Salah Khalil is the founder and chief executive officer of Macat International, a company that measures and develops critical thinking skills in higher education and in the corporate sector. Khalil is former strategy consultant at the Westminster Foundation for Democracy in London. He also serves on the advisory board of the Business School at the American University in Cairo. Khalil says many skills that we're using in the current economy might be surpassed by machines in the future. These skills will decay with time, and critical thinking is one of those skills that will not decay with time.

Tailoring is fancy, sufficiently fancy that you may go your entire life and never once experience the art. It's expensive, having garments custom-made to suit your body shape, even if there are a legion of benefits in doing so. Mass-produced clothes, meanwhile, are never going to do the job if you've got a body that diverges from what's expected or treated as "normal." There are two real problems: Measurement, and manufacturing, issues that the fashion industry is wrestling with right now. A Taiwanese company, TG3D, has at least discovered a way to solve the first part of the equation with little more than an iPhone.

Existing work (Ji and Grishman, we consider the attention weight between 2008; McClosky et al., 2011; Li et al., 2013; two event mentions as a learned similarity, and we Chen et al., 2015; Du and Cardie, 2020; Li et al., ensure that the attention mechanism learns to align 2021a) traditionally uses a predefined list of event similar events using a semi-supervised contrastive types and their respective annotations to learn an loss. By doing this, we are able to leverage the event extraction model. However, these annotations large variety of semantic information in pretrained are both expensive and time-consuming to language models for clustering unseen types by using create. This problem is amplified when considering a trained attention head. Unlike (Huang and specialization-intensive domains such as scientific Ji, 2020), we are able to separate clustering from literature, which requires years of specialized experience learning, allowing specific task-suited clustering to understand even a specific niche. For algorithms to be selected.

We present ClidSum, a benchmark dataset for building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents from two subsets (i.e., SAMSum and MediaSum) and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART-50 (a multi-lingual BART) via further pre-training. The multiple objectives used in the further pre-training stage help the pre-trained model capture the structural characteristics as well as important content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at https://github.com/krystalan/ClidSum.

This paper deals with the problem of finding the preferred extensions of an argumentation framework by means of a bijection with the naive extensions of another framework. First we consider the case where an argumentation framework is naive-realizable: its naive and preferred extensions are equal. Recognizing naive-realizable argumentation frameworks is hard, but we show that it is tractable for frameworks with bounded in-degree. Next, we give a bijection between the preferred extensions of an argumentation framework being admissible-closed (the intersection of two admissible sets is admissible) and the naive extensions of another framework on the same set of arguments. On the other hand, we prove that identifying admissible-closed argumentation frameworks is coNP-complete. At last, we introduce the notion of irreducible self-defending sets as those that are not the union of others. It turns out there exists a bijection between the preferred extensions of an argumentation framework and the naive extensions of a framework on its irreducible self-defending sets. Consequently, the preferred extensions of argumentation frameworks with some lattice properties can be listed with polynomial delay and polynomial space.

As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In recent years, many deep learning algorithms have been introduced to improve bicycle usage forecast. A typical practice is to integrate convolutional (CNN) and recurrent neural network (RNN) to capture spatial-temporal dependency in historical travel demand. For typical CNN, the convolution operation is conducted through a kernel that moves across a "matrix-format" city to extract features over spatially adjacent urban areas. This practice assumes that areas close to each other could provide useful information that improves prediction accuracy. However, bicycle usage in neighboring areas might not always be similar, given spatial variations in built environment characteristics and travel behavior that affect cycling activities. Yet, areas that are far apart can be relatively more similar in temporal usage patterns. To utilize the hidden linkage among these distant urban areas, the study proposes an irregular convolutional Long-Short Term Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast. The model modifies traditional CNN with irregular convolutional architecture to extract dependency among "semantic neighbors". The proposed model is evaluated with a set of benchmark models in five study sites, which include one dockless bike sharing system in Singapore, and four station-based systems in Chicago, Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms other benchmark models in the five cities. The model also achieves superior performance in areas with varying levels of bicycle usage and during peak periods. The findings suggest that "thinking beyond spatial neighbors" can further improve short-term travel demand prediction of urban bike sharing systems.

New forecasts from Transforma Insights point at an explosion in the use of Artificial Intelligence for improving enterprise processes and critical systems. The devil is, as ever, in the detail, but the headline is that adoption of AI, measured in'instances' is set to grow ten-fold in the next decade. At Transforma Insights we are currently in the process of pulling together a set of forecasts of the Artificial Intelligence market, and preparing for our webinar on the 16th February. In this blog post we have a peek at the first sets of data coming from the report. We have pulled out a couple of highlights of the research to give a flavour of the granularity of the data, the topics we'll be looking at in the webinar and the key emerging themes.

Remember Skynet, the artificial intelligence that wanted to wipe out humanity in the Terminator movies? Now that is an example of AI gone wrong. Luckily, this will not be the case for us in 2022. AI today is by far not as advanced yet. But the movie does raise a couple of interesting questions.

Data Science is an exciting field for knowledge workers because it increasingly intersects with the future of how industries, society, governance and policy will function. While it's one of those vague terms thrown around a lot for students, it's actually fairly simple to define. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is thus related to an explosion of Big Data and optimizing it for human progress, machine learning and AI systems. I'm not an expert in the field by any means, just a futurist analyst, and what I see is an explosion in data science jobs globally and new talent getting into the field, people who will build the companies of tomorrow. Many of those jobs will actually be in companies that do not exist yet in South and South-East Asia and China.

State-of-the-art sign language generation frameworks lack expressivity and naturalness which is the result of only focusing manual signs, neglecting the affective, grammatical and semantic functions of facial expressions. The purpose of this work is to augment semantic representation of sign language through grounding facial expressions. We study the effect of modeling the relationship between text, gloss, and facial expressions on the performance of the sign generation systems. In particular, we propose a Dual Encoder Transformer able to generate manual signs as well as facial expressions by capturing the similarities and differences found in text and sign gloss annotation. We take into consideration the role of facial muscle activity to express intensities of manual signs by being the first to employ facial action units in sign language generation. We perform a series of experiments showing that our proposed model improves the quality of automatically generated sign language.