To enhance research on multimodal knowledge base and multimodal information processing, we propose a new task called multimodal entity tagging (MET) with a multimodal knowledge base (MKB). We also develop a dataset for the problem using an existing MKB. In an MKB, there are entities and their associated texts and images. In MET, given a text-image pair, one uses the information in the MKB to automatically identify the related entity in the text-image pair. We solve the task by using the information retrieval paradigm and implement several baselines using state-of-the-art methods in NLP and CV. We conduct extensive experiments and make analyses on the experimental results. The results show that the task is challenging, but current technologies can achieve relatively high performance. We will release the dataset, code, and models for future research.

Multi-armed bandit algorithms like Thompson Sampling can be used to conduct adaptive experiments, in which maximizing reward means that data is used to progressively assign more participants to more effective arms. Such assignment strategies increase the risk of statistical hypothesis tests identifying a difference between arms when there is not one, and failing to conclude there is a difference in arms when there truly is one (Rafferty et al., 2019). We present simulations for 2-arm experiments that explore two algorithms that combine the benefits of uniform randomization for statistical analysis, with the benefits of reward maximization achieved by Thompson Sampling (TS). First, Top-Two Thompson Sampling (Russo, 2016) adds a fixed amount of uniform random allocation (UR) spread evenly over time. Second, a novel heuristic algorithm, called TS PostDiff (Posterior Probability of Difference). TS PostDiff takes a Bayesian approach to mixing TS and UR: the probability a participant is assigned using UR allocation is the posterior probability that the difference between two arms is'small' (below a certain threshold), allowing for more UR exploration when there is little or no reward to be gained. We find that TS PostDiff method performs well across multiple effect sizes, and thus does not require tuning based on a guess for the true effect size.

Autonomous driving has achieved a significant milestone in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating vehicles on roads promises safer and more ecologically friendly transportation systems. With the rise of computationally powerful artificial intelligence (AI) techniques, autonomous vehicles can sense their environment with high precision, make safe real-time decisions, and operate more reliably without human interventions. However, intelligent decision-making in autonomous cars is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, the AI systems of autonomous vehicles also need to explain how these decisions are constructed in order to be regulatory compliant across many jurisdictions. Our study sheds a comprehensive light on developing explainable artificial intelligence (XAI) approaches for autonomous vehicles. In particular, we make the following contributions. First, we provide a thorough overview of the present gaps with respect to explanations in the state-of-the-art autonomous vehicle industry. We then show the taxonomy of explanations and explanation receivers in this field. Thirdly, we propose a framework for an architecture of end-to-end autonomous driving systems and justify the role of XAI in both debugging and regulating such systems. Finally, as future research directions, we provide a field guide on XAI approaches for autonomous driving that can improve operational safety and transparency towards achieving public approval by regulators, manufacturers, and all engaged stakeholders.

Renewable energy resources (RERs) have been increasingly integrated into modern power systems, especially in large-scale distribution networks (DNs). In this paper, we propose a deep reinforcement learning (DRL)-based approach to dynamically search for the optimal operation point, i.e., optimal power flow (OPF), in DNs with a high uptake of RERs. Considering uncertainties and voltage fluctuation issues caused by RERs, we formulate OPF into a multi-objective optimization (MOO) problem. To solve the MOO problem, we develop a novel DRL algorithm leveraging the graphical information of the distribution network. Specifically, we employ the state-of-the-art DRL algorithm, i.e., deep deterministic policy gradient (DDPG), to learn an optimal strategy for OPF. Since power flow reallocation in the DN is a consecutive process, where nodes are self-correlated and interrelated in temporal and spatial views, to make full use of DNs' graphical information, we develop a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN) for spatial-temporal graph information extraction, preparing for its sequential DDPG. We validate our proposed DRL-based approach in modified IEEE 33, 69, and 118-bus radial distribution systems (RDSs) and show that our DRL-based approach outperforms other benchmark algorithms. Our experimental results also reveal that MG-ASTGCN can significantly accelerate the DDPG training process and improve DDPG's capability in reallocating power flow for OPF. The proposed DRL-based approach also promotes DNs' stability in the presence of node faults, especially for large-scale DNs.

Task-oriented dialogue systems (TODS) are continuing to rise in popularity as various industries find ways to effectively harness their capabilities, saving both time and money. However, even state-of-the-art TODS are not yet reaching their full potential. TODS typically have a primary design focus on completing the task at hand, so the metric of task-resolution should take priority. Other conversational quality attributes that may point to the success, or otherwise, of the dialogue, may be ignored. This can cause interactions between human and dialogue system that leave the user dissatisfied or frustrated. This paper explores the literature on evaluative frameworks of dialogue systems and the role of conversational quality attributes in dialogue systems, looking at if, how, and where they are utilised, and examining their correlation with the performance of the dialogue system.

The Morrison Government is investing $12 million to strengthen partnerships between the technology sector and regional Australians to solve uniquely Australian challenges. The new Catalysing the Artificial Intelligence Opportunity in Our Regions program will provide competitive grant funding over three rounds to regional organisations for artificial intelligence (AI) projects that deliver benefits to regional industries, businesses and communities. The program is part of the Government's $124.1 million investment under the AI Action Plan, which sets out a vision for Australia to become a global leader in developing and adopting trusted, secure and responsible artificial intelligence. Under round one, grants of between $250,000 and $500,000 are available. Minister for Science and Technology Melissa Price said the program would showcase the opportunities for AI to boost regional capabilities, grow trust in this technology and create a pathway for high-skilled jobs in regional Australia.

Dec 20 (Thomson Reuters Foundation) -Two days after it was sued by Rohingya refugees from Myanmar over allegations that it did not take action against hate speech, social media company Meta, formerly known as Facebook, announced a new artificial intelligence system to tackle harmful content. Machine learning tools have increasingly become the go-to solution for tech firms to police their platforms, but questions have been raised about their accuracy and their potential threat to freedom of speech. WHY ARE SOCIAL MEDIA FIRMS UNDER FIRE OVER CONTENT MODERATION? The $150 billion Rohingya class-action lawsuit filed this month came at the end of a tumultuous period for social media giants, which have been criticised for failing to effectively tackle hate speech online and increasing polarization. The complaint argues that calls for violence shared on Facebook contributed to real-world violence against the Rohingya community, which suffered a military crackdown in 2017 that refugees said included mass killings and rape.

As a lover of tough single player games, I’m quite accustomed to getting my butt handed to me by AI, and usually not even a real one. I also happen to be the owner of a full sized human brain, and though it’s not without its problems, its ability to learn and change is usually why I eventually overcome those difficult in game challenges.So when I read about a few human brain cells in a petri dish that are already performing much better at a videogame than AI can, it’s concerning to me and my gaming future. New Scientist reports that a team in Australia has been growing these small puddles of brain and now one has learnt to play Pong, in fairly impressive time.Cortical labs is a company working on integrating biological neurons with your more traditional silicon based computing hardware. They grow brain cells on microelectronic arrays, so the cells can be stimulated. These hybrid chips are said to be able to learn and restructure themselves to get past problems, like stopping a sneaky ball that wants in your goal.According to Cortical labs, AIs typically take 90 minutes to learn Pong, whereas this ‘DishBrain’ (yes, that’s what it’s called) managed to have it down in five. Though the researchers do note that a good AI would still absolutely demolish the cells, once both properly trained. 

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GENEVA – Japan, the United States and other countries have blocked any advancement in U.N. talks toward legally binding measures to ban and regulate the development and use of lethal autonomous weapon systems. The Sixth Review Conference of the Convention on Certain Conventional Weapons ended Friday in Geneva without progress, failing to reflect eight years of work and leaving countries and nongovernmental organizations that have called for legally binding rules expressing disappointment. Also referred to as "killer robots," autonomous weapons are artificial intelligence-powered weapons using facial recognition and algorithms. Once activated, the weapons can select and attack targets without the assistance of a human operator. They pose ethical, legal and security risks.