Artificial Intelligence is no new concept. The phrase was first coined by John McCarthy in 1956[1], when he invited a group of researchers to discuss the notion of'thinking machines' during a conference at Dartmouth College. Since then, it has been a point of fascination for scientists, academics, software developers, and moviemakers alike. Fast-forward to today where you'll find lots of examples hiding in plain sight. From digital assistants like Amazon's Alexa or Apple's Siri, who use AI to learn from user interactions, to automated email responses and search engines predicting what you're looking for.

Artificial intelligence (AI) and automation are continually changing the way we do business. Organisations across all industries and sectors are deploying machine learning and NLP (natural language processing) technologies to automate processes in almost every part of their operation. For businesses, AI means improving efficiencies, amplifying productivity and reducing cost. But while there are many advantages, AI also presents a wide range of legal challenges – especially in areas such as regulatory compliance, liability, risk, privacy and ethics. To compound matters, regulation of AI is slow to develop, leaving businesses with no choice but to navigate the unknown.

The United States announced a new alliance Wednesday with Australia and Britain to strengthen military capabilities in the face of growing rivalry with China, including a new Australian nuclear submarine fleet and cruise missiles. The announcement of the alliance -- made in a video meeting by President Joe Biden, Australian Prime Minister Scott Morrison and his British counterpart Boris Johnson -- is sure to raise hackles in Beijing. It also met with swift pushback from France, which has been negotiating a multi-billion-dollar sale of conventional submarines to Australia. Biden said the work to enable Australia to build nuclear-powered submarines would ensure that they had "the most modern capabilities we need to maneuver and defend against rapidly evolving threats." The submarines, stressed Biden and the other leaders, will not be nuclear armed, only powered with nuclear reactors.

Queensland police are preparing to begin trials of an artificial intelligence system to identify high-risk domestic violence offenders, and officers intend to use the data to "knock on doors" before serious escalation. The "actuarial tool" uses data from the police Qprime computer system to develop a risk assessment of all potential domestic and family violence offenders. The algorithm has been in development for about three years and practical trials will begin in some police districts before the end of 2021. "With these perpetrators, we will not wait for a triple-zero phone call and for a domestic and family violence incident to reach the point of crisis," acting Supt Ben Martain said. "Rather, with this cohort of perpetrators, who our predictive analytical tools tell us are most likely to escalate into further DFV offending, we are proactively knocking on doors without any call for service."

Over the last few years, the concept of Artificial Intelligence has become central in different tasks concerning both our daily life and several working scenarios. Among these tasks automated planning has always been central in the AI research community. In particular, this manuscript is focused on a specialized kind of planning known as Multi-agent Epistemic Planning (MEP). Epistemic Planning (EP) refers to an automated planning setting where the agent reasons in the space of knowledge/beliefs states and tries to find a plan to reach a desirable state from a starting one. Its general form, the MEP problem, involves multiple agents who need to reason about both the state of the world and the information flows between agents. To tackle the MEP problem several tools have been developed and, while the diversity of approaches has led to a deeper understanding of the problem space, each proposed tool lacks some abilities and does not allow for a comprehensive investigation of the information flows. That is why, the objective of our work is to formalize an environment where a complete characterization of the agents' knowledge/beliefs interaction and update is possible. In particular, we aim to achieve such goal by defining a new action-based language for multi-agent epistemic planning and to implement an epistemic planner based on it. This solver should provide a tool flexible enough to reason on different domains, e.g., economy, security, justice and politics, where considering others' knowledge/beliefs could lead to winning strategies.

This research is aimed to propose an artificial intelligence algorithm comprising an ontology-based design, text mining, and natural language processing for automatically generating gap-fill multiple choice questions (MCQs). The simulation of this research demonstrated an application of the algorithm in generating gap-fill MCQs about software testing. The simulation results revealed that by using 103 online documents as inputs, the algorithm could automatically produce more than 16 thousand valid gap-fill MCQs covering a variety of topics in the software testing domain. Finally, in the discussion section of this paper we suggest how the proposed algorithm should be applied to produce gap-fill MCQs being collected in a question pool used by a knowledge expert system.

Aspect-based sentiment analysis (ABSA) is an emerging fine-grained sentiment analysis task that aims to extract aspects, classify corresponding sentiment polarities and find opinions as the causes of sentiment. The latest research tends to solve the ABSA task in a unified way with end-to-end frameworks. Yet, these frameworks get fine-tuned from downstream tasks without any task-adaptive modification. Specifically, they do not use task-related knowledge well or explicitly model relations between aspect and opinion terms, hindering them from better performance. In this paper, we propose SentiPrompt to use sentiment knowledge enhanced prompts to tune the language model in the unified framework. We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets. Experimental results demonstrate that our approach can outperform strong baselines on Triplet Extraction, Pair Extraction, and Aspect Term Extraction with Sentiment Classification by a notable margin.

Weighted knowledge bases for description logics with typicality have been recently considered under a "concept-wise" multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of Multilayer Perceptrons. In this paper we consider weighted conditional EL^bot knowledge bases in the two-valued case, and exploit ASP and asprin for encoding concept-wise multipreference entailment for weighted KBs with integer weights.

The importance and pervasiveness of emotions in our lives makes affective computing a tremendously important and vibrant line of work. Systems for automatic emotion recognition (AER) and sentiment analysis can be facilitators of enormous progress (e.g., in improving public health and commerce) but also enablers of great harm (e.g., for suppressing dissidents and manipulating voters). Thus, it is imperative that the affective computing community actively engage with the ethical ramifications of their creations. In this paper, I have synthesized and organized information from AI Ethics and Emotion Recognition literature to present fifty ethical considerations relevant to AER. Notably, the sheet fleshes out assumptions hidden in how AER is commonly framed, and in the choices often made regarding the data, method, and evaluation. Special attention is paid to the implications of AER on privacy and social groups. The objective of the sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems. Additionally, the sheet acts as a useful introductory document on emotion recognition (complementing survey articles).

When humans conceive how to perform a particular task, they do so hierarchically: splitting higher-level tasks into smaller sub-tasks. However, in the literature on natural language (NL) command of situated agents, most works have treated the procedures to be executed as flat sequences of simple actions, or any hierarchies of procedures have been shallow at best. In this paper, we propose a formalism of procedures as programs, a powerful yet intuitive method of representing hierarchical procedural knowledge for agent command and control. We further propose a modeling paradigm of hierarchical modular networks, which consist of a planner and reactors that convert NL intents to predictions of executable programs and probe the environment for information necessary to complete the program execution. We instantiate this framework on the IQA and ALFRED datasets for NL instruction following. Our model outperforms reactive baselines by a large margin on both datasets. We also demonstrate that our framework is more data-efficient, and that it allows for fast iterative development.