Maxime Firth's business is complicated to manage, even in good times. His company, Onduline, turns recycled fibres into roofing material, after dousing them with bitumen to make them waterproof, and sells products in 100 countries. Its eight production plants span from Nizhny Novgorod in Russia and Penang in Malaysia, to Juiz de Fora in Brazil. Further complicating his supply chain, Mr Firth's business is strongly seasonal. People install roofs in the summer, so products are made from January to March, to sell from April to September.

The incorporation of new technologies into architectural designs has been expanding design possibilities over the last few years. Automation in construction processes can be used both in large scale city strategies, and smaller-scale demands like in the construction of residences. One of the more recent ways that technology has been integrated into the design of workplaces is through the incorporation of artificial intelligence, which uses data that can "teach" the machines how to work in several levels of autonomy. The way that artificial intelligence can be incorporated into the daily function of the workplaces depends on the type and amount of data used to fulfill the projects, and how it can contribute to the evaluating the efficiency of construction, simulation of human movement reflected in the drawings, structural calculations, and other design opportunities. Here, we've compiled a short list of projects that effectively utilize artificial intelligence below: Philips Lighting Headquarters located in Eindhoven, Netherlands, takes advantage of understanding how lighting could become a center point of a design project.

Machine-learning systems can be duped or confounded by situations they haven't seen before. A self-driving car gets flummoxed by a scenario that a human driver could handle easily. An AI system laboriously trained to carry out one task (identifying cats, say) has to be taught all over again to do something else (identifying dogs). In the process, it's liable to lose some of the expertise it had in the original task. Computer scientists call this problem "catastrophic forgetting."

Neural natural language generation (NLG) models have recently shown remarkable progress in fluency and coherence. However, existing studