Predictable Artificial Intelligence

We introduce the fundamental ideas and challenges of Predictable AI, a nascent research area that explores the ways in which we can anticipate key indicators of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems, and thus should be prioritised over performance. While distinctive from other areas of technical and non-technical AI research, the questions, hypotheses and challenges relevant to Predictable AI were yet to be clearly described. This paper aims to elucidate them, calls for identifying paths towards AI predictability and outlines the potential impact of this emergent field.Comment: 11 pages excluding references, 4 figures, and 2 tables. Paper Under Revie

Differentiating between Bayesian parameter learning and structure learning based on behavioural and pupil measures.

Funder: Radboud UniversiteitWithin predictive processing two kinds of learning can be distinguished: parameter learning and structure learning. In Bayesian parameter learning, parameters under a specific generative model are continuously being updated in light of new evidence. However, this learning mechanism cannot explain how new parameters are added to a model. Structure learning, unlike parameter learning, makes structural changes to a generative model by altering its causal connections or adding or removing parameters. Whilst these two types of learning have recently been formally differentiated, they have not been empirically distinguished. The aim of this research was to empirically differentiate between parameter learning and structure learning on the basis of how they affect pupil dilation. Participants took part in a within-subject computer-based learning experiment with two phases. In the first phase, participants had to learn the relationship between cues and target stimuli. In the second phase, they had to learn a conditional change in this relationship. Our results show that the learning dynamics were indeed qualitatively different between the two experimental phases, but in the opposite direction as we originally expected. Participants were learning more gradually in the second phase compared to the first phase. This might imply that participants built multiple models from scratch in the first phase (structure learning) before settling on one of these models. In the second phase, participants possibly just needed to update the probability distribution over the model parameters (parameter learning)

On kitsch and kič: Comparing kitsch concepts from Bavaria, Serbia and Slovenia

The German word kitsch has been internationally successful. Today, it is commonly used in many modern languages including Serbian and Slovenian (kič)-but does it mean the same? In a pilot study, thirty-six volunteers from Bavaria, Serbia and Slovenia rated two hundred images of kitsch objects in terms of liking, familiarity, determinacy, arousal, perceived threat, and kitschiness. Additionally, art expertise, ambiguity tolerance, and value orientations were assessed. Multilevel regression analysis with crossed random effects was used to explore crosscultural differences: Regardless of cultural background, liking of kitsch objects was positively linked to emotionally arousing items with non-threatening content. Self-transcendence was positively linked to liking, while ambiguity of the parental image was concordantly associated with kitschiness. For participants from Serbia and Slovenia, threatening content was correlated with kitschiness, while participants from Bavaria rated determinate items as kitschier. Results are discussed with regard to literature on kitsch and implications for future research

Beyond the Adult Mind: A Developmental Framework for Predictive Processing in Infancy

Predictive Processing has been proposed as the single unifying computation underlying all of cognition, and proponents argue that all psychological phenomena can be explained as consequences of this mechanism. This theory has inspired many cognitive scientists and neuroscientists, but it currently has no developmental mechanism that would explain how infants begin to perceive and learn about the world. Rather, it treats human cognition as if it exists in a fully-developed adult with a history of observations and world knowledge. In its current formulation, Predictive Processing only allows for perception of incoming stimuli given the existence of expectations based on previous experiences, and as such does not allow for an infant to ever make a first observation. In this paper, we propose a possible starting point from which the infant can begin to develop predictive models, as well as a toolkit necessary to allow the infant to perform the range of cognitive operations on predictive models necessary for learning. The starting point we propose is a set of low precision, low-level of detail predictions with little or no hierarchical structure, which is very rapidly updated to reflect the infant’s early environment. The toolkit contains a range of operations referred to collectively as structure learning, which are applied to models in order to allow for building adult-like hierarchical models. These modifications are necessary for developmental scientists to be able to adopt the Predictive Processing framework and benefit from its advantages, but also for Predictive Processing to be able to explain all human cognition, which inherently must include development

On kitsch and kič: Comparing kitsch concepts from Bavaria, Serbia and Slovenia

The German word kitsch has been internationally successful. Today, it is commonly used in many modern languages including Serbian and Slovenian (kič)-but does it mean the same? In a pilot study, thirty-six volunteers from Bavaria, Serbia and Slovenia rated two hundred images of kitsch objects in terms of liking, familiarity, determinacy, arousal, perceived threat, and kitschiness. Additionally, art expertise, ambiguity tolerance, and value orientations were assessed. Multilevel regression analysis with crossed random effects was used to explore crosscultural differences: Regardless of cultural background, liking of kitsch objects was positively linked to emotionally arousing items with non-threatening content. Self-transcendence was positively linked to liking, while ambiguity of the parental image was concordantly associated with kitschiness. For participants from Serbia and Slovenia, threatening content was correlated with kitschiness, while participants from Bavaria rated determinate items as kitschier. Results are discussed with regard to literature on kitsch and implications for future research

Rethink reporting of evaluation results in AI.

Aggregate metrics and lack of access to results limit understanding