Recent work proposed $\delta$-relevant inputs (or sets) as a probabilistic explanation for the predictions made by a classifier on a given input. $\delta$-relevant sets are significant because they serve to relate (model-agnostic) Anchors with (model-accurate) PI- explanations, among other explanation approaches. Unfortunately, the computation of smallest size $\delta$-relevant sets is complete for ${NP}^{PP}$, rendering their computation largely infeasible in practice. This paper investigates solutions for tackling the practical limitations of $\delta$-relevant sets. First, the paper alternatively considers the computation of subset-minimal sets. Second, the paper studies concrete families of classifiers, including decision trees among others. For these cases, the paper shows that the computation of subset-minimal $\delta$-relevant sets is in NP, and can be solved with a polynomial number of calls to an NP oracle. The experimental evaluation compares the proposed approach with heuristic explainers for the concrete case of the classifiers studied in the paper, and confirms the advantage of the proposed solution over the state of the art.

The paper investigates the properties of a fuzzy logic of typicality. The extension of fuzzy logic with a typicality operator was proposed in recent work to define a fuzzy multipreference semantics for Multilayer Perceptrons, by regarding the deep neural network as a conditional knowledge base. In this paper, we study its properties. First, a monotonic extension of a fuzzy ALC with typicality is considered (called ALCFT) and a reformulation the KLM properties of a preferential consequence relation for this logic is devised. Most of the properties are satisfied, depending on the reformulation and on the fuzzy combination functions considered. We then strengthen ALCFT with a closure construction by introducing a notion of faithful model of a weighted knowledge base, which generalizes the notion of coherent model of a conditional knowledge base previously introduced, and we study its properties.

A product built to shuffle characters and events and generate narrative possibilities in real time, dancers using it brought a new version of the classic tragedy to life. The one-off production, R J RMX, was filmed for the Opera House's streaming platform. The "remix" was interactive: audience members were sent to a website where they could restructure the play with the touch of a button, while on stage narrators and dancers ran through numerous renditions of the story. The works of Shakespeare, surely more than those of any other writer, have been subject to interminable reworkings, as if we are at once infinitely fascinated and infinitely dissatisfied with the source material. So how does technology alter this process?

The quest for this Holy Grail of a universal Code of ethics in AI has left in its wake a remarkable, if not worrying, quantity of projects aiming to establish a corpus of ethical standards to frame its development. But it is vital that we question the basis on which this corpus is established. And the fast-increasing number of initiatives requiring this tool makes the necessity of ensuring the basis all the more urgent. We must ask two fundamental questions. Is it possible to create one single tool for everything and is there a real widespread desire to create such a tool?

It is one of the more unlikely consequences of the pandemic: a plague of dog shit, with no obvious solution in sight. This story originally appeared on WIRED UK. The rise is down to the sheer number of potential pet owners that rushed to realize the dream of owning a dog while in lockdown. Such was the demand, the price of puppies in the UK more than doubled last year, with popular breeds selling for more than £3,000 a pup. And with such money came the thieves and fraudsters.

Google has developed a new tool that uses AI to help people identify common skin conditions. Every year, people reach out to Google Search almost 10 billion times to ask questions about skin, nails, and hair. While the information is there, it remains difficult for many to precisely describe the visible symptoms with words alone. Statistics show that over two billion people across the globe are affected by dermatological issues, and there is a global shortage of specialists. That's why Google developed the AI-powered dermatology assist tool – a web-based application that works with the camera on your phone.

The federal government should establish an AI Safety Commissioner and halt the use of facial recognition and algorithms in important decision-making until adequate protections are in place, the Australian Human Rights Commission has concluded after a three-year investigation. The Australian Human Rights Commission's (AHRC) report on Human Rights and Technology was tabled in Parliament on Thursday afternoon, with 38 recommendations to the government on ensuring human rights are upheld in the laws, policies, funding and education on artificial intelligence. Human Rights Commissioner Ed Santow has urged local, state, territory and federal governments to put on hold the use of facial recognition and AI in decision-making that has a significant impact on individuals. This moratorium should be until adequate legislation is in place that regulates the use of these technologies and ensures human rights are protected. The use of automation and algorithms in government decision-making should also be paused until a range of protections and transparency measures are in place, Mr Santow said in the report.

This paper explores character-driven story continuation, in which the story emerges through characters' first- and second-person narration as well as dialogue -- requiring models to select language that is consistent with a character's persona and their relationships with other characters while following and advancing the story. We hypothesize that a multi-task model that trains on character dialogue plus character relationship information improves transformer-based story continuation. To this end, we extend the Critical Role Dungeons and Dragons Dataset (Rameshkumar and Bailey, 2020) -- consisting of dialogue transcripts of people collaboratively telling a story while playing the role-playing game Dungeons and Dragons -- with automatically extracted relationships between each pair of interacting characters as well as their personas. A series of ablations lend evidence to our hypothesis, showing that our multi-task model using character relationships improves story continuation accuracy over strong baselines.

Span-extraction reading comprehension models have made tremendous advances enabled by the availability of large-scale, high-quality training datasets. Despite such rapid progress and widespread application, extractive reading comprehension datasets in languages other than English remain scarce, and creating such a sufficient amount of training data for each language is costly and even impossible. An alternative to creating large-scale high-quality monolingual span-extraction training datasets is to develop multilingual modeling approaches and systems which can transfer to the target language without requiring training data in that language. In this paper, in order to solve the scarce availability of extractive reading comprehension training data in the target language, we propose a multilingual extractive reading comprehension approach called XLRC by simultaneously modeling the existing extractive reading comprehension training data in a multilingual environment using self-adaptive attention and multilingual attention. Specifically, we firstly construct multilingual parallel corpora by translating the existing extractive reading comprehension datasets (i.e., CMRC 2018) from the target language (i.e., Chinese) into different language families (i.e., English). Secondly, to enhance the final target representation, we adopt self-adaptive attention (SAA) to combine self-attention and inter-attention to extract the semantic relations from each pair of the target and source languages. Furthermore, we propose multilingual attention (MLA) to learn the rich knowledge from various language families. Experimental results show that our model outperforms the state-of-the-art baseline (i.e., RoBERTa_Large) on the CMRC 2018 task, which demonstrate the effectiveness of our proposed multi-lingual modeling approach and show the potentials in multilingual NLP tasks.

In this work we study how to learn good algorithms for selecting reasoning steps in theorem proving. We explore this in the connection tableau calculus implemented by leanCoP where the partial tableau provides a clean and compact notion of a state to which a limited number of inferences can be applied. We start by incorporating a state-of-the-art learning algorithm -- a graph neural network (GNN) -- into the plCoP theorem prover. Then we use it to observe the system's behaviour in a reinforcement learning setting, i.e., when learning inference guidance from successful Monte-Carlo tree searches on many problems. Despite its better pattern matching capability, the GNN initially performs worse than a simpler previously used learning algorithm. We observe that the simpler algorithm is less confident, i.e., its recommendations have higher entropy. This leads us to explore how the entropy of the inference selection implemented via the neural network influences the proof search. This is related to research in human decision-making under uncertainty, and in particular the probability matching theory. Our main result shows that a proper entropy regularisation, i.e., training the GNN not to be overconfident, greatly improves plCoP's performance on a large mathematical corpus.