Artificial intelligence versus Maya Angelou:Experimental evidence that people cannot differentiate AI-generated from human-written poetry

The release of openly available, robust natural language generation algorithms (NLG) has spurred much public attention and debate. One reason lies in the algorithms' purported ability to generate human-like text across various domains. Empirical evidence using incentivized tasks to assess whether people (a) can distinguish and (b) prefer algorithm-generated versus human-written text is lacking. We conducted two experiments assessing behavioral reactions to the state-of-the-art Natural Language Generation algorithm GPT-2 (Ntotal = 830). Using the identical starting lines of human poems, GPT-2 produced samples of poems. From these samples, either a random poem was chosen (Human-out-of-the-loop) or the best one was selected (Human-in-the-loop) and in turn matched with a human-written poem. In a new incentivized version of the Turing Test, participants failed to reliably detect the algorithmically-generated poems in the Human-in-the-loop treatment, yet succeeded in the Human-out-of-the-loop treatment. Further, people reveal a slight aversion to algorithm-generated poetry, independent on whether participants were informed about the algorithmic origin of the poem (Transparency) or not (Opacity). We discuss what these results convey about the performance of NLG algorithms to produce human-like text and propose methodologies to study such learning algorithms in human-agent experimental settings.Comment: Computers in Human Behavior 202

The corruptive force of AI-generated advice

Artificial Intelligence (AI) is increasingly becoming a trusted advisor in people's lives. A new concern arises if AI persuades people to break ethical rules for profit. Employing a large-scale behavioural experiment (N = 1,572), we test whether AI-generated advice can corrupt people. We further test whether transparency about AI presence, a commonly proposed policy, mitigates potential harm of AI-generated advice. Using the Natural Language Processing algorithm, GPT-2, we generated honesty-promoting and dishonesty-promoting advice. Participants read one type of advice before engaging in a task in which they could lie for profit. Testing human behaviour in interaction with actual AI outputs, we provide first behavioural insights into the role of AI as an advisor. Results reveal that AI-generated advice corrupts people, even when they know the source of the advice. In fact, AI's corrupting force is as strong as humans'.Comment: Leib & K\"obis share first authorshi

Strange nucleon form factors in the perturbative chiral quark model

We apply the perturbative chiral quark model at one loop to calculate the strange form factors of the nucleon. A detailed numerical analysis of the strange magnetic moments and radii of the nucleon, and also the momentum dependence of the form factors is presented.Comment: 18 pages, 6 figure

Unifying local-global type properties in vector optimization.

It is well-known that all local minimum points of a semistrictly quasiconvex real-valued function are global minimum points. Also, any local maximum point of an explicitly quasiconvex real-valued function is a global minimum point, provided that it belongs to the intrinsic core of the function’s domain. The aim of this paper is to show that these “local min - global min” and “local max - global min” type properties can be extended and unified by a single general localglobal extremality principle for certain generalized convex vector-valued functions with respect to two proper subsets of the outcome space. For particular choices of these two sets, we recover and refine several local-global properties known in the literature, concerning unified vector optimization (where optimality is defined with respect to an arbitrary set, not necessarily a convex cone) and, in particular, classical vector/multicriteria optimization.Nicolae Popovici’s research was supported by a grant of the Romanian Ministry of Research and Innovation, CNCS-UEFISCDI, project number PN-III-P4-ID-PCE- 2016-0190, within PNCDI III