Textual adversarial samples play important roles in multiple subfields of NLP research, including security, evaluation, explainability, and data augmentation. However, most work mixes all these roles, obscuring the problem definitions and research goals of the security role that aims to reveal the practical concerns of NLP models. In this paper, we rethink the research paradigm of textual adversarial samples in security scenarios. We discuss the deficiencies in previous work and propose our suggestions that the research on the Security-oriented adversarial NLP (SoadNLP) should: (1) evaluate their methods on security tasks to demonstrate the real-world concerns; (2) consider real-world attackers' goals, instead of developing impractical methods. To this end, we first collect, process, and release a security datasets collection Advbench. Then, we reformalize the task and adjust the emphasis on different goals in SoadNLP. Next, we propose a simple method based on heuristic rules that can easily fulfill the actual adversarial goals to simulate real-world attack methods. We conduct experiments on both the attack and the defense sides on Advbench. Experimental results show that our method has higher practical value, indicating that the research paradigm in SoadNLP may start from our new benchmark. All the code and data of Advbench can be obtained at \url{https://github.com/thunlp/Advbench}.

Multi-aspect controllable text generation is a more challenging and practical task than single-aspect control. Existing methods achieve complex multi-aspect control by fusing multiple controllers learned from single-aspect, but suffer from attribute degeneration caused by the mutual interference of these controllers. To address this, we provide observations on attribute fusion from a distributional perspective and propose to directly search for the intersection areas of multiple attribute distributions as their combination for generation. Our method first estimates the attribute space with an autoencoder structure. Afterward, we iteratively approach the intersections by jointly minimizing distances to points representing different attributes. Finally, we map them to attribute-relevant sentences with a prefix-tuning-based decoder. Experiments on the three-aspect control task, including sentiment, topic, and detoxification aspects, reveal that our method outperforms several strong baselines on attribute relevance and text quality and achieves the SOTA. Further analysis also supplies some explanatory support for the effectiveness of our approach.

In this paper, we explore legal argument mining using multiple levels of granularity. Argument mining has usually been conceptualized as a sentence classification problem. In this work, we conceptualize argument mining as a token-level (i.e., word-level) classification problem. We use a Longformer model to classify the tokens. Results show that token-level text classification identifies certain legal argument elements more accurately than sentence-level text classification. Token-level classification also provides greater flexibility to analyze legal texts and to gain more insight into what the model focuses on when processing a large amount of input data.

Multi-modal retrieval is an important problem for many applications, such as recommendation and search. Current benchmarks and even datasets are often manually constructed and consist of mostly clean samples where all modalities are well-correlated with the content. Thus, current video-text retrieval literature largely focuses on video titles or audio transcripts, while ignoring user comments, since users often tend to discuss topics only vaguely related to the video. Despite the ubiquity of user comments online, there is currently no multi-modal representation learning datasets that includes comments. In this paper, we a) introduce a new dataset of videos, titles and comments; b) present an attention-based mechanism that allows the model to learn from sometimes irrelevant data such as comments; c) show that by using comments, our method is able to learn better, more contextualised, representations for image, video and audio representations.

Strategies based on Explainable Artificial Intelligence - XAI have promoted better human interpretability of the results of black box machine learning models. This sets a precedent for questioning whether or not human expectations are being met when faced with the explanations of this type of model. The XAI measures being currently used (Ciu, Dalex, Eli5, Lofo, Shap, and Skater) provide various forms of explanations, including global rankings of relevance of attributes, which allow for an overview of how the model is explained as a result of its inputs and outputs. These measures provide for an increase in the explainability of the model and a greater interpretability grounded on the context of the problem. Current research points to the need for further studies (within a specific context/problem) on how these explanations meet the Interpretability Expectations of human experts and how they can be used to make the model even more transparent while taking into account specific complexities of the model and dataset being analyzed, as well as important human factors of sensitive real-world contexts/problems. Intending to shed light on the explanations generated by XAI measures and their interpretabilities, this research addresses a real-world classification problem related to homicide prediction, duly endorsed by the scientific community, replicated its proposed black box model and used 6 different XAI measures to generate explanations and 6 different human experts to generate what this research referred to as Interpretability Expectations - IE. The results were computed by means of comparative analysis and identification of relationships among all the attribute ranks produced, and 49% concordance was found among attributes indicated by means of XAI measures and human experts, 41% exclusively by XAI measures and 10% exclusively by human experts. The results allow for answering questions such as: "Do the different XAI measures generate similar explanations for the proposed problem?", "Are the interpretability expectations generated among different human experts similar?","Do the

In 2020 The Guardian published an article that had been written by AI. It was about the increasing use of AI in journalism, and how it is changing the landscape of the industry. It discussed how AI is being used to generate news stories, and how it is being used to help reporters with their work. It was so natural that it was hard to believe that it was written by a software called GPT-3 developed by OpenAI, a research company. The Guardian isn't the only news organization using algorithms to write articles.

VERSES is Blockchain agnostic which means you can use multiple chains and even operate a hybrid data layer using both DLT technologies and the cloud. On September 21, 2022, VERSES introduced the creation of its new Artificial Intelligence Lab and Sensor Fusion Research Facility, showcasing their technology portfolio, including COSM, and providing an immersive space for data science and product development teams to cultivate advanced adaptive intelligence solutions required for translating diverse data into contextual awareness between humans, machines and AI in physical and digital spaces. Smart Contracts, at the heart of DLTs, are a programmable set of rules, stored on Blockchains, and run when certain pre-determined conditions are met. These automated, self-executing & immutable strings of code are recorded onto Distributed Ledger Blockchains, securing transactions & agreements by replacing static documents and the need for third party mediation. Through intelligent automation, Smart Contracts secure the management of property rights, spatial rights, proof of origination, verifiable traceability, and auto-execution of payments and transfer of assets, providing security, protecting privacy, and allowing risk-free interoperability -- all essential to a favorable and prosperous augmented and networked Web 3.0 experience.

The United States Artificial Intelligence Institute (USAII) is an independent, third-party, international certification institute for Artificial Intelligence, Machine Learning, Deep learning, and related industry. It has no interest in the promotion or marketing of its own or any other affiliates. The USAII's Artificial Intelligence certification is designed to deal with the futuristic issues of data-driven decision-making. Information provided on the official USAII website is for informational purposes only and does not establish any legal contract between the USAII and any other person or entity unless otherwise specified. All the information on USAII's official website is subject to change without any prior notice.

Did you miss a session from MetaBeat 2022? Head over to the on-demand library for all of our featured sessions here. There hasn't been a revolution quite like this before, one that's shaken the talent industry so dramatically over the past few years. The pandemic, the Great Resignation, inflation and now talk of looming recessions are changing talent strategies as we know them. Such significant changes, and the challenge of staying ahead of them, have brought artificial intelligence (AI) to the forefront of the minds of HR leaders and recruitment teams as they endeavor to streamline workflows and identify suitable talent to fill vacant positions faster.

Legl is a fast-growing, vertical B2B SaaS platform with a mission to bring the legal industry into the 21st century. We closed our Series B (May 2022), and we are scaling quickly on our next phase of growth. Our vision is to be part of a structural change in the legal industry so that firms, as well as their clients, have a better experience of accessing legal services. We launched in October 2019 and in the short time since then have built a large law firm customer base who love our product and our team. We have an agile, ambitious and collaborative team.