Probing the ethical implications surrounding the use of AI, Franklin Ritch's debut feature hinges on a high-concept premise: an entirely digital avatar of a young girl named Cherry (Tatum Matthews) is used as bait to trap paedophiles in online chatrooms. Without the signature spectacle of the sci-fi genre, The Artifice Girl is a markedly low-key and small-scale endeavour, steeped in philosophical musings that ultimately seem stagey rather than cinematic. It starts in a police interrogation room where Ritch's Gareth, Cherry's creator, is questioned by Deena (Sinda Nichols) and Amos (David Girard), members of a taskforce combatting child sexual abuse. Once Gareth reveals Cherry is a virtual being, concerns arise as to whether she can meaningfully consent to interacting with men on a daily basis. As Cherry grows increasingly sentient, the same talking points are reiterated in the second section of the film, as Gareth advocates to transfer Cherry's intelligence into a physical form.

The AI-generated song, "Heart On my Sleeve," serves as a great example of the issue, according to Coleman. "It sounds remarkably like [Drake], but it's not his lyrics," the AI educator told Fox News. Coleman, who's experimented with AI since about 2017 in his role as a gaming developer, said he believed the AI software that produced the song "was trained potentially" on Drake's voice. "Everything about the labeling of it online says this was an AI song made using Drake's likeness," Coleman told Fox News. Rapper Drake performs on Dec. 9, 2022 in Atlanta, Georgia.

In many recommender systems and search problems, presenting a well balanced set of results can be an important goal in addition to serving highly relevant content. For example, in a movie recommendation system, it may be helpful to achieve a certain balance of different genres, likewise, it may be important to balance between highly popular versus highly personalized shows. Such balances could be thought across many categories and may be required for enhanced user experience, business considerations, fairness objectives etc. In this paper, we consider the problem of calibrating with respect to any given categories over items. We propose a way to balance a trade-off between relevance and calibration via a Linear Programming optimization problem where we learn a doubly stochastic matrix to achieve optimal balance in expectation. We then realize the learned policy using the Birkhoff-von Neumann decomposition of a doubly stochastic matrix. Several optimizations are considered over the proposed basic approach to make it fast. The experiments show that the proposed formulation can achieve a much better trade-off compared to many other baselines. This paper does not prescribe the exact categories to calibrate over (such as genres) universally for applications. This is likely dependent on the particular task or business objective. The main contribution of the paper is that it proposes a framework that can be applied to a variety of problems and demonstrates the efficacy of the proposed method using a few use-cases.

This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels (causing a lack of labelled data for some language-label combinations), we opted for fine-tuning pre-trained transformer-based language models. Conducting multiple experiments, we find the best configuration, which consists of large multilingual model (XLM-RoBERTa large) trained jointly on all input data, with carefully calibrated confidence thresholds for seen and surprise languages separately. Our final system performed the best on 6 out of 9 languages (including two surprise languages) and achieved highly competitive results on the remaining three languages.

Counterfactual examples explain a prediction by highlighting changes of instance that flip the outcome of a classifier. This paper proposes TIGTEC, an efficient and modular method for generating sparse, plausible and diverse counterfactual explanations for textual data. TIGTEC is a text editing heuristic that targets and modifies words with high contribution using local feature importance. A new attention-based local feature importance is proposed. Counterfactual candidates are generated and assessed with a cost function integrating semantic distance, while the solution space is efficiently explored in a beam search fashion. The conducted experiments show the relevance of TIGTEC in terms of success rate, sparsity, diversity and plausibility. This method can be used in both model-specific or model-agnostic way, which makes it very convenient for generating counterfactual explanations.

