The widespread diffusion of medical and political claims in the wake of COVID-19 has led to a voluminous rise in misinformation and fake news. The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation. However, the rate of information dissemination is such that it vastly outpaces the fact-checkers' strength. Therefore, to aid manual fact-checkers in eliminating the superfluous content, it becomes imperative to automatically identify and extract the snippets of claim-worthy (mis)information present in a post. In this work, we introduce the novel task of Claim Span Identification (CSI). We propose CURT, a large-scale Twitter corpus with token-level claim spans on more than 7.5k tweets. Furthermore, along with the standard token classification baselines, we benchmark our dataset with DABERTa, an adapter-based variation of RoBERTa. The experimental results attest that DABERTa outperforms the baseline systems across several evaluation metrics, improving by about 1.5 points. We also report detailed error analysis to validate the model's performance along with the ablation studies. Lastly, we release our comprehensive span annotation guidelines for public use.

Recently, increasing attention has been paid to heterogeneous graph representation learning (HGRL), which aims to embed rich structural and semantic information in heterogeneous information networks (HINs) into low-dimensional node representations. To date, most HGRL models rely on hand-crafted meta-paths. However, the dependency on manually-defined meta-paths requires domain knowledge, which is difficult to obtain for complex HINs. More importantly, the pre-defined or generated meta-paths of all existing HGRL methods attached to each node type or node pair cannot be personalised to each individual node. To fully unleash the power of HGRL, we present a novel framework, Personalised Meta-path based Heterogeneous Graph Neural Networks (PM-HGNN), to jointly generate meta-paths that are personalised for each individual node in a HIN and learn node representations for the target downstream task like node classification. Precisely, PM-HGNN treats the meta-path generation as a Markov Decision Process and utilises a policy network to adaptively generate a meta-path for each individual node and simultaneously learn effective node representations. The policy network is trained with deep reinforcement learning by exploiting the performance improvement on a downstream task. We further propose an extension, PM-HGNN++, to better encode relational structure and accelerate the training during the meta-path generation. Experimental results reveal that both PM-HGNN and PM-HGNN++ can significantly and consistently outperform 16 competing baselines and state-of-the-art methods in various settings of node classification. Qualitative analysis also shows that PM-HGNN++ can identify meaningful meta-paths overlooked by human knowledge.

Research in music and AI has never been so prolific nor so exciting. However, a large majority of research is kept in university laboratories, with little connection to the outside world. I decided to have a deeper look to see what current AI jazz improvisation programs or applications exist which could help teachers and practitioners develop improvisational skills in their students and themselves. AI is still seen as something mysterious within music circles, so I hope as time goes by, to dispel some of this and to inform musicians/teachers on current AI realities. Indeed, it is becoming increasingly important for the wider world to become involved in discussions around these technologies, and to ensure that AI develops in directions that will serve people, rather than dominate or eliminate them.

AI can enhance automation by generating faster and more personalized processes, greater use and accuracy in data, and improvements in overall customer experience. Depending on who you talk to, artificial intelligence (AI) and automation are either a godsend or a curse. They will either be the salvation of humanity or will lead us into a dystopian future, where machines and robots replace humans for everything. The movie "I, Robot" highlighted some of the common fears that AI and automation may elicit. Robots took care of more and more functions such as cooking, cleaning houses, driving, garbage collection, and more.

Early Stage Researcher (f/m/d) in the areas of Machine Learning, Speech Processing, and Neural Infor in Computer Science, Research Related with …

… adopt data-centricity, and get us to artificial intelligence, machine learning and [figuring] out where the right applications of that are so …

It's no secret that fake news and disinformation are threatening our society. How is any discourse possible when we can't agree on a baseline of truth? With the scale of today's (social) media, it's impossible to keep track of everything manually. Clearly, the only way to solve fake news is through automation. In the world of AI, fake news detection has been a hot topic for a few years now.

Sensorium, a leading developer in the metaverse space, is joining GITEX GLOBAL for the second year in a row. The company has prepared a range of thrilling activities for all visitors to discover and experience the Sensorium Galaxy metaverse ahead of its public launch. As a key participant of X-Verse, a new GITEX event dedicated to immersive technologies and the Web 3.0 industry, Sensorium will showcase some of its latest innovations in the field of extended reality and artificial intelligence with an interactive multi-day program. Sasha Tityanko, Deputy CEO & Art Director at Sensorium: "GITEX GLOBAL is one of the most influential events for the tech sector, and we are delighted to once again contribute to its outstanding agenda. This year, we wanted to take the metaverse one step closer to everyone, ensuring all visitors can experience the different technological advancements that come together at Sensorium Galaxy. From remarkably natural conversations with AI beings to the high-quality generative music created by our virtual artists and the striking visuals available all around the metaverse. With our program this year, we intend to reshape people's vision of the metaverse and set new standards for the creation of future virtual environments."

AI-written books are now an incoming reality. But can they write the next great bestseller? AI is already writing music, creating pictures for graphic novels, and winning art competitions, beating humans. One of the first experimental AI-written novels turned up as early as 2017. Called 1 the Road, it was an experiment by Ross Goodwin.

Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Manual annotation of question and answers for videos, however, is tedious and prohibits scalability. To tackle this problem, recent methods consider zero-shot settings with no manual annotation of visual question-answer. In particular, a promising approach adapts frozen autoregressive language models pretrained on Web-scale text-only data to multi-modal inputs. In contrast, we here build on frozen bidirectional language models (BiLM) and show that such an approach provides a stronger and cheaper alternative for zero-shot VideoQA. In particular, (i) we combine visual inputs with the frozen BiLM using light trainable modules, (ii) we train such modules using Web-scraped multi-modal data, and finally (iii) we perform zero-shot VideoQA inference through masked language modeling, where the masked text is the answer to a given question. Our proposed approach, FrozenBiLM, outperforms the state of the art in zero-shot VideoQA by a significant margin on a variety of datasets, including LSMDC-FiB, iVQA, MSRVTT-QA, MSVD-QA, ActivityNet-QA, TGIF-FrameQA, How2QA and TVQA. It also demonstrates competitive performance in the few-shot and fully-supervised setting. Our code and models are publicly available at https://github.com/antoyang/FrozenBiLM.