general artificial intelligence
. This is a very challenging task that requires the definition of
as well as many vital
Open Research Questions
To progress in the search for human-level artificial intelligence, we need to focus on areas of research that truly matter. This means avoiding our inherent biases as researchers, as well as biases imposed upon us by our environment.
History has taught us repeatedly that fashionable and trendy approaches, albeit exciting, are frequently short-sighted and possibly detrimental to the long-term progress in the field. For this reason, as a community we should strive to distance ourselves from our biases and open up to the 'bigger picture', especially from a long-term perspective.
This should allow us to
compare and contrast various approaches to solving human-level artificial intelligence
identify potential dead ends
highlight open research problems
that we should focus on now, rather than be swayed by current trends in the field.
The institute continually maps the field and identifies such areas and publishes them.
Mapping AI Research
The landscape of AI research is expanding at an accelerating rate. It is increasingly more difficult to keep up-to-date with all relevant progress in the field. We strive to find ways to track progress in the field in a manageable way, for it to be beneficial to all our endeavors and the community at large.
The amount of published research, software libraries, datasets and environments has exploded over the last few years, with hundreds of new papers appearing daily. This project explores ways in which tracking the progress in the field can be made practically more feasible.