Picture this: a small group of middle school students are learning about ancient Egypt, so they strap on a virtual reality headset and, with the assistance of an artificial intelligence tour guide, begin to explore the Pyramids of Giza. The teacher, also journeying to one of the oldest known civilizations via a VR headset, has assigned students to gather information to write short essays. During the tour, the AI guide fields questions from students and points them to specific artifacts and discuss what they see. In preparing the AI-powered lesson on Egypt, the teacher beforehand would have worked with the AI program to craft a lesson plan that not only dives deep into the subject, but figures out how to keep the group moving through the virtual field trip and how to create more equal participation during the discussion. In that scenario, the AI listens, observes and interacts naturally to enhance a group learning experience, and to make a teacher's job easier.

DBIC Ventures, the venture arm of Dublin BIC, today announces details of a co-investment in AI-powered construction software company Evercam, representing the first investment by DBIC Ventures' new seed and early stage fund which is backed by Enterprise Ireland and a number of leading Irish technology entrepreneurs and business leaders. The investment round totalled €600,000 and will support the creation of 50 new jobs and the company's continued strong growth in international markets. It is the first investment by DBIC Ventures' latest fund which plans to back around 30 leading high growth early stage Irish tech companies over the next four years. Elkstone, the Irish multi-family office, is a co-investor in the round. Evercam enhances construction site productivity by improving project visibility with verifiable intelligence from high-resolution time-lapse cameras located on client sites.

If you've spoken to Amazon's Alexa voice assistant through the Alexa app or an Echo device, you may have wondered who the woman is behind the speaker. Do you think that Alexa is voiced by a celebrity or an Amazon employee? You might be surprised to know that the voice of Alexa is not formed from any real person. Rather, Alexa's voice is generated by artificial intelligence. Alexa's voice was developed using special software that evolved from text-to-speech technology.

Vaishak Belle (University of Edinburgh & Alan Turing Institute) and Ioannis Papantonis (University of Edinburgh) which presents a taxonomy of explainable AI (XAI). XAI is a complex subject and as far as I can see, I have not yet seen a taxonomy of XAI. Model-agnostic Explainability Approaches are designed to be flexible and do not depend on the intrinsic architecture of a model(such as Random forest). These approaches solely relate the inputs to the outputs. Model agnistic approaches could be explanation by simplification, explanation by feature relevance or explanation by visualizations.

Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main imaging tools, chest X-rays (CXRs) are common, fast, non-invasive, relatively cheap, and potentially bedside to monitor the progression of the disease. This paper describes the first public COVID-19 image data collection as well as a preliminary exploration of possible use cases for the data. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. It was manually aggregated from publication figures as well as various web based repositories into a machine learning (ML) friendly format with accompanying dataloader code. We collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location.

Temporal relational modeling in video is essential for human action understanding, such as action recognition and action segmentation. Although Graph Convolution Networks (GCNs) have shown promising advantages in relation reasoning on many tasks, it is still a challenge to apply graph convolution networks on long video sequences effectively. The main reason is that large number of nodes (i.e., video frames) makes GCNs hard to capture and model temporal relations in videos. To tackle this problem, in this paper, we introduce an effective GCN module, Dilated Temporal Graph Reasoning Module (DTGRM), designed to model temporal relations and dependencies between video frames at various time spans. In particular, we capture and model temporal relations via constructing multi-level dilated temporal graphs where the nodes represent frames from different moments in video. Moreover, to enhance temporal reasoning ability of the proposed model, an auxiliary self-supervised task is proposed to encourage the dilated temporal graph reasoning module to find and correct wrong temporal relations in videos. Our DTGRM model outperforms state-of-the-art action segmentation models on three challenging datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset. The code is available at https://github.com/redwang/DTGRM.

The pre-training models such as BERT have achieved great results in various natural language processing problems. However, a large number of parameters need significant amounts of memory and the consumption of inference time, which makes it difficult to deploy them on edge devices. In this work, we propose a knowledge distillation method LRC-BERT based on contrastive learning to fit the output of the intermediate layer from the angular distance aspect, which is not considered by the existing distillation methods. Furthermore, we introduce a gradient perturbation-based training architecture in the training phase to increase the robustness of LRC-BERT, which is the first attempt in knowledge distillation. Additionally, in order to better capture the distribution characteristics of the intermediate layer, we design a two-stage training method for the total distillation loss. Finally, by verifying 8 datasets on the General Language Understanding Evaluation (GLUE) benchmark, the performance of the proposed LRC-BERT exceeds the existing state-of-the-art methods, which proves the effectiveness of our method.

Most explanation methods in deep learning map importance estimates for a model's prediction back to the original input space. These "visual" explanations are often insufficient, as the model's actual concept remains elusive. Moreover, without insights into the model's semantic concept, it is difficult -- if not impossible -- to intervene on the model's behavior via its explanations, called Explanatory Interactive Learning. Consequently, we propose to intervene on a Neuro-Symbolic scene representation, which allows one to revise the model on the semantic level, e.g. "never focus on the color to make your decision". We compiled a novel confounded visual scene data set, the CLEVR-Hans data set, capturing complex compositions of different objects. The results of our experiments on CLEVR-Hans demonstrate that our semantic explanations, i.e. compositional explanations at a per-object level, can identify confounders that are not identifiable using "visual" explanations only. More importantly, feedback on this semantic level makes it possible to revise the model from focusing on these confounding factors.

Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health. Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas with potential negative public health impact. Methods: Using a curated data set of COVID-19 tweets (N ~120 million tweets) spanning late January to early May 2020, we applied methods including regular expression filtering, supervised machine learning, sentiment analysis, geospatial analysis, and dynamic topic modeling to trace the spread of misinformation and to characterize novel features of COVID-19 conspiracy theories. Results: Random forest models for four major misinformation topics provided mixed results, with narrowly-defined conspiracy theories achieving F1 scores of 0.804 and 0.857, while more broad theories performed measurably worse, with scores of 0.654 and 0.347. Despite this, analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. We were able to identify distinct increases in negative sentiment, theory-specific trends in geospatial spread, and the evolution of conspiracy theory topics and subtopics over time. Conclusions: COVID-19 related conspiracy theories show that history frequently repeats itself, with the same conspiracy theories being recycled for new situations. We use a combination of supervised learning, unsupervised learning, and natural language processing techniques to look at the evolution of theories over the first four months of the COVID-19 outbreak, how these theories intertwine, and to hypothesize on more effective public health messaging to combat misinformation in online spaces.

This time last year, I was in Hong Kong, meeting with human rights activists and documenting the large pro-democracy demonstrations. The police were cracking down on protestors, using excessive force in the streets, and perpetrating abuse behind closed doors. An independent inquiry into police abuses was, and is, crucial - and I proposed just that. On a typical Saturday evening on Nathan Road, thousands of young people would be marching and singing, and many, if not most, would be using their phones to film police using tear gas, batons, and other weapons throughout the night. At the same time, camerapersons both amateur and professional captured footage that circulated around the world via news broadcasts and social media.