Objective: Reveal and quantify qualities of reported experiences of chronic pain on social media, from multiple pathological backgrounds, by means of the novel Reddit Reports of Chronic Pain (RRCP) dataset, using Natural Language Processing techniques. Materials and Methods: Define and validate the RRCP dataset for a set of subreddits related to chronic pain. Identify the main concerns discussed in each subreddit. Model each subreddit according to their main concerns. Compare subreddit models. Results: The RRCP dataset comprises 86,537 Reddit submissions from 12 subreddits related to chronic pain (each related to one pathological background). Each RRCP subreddit has various main concerns. Some of these concerns are shared between multiple subreddits (e.g., the subreddit Sciatica semantically entails the subreddit backpain in their various concerns, but not the other way around), whilst some concerns are exclusive to specific subreddits (e.g., Interstitialcystitis and CrohnsDisease). Discussion: These results suggest that the reported experience of chronic pain, from multiple pathologies (i.e., subreddits), has concerns relevant to all, and concerns exclusive to certain pathologies. Our analysis details each of these concerns and their similarity relations. Conclusion: Although limited by intrinsic qualities of the Reddit platform, to the best of our knowledge, this is the first research work attempting to model the linguistic expression of various chronic pain-inducing pathologies and comparing these models to identify and quantify the similarities and differences between the corresponding emergent chronic pain experiences.

Data, algorithms and compute (the main elements of AI) have advanced rapidly over the past two decades and are being deployed in people's lives in disruptive ways -- for good and ill -- without much regulation or, until recently, community deliberation. The fallout is considerable, and many concerns have been raised over harms of AI algorithms in society to individuals or groups. A recent example is a white paper by the American Civil Liberties Union (ACLU) entitled "AI in healthcare may worsen medical racism"4. The paper discusses several studies that inadvertently used biased data and machine learning models to make harmful medical decisions. In recent years, the machine learning community has called for participatory approaches whereby those whose life is impacted by algorithms have a major role in their development5.

NEW YORK--(BUSINESS WIRE)--While the majority of organizations that use artificial intelligence (AI) are still experimenting with the technology, only 12% are using it at an AI maturity level that achieves a strong competitive advantage, according to new global research from Accenture (NYSE: ACN). "The Art of AI Maturity: Advancing from Practice to Performance" uncovers strategies for AI success through a holistic framework, which includes a new index to express company AI maturity on a 0-100 scale. According to the research, AI maturity is the degree to which organizations outperform their peers in a combination of AI-related foundational and differentiating capabilities. These capabilities include the technology -- data, AI, cloud -- as well as organizational strategy, Responsible AI, C-suite sponsorship, talent and culture. The research puts the median AI maturity of organizations at a moderate score of 36, revealing most companies have significant opportunities to generate greater value with AI.

Reveal-Brainspace announced that Intellectual Data, an eDiscovery service provider in Korea, will be integrating Reveal's AI-powered eDiscovery, review & investigations platform – Reveal 11 – onto its suite of enterprise cloud services for legal and corporate entities throughout the region. Specifically, Intellectual Data will leverage Reveal's end-to-end, SaaS-based platform to offer eDiscovery hosting, business process optimization and consulting services to its clients – all underpinned by advanced AI and machine learning technology. "As the first partner of Reveal in Korea, we look forward to collaborating on evolving the service to mitigate the risk factors blocking global growth of our clients." "Korea is a lynchpin in Reveal's strategic growth initiative in the APAC region, which makes the partnership with one of Korea's most respected eDiscovery service providers even more significant," said Wendell Jisa, CEO of Reveal. "We're thrilled to work with the talented team at Intellectual Data to provide to expand the reach of our Reveal 11 platform to the growing network of law firms and corporations in Korea looking to solve their most complex challenges with leading AI and review technology."

Football fans now have only a few more days of waiting to endure before the men's FIFA World Cup finally commences in Qatar. After an agonising four-and-a-half-year gap since the last tournament, the host nation will kick off Qatar 2022 on Sunday against Ecuador in Al Khor. England, meanwhile, play their fist match against Iran the following day, as Gareth Southgate's men seek to finally bring it home after 56 years of hurt at the World Cup final on December 18. This year, players and fans alike will see a host of new technologies that have never been seen at a FIFA World Cup. Here's a look at the innovations at Qatar 2022, from AI-powered limb-tracking to a demountable stadium inspired by Lego.

The new analysis was done by using deep convolutional neural networks (DCNNs) – computing systems based on neural networks of the brain – to track the fossils of these prehistoric reptiles. The study was conducted by Dr. Anthony Romilio, a paleontologist at the University of Queensland, and his team of team from Australia, the U.K. and Germany. For decades, scientists thought the giant footprints left by the dinosaurs were from a carnivorous dinosaur. "Large dinosaur footprints were first discovered back in the 1970s at a track site called the Dinosaur Stampede National Monument, and for many years they were believed to be left by a predatory dinosaur, like Australovenator, with legs nearly two meters long, said Dr. Romilio. The discovery, using artificial intelligence, leads researchers to believe that the dinosaur was actually a friendly plant-eating prehistoric reptile. Dr. Romilio mentioned the difficulty in tracing the footprints from millions of years ago to determine the truth of what kind of dinosaur it really was. "The mysterious tracks were thought to be left during the mid-Cretaceous Period, around 93 million years ago," he stated. But working out what dino species made the footprints exactly--especially from tens of millions of years ago--can be a pretty difficult and confusing business."

Through ML we can address SE problems that cannot be completely algorithmically modeled, or for which existing solutions do not provide satisfactory results yet (e.g., defect/fault detection [16, 165, 180]). In addition, ML finds application in SE tasks where data cannot be easily analyzed with other algorithms (e.g., software requirements, code comments, code reviews, issues [9, 91, 174]). Another important aspect of ML is that it can significantly reduce manual effort in common SE tasks (e.g., automatic program repair [157], code suggestion [61], defect prediction [19], malware detection [147], feature location [40]) with great accuracy results [146, 164]. In fields such as health informatics ML and SE are considered complementary disciplines, since the growing scale and complexity of healthcare datasets have posed a challenge for clinical practice and medical research, requiring new engineering approaches from both fields [38]. In the early nineties, Huff and Selfridge [68] recognized the need for creating software systems that partially take some responsibility for their own evolution, offering the ability to implement, measure, and assess changes easily. These changes should also contribute to the overall improvement of the corresponding systems [142].

Public conversations on Twitter comprise many pertinent topics including disasters, protests, politics, propaganda, sports, climate change, epidemics/pandemic outbreaks, etc., that can have both regional and global aspects. Spatial discourse analysis rely on geographical data. However, today less than 1% of tweets are geotagged; in both cases--point location or bounding place information. A major issue with tweets is that Twitter users can be at location A and exchange conversations specific to location B, which we call the Location A/B problem. The problem is considered solved if location entities can be classified as either origin locations (Location As) or non-origin locations (Location Bs). In this work, we propose a simple yet effective framework--the True Origin Model--to address the problem that uses machine-level natural language understanding to identify tweets that conceivably contain their origin location information. The model achieves promising accuracy at country (80%), state (67%), city (58%), county (56%) and district (64%) levels with support from a Location Extraction Model as basic as the CoNLL-2003-based RoBERTa. We employ a tweet contexualizer (locBERT) which is one of the core components of the proposed model, to investigate multiple tweets' distributions for understanding Twitter users' tweeting behavior in terms of mentioning origin and non-origin locations. We also highlight a major concern with the currently regarded gold standard test set (ground truth) methodology, introduce a new data set, and identify further research avenues for advancing the area.

Emotion recognition is a key attribute for artificial intelligence systems that need to naturally interact with humans. However, the task definition is still an open problem due to the inherent ambiguity of emotions. In this paper, a novel Bayesian training loss based on per-utterance Dirichlet prior distributions is proposed for verbal emotion recognition, which models the uncertainty in one-hot labels created when human annotators assign the same utterance to different emotion classes. An additional metric is used to evaluate the performance by detection test utterances with high labelling uncertainty. This removes a major limitation that emotion classification systems only consider utterances with labels where the majority of annotators agree on the emotion class. Furthermore, a frequentist approach is studied to leverage the continuous-valued "soft" labels obtained by averaging the one-hot labels. We propose a two-branch model structure for emotion classification on a per-utterance basis, which achieves state-of-the-art classification results on the widely used IEMOCAP dataset. Based on this, uncertainty estimation experiments were performed. The best performance in terms of the area under the precision-recall curve when detecting utterances with high uncertainty was achieved by interpolating the Bayesian training loss with the Kullback-Leibler divergence training loss for the soft labels. The generality of the proposed approach was verified using the MSP-Podcast dataset which yielded the same pattern of results.

Short-term memory in standard, general-purpose, sequence-processing recurrent neural networks (RNNs) is stored as activations of nodes or "neurons." Generalising feedforward NNs to such RNNs is mathematically straightforward and natural, and even historical: already in 1943, McCulloch and Pitts proposed this as a surrogate to "synaptic modifications" (in effect, generalising the Lenz-Ising model, the first non-sequence processing RNN architecture of the 1920s). A lesser known alternative approach to storing short-term memory in "synaptic connections" -- by parameterising and controlling the dynamics of a context-sensitive time-varying weight matrix through another NN -- yields another "natural" type of short-term memory in sequence processing NNs: the Fast Weight Programmers (FWPs) of the early 1990s. FWPs have seen a recent revival as generic sequence processors, achieving competitive performance across various tasks. They are formally closely related to the now popular Transformers. Here we present them in the context of artificial NNs as an abstraction of biological NNs -- a perspective that has not been stressed enough in previous FWP work. We first review aspects of FWPs for pedagogical purposes, then discuss connections to related works motivated by insights from neuroscience.