New advancements for the detection of synthetic images are critical for fighting disinformation, as the capabilities of generative AI models continuously evolve and can lead to hyper-realistic synthetic imagery at unprecedented scale and speed. In this paper, we focus on the challenge of generalizing across different concept classes, e.g., when training a detector on human faces and testing on synthetic animal images - highlighting the ineffectiveness of existing approaches that randomly sample generated images to train their models. By contrast, we propose an approach based on the premise that the robustness of the detector can be enhanced by training it on realistic synthetic images that are selected based on their quality scores according to a probabilistic quality estimation model. We demonstrate the effectiveness of the proposed approach by conducting experiments with generated images from two seminal architectures, StyleGAN2 and Latent Diffusion, and using three different concepts for each, so as to measure the cross-concept generalization ability. Our results show that our quality-based sampling method leads to higher detection performance for nearly all concepts, improving the overall effectiveness of the synthetic image detectors.

Neural models for abstractive summarization tend to generate output that is fluent and well-formed but lacks semantic faithfulness, or factuality, with respect to the input documents. In this paper, we analyze the tradeoff between abstractiveness and factuality of generated summaries across multiple datasets and models, using extensive human evaluations of factuality. In our analysis, we visualize the rates of change in factuality as we gradually increase abstractiveness using a decoding constraint, and we observe that, while increased abstractiveness generally leads to a drop in factuality, the rate of factuality decay depends on factors such as the data that the system was trained on. We introduce two datasets with human factuality judgements; one containing 10.2k generated summaries with systematically varied degrees of abstractiveness; the other containing 4.2k summaries from five different summarization models. We propose new factuality metrics that adjust for the degree of abstractiveness, and we use them to compare the abstractiveness-adjusted factuality of previous summarization works, providing baselines for future work.

Computational creativity has contributed heavily to abstract art in modern era, allowing artists to create high quality, abstract two dimension (2D) arts with a high level of controllability and expressibility. However, even with computational approaches that have promising result in making concrete 3D art, computationally addressing abstract 3D art with high-quality and controllability remains an open question. To fill this gap, we propose to explore computational creativity in making abstract 3D art by bridging evolution strategies (ES) and 3D rendering through customizable parameterization of scenes. We demonstrate that our approach is capable of placing semi-transparent triangles in 3D scenes that, when viewed from specified angles, render into films that look like artists' specification expressed in natural language. This provides a new way for the artist to easily express creativity ideas for abstract 3D art. The supplementary material, which contains code, animation for all figures, and more examples, is here: https://es3dart.github.io/

EXCLUSIVE: An AI whizz has reimagined the Duke and Duchess of Sussex starring in a range of romantic comedies from Notting Hill to Love Actually.

When the boss of Google admits to losing sleep over the negative potential of artificial intelligence, perhaps it is time to get worried. Sundar Pichai told the CBS programme 60 Minutes this month that AI could be "very harmful" if deployed wrongly, and was developing fast. "So does that keep me up at night? Google has launched Bard, a chatbot to rival the ChatGPT phenomenon, and its parent, Alphabet, owns the world-leading DeepMind, a UK-based AI company. He is not the only AI insider to voice concerns. Last week, Elon Musk said he had fallen out with the Google co-founder Larry Page because Page was "not taking AI safety seriously enough". Musk told Fox News that Page wanted "digital superintelligence, basically a digital god, if you will, as soon as possible". So how much of a danger is posed by unrestrained AI development? Musk is one of thousands of signatories to a letter published by the Future of Life Institute, a thinktank, that called for a six-month moratorium on the creation of "giant" AIs more powerful than GPT-4, the system that underpins ChatGPT and the chatbot integrated with Microsoft's Bing search engine. The risks cited by the letter include "loss of control of our civilization". The approach to product development shown by AI practitioners and the tech industry would not be tolerated in any other field, said Valérie Pisano, another signatory to the letter. Pisano, the chief executive of Mila – the Quebec Artificial Intelligence Institute – says work was being carried out to make sure that these systems were not racist or violent, in a process known as alignment (ie, making sure they "align" with human values). But then they were released into the public realm. "The technology is put out there, and as the system interacts with humankind, its developers wait to see what happens and make adjustments based on that.