Abstract — The principle of artificial curiosity directs active exploration towards the most informative or most interesting data. We show its usefulness for global black box optimization when data point evaluations are expensive. Gaussian process regression is used to model the fitness function based on all available observations so far. For each candidate point this model estimates expected fitness reduction, and yields a novel closed-form expression of expected information gain. A new type of Pareto-front algorithm continually pushes the boundary of candidates not dominated by any other known data according to both criteria, using multi-objective evolutionary search. This makes the exploration-exploitation trade-off explicit, and permits maximally informed data selection. We illustrate the robustness of our approach in a number of experimental scenarios. I.

The technological singularity refers to a hypothetical scenario in which technological advances virtually explode. The most popular scenario is the creation of super-intelligent algorithms that recursively create ever higher intelligences. It took many decades for these ideas to spread from science fiction to popular science magazines and finally to attract the attention of serious philosophers. David Chalmers ’ (JCS 2010) article is the first comprehensive philosophical analysis of the singularity in a respected philosophy journal. The motivation of my article is to augment Chalmers ’ and to discuss some issues not addressed by him, in particular what it could mean for intelligence to explode. In this course, I will (have to) provide a more careful treatment of what intelligence actually is, separate speed from intelligence explosion, compare what super-intelligent participants and classical human observers might experience and do, discuss immediate implications for the diversity and value of life, consider possible bounds on intelligence, and contemplate intelligences

The first decade of this century has seen the nascency of the first mathematical theory of general artificial intelligence. This theory of Universal Artificial Intelligence (UAI) has made significant contributions to many theoretical, philosophical, and practical AI questions. In a series of papers culminating in book (Hutter, 2005), an exciting sound and complete mathematical model for a super intelligent agent (AIXI) has been developed and rigorously analyzed. While nowadays most AI researchers avoid discussing intelligence, the awardwinning PhD thesis (Legg, 2008) provided the philosophical embedding and investigated the UAI-based universal measure of rational intelligence, which is formal, objective and non-anthropocentric. Recently, effective approximations of AIXI have been derived and experimentally investigated in JAIR paper (Veness et al. 2011). This practical breakthrough has resulted in some impressive applications, finally muting earlier critique that UAI is only a theory. For the first time, without providing any domain knowledge, the same

I argue that science, art, music, comedy are just by-products of our intrinsic desire to create / discover more novel patterns, that is, data compressible in hitherto unknown ways. It is possible to rigorously formalize this concept and implement it on learning machines, thus building artificial robotic scientists and artists equipped with intrinsically motivated curiosity and creativity. I summarize our work on this topic since 1990; for concrete implementation details (1990-2009) see [17, 16, 30,

I have argued that a simple but general formal theory of creativity explains many essential aspects of intelligence including science, art, music, humor. It is based on the concept of maximizing reward for the creation or discovery of novel patterns allowing for improved data compression or prediction. Here I discuss what kind of general bias towards algorithmic regularities we insert into our robots by implementing the principle, why that bias is good, and how the approach greatly generalizes the field of active learning. I emphasize the importance of limited computational resources for online prediction and compression, and provide discrete and continuous time formulations for ongoing work on building an Artificial General Intelligence (AGI) based on variants of the artificial creativity framework.

Curiosity is an essential driving force for science as well as technology, and has led mankind to explore its surroundings, all the way to our current understanding of the universe. Space science and exploration is at the pinnacle of each of these developments, in that it requires the most advanced technology, explores our world and outer space, and constantly pushes the frontier of scientific knowledge. Manned space missions carry disproportionate costs and risks, so it is only natural for the field to strive for autonomous exploration. While recent innovations in engineering, robotics and AI provide solutions to many sub-problems of autonomous exploration, insufficient emphasis has been on the higher level question of autonomously deciding what to explore. Artificial curiosity, the subject of this paper, precisely addresses this issue. We will introduce formal notions of “interestingness” based on the concepts of (1) compression progress through discovery of novel regularities in the observations, and (2) coherence progress through selection of data that “fits” the already known data in a compressionbased way. Further, we discuss how to construct a system that exhibits curiosity driven by the interestingness of certain types of novel observations, with the mission to curiously go where no probe has gone before. 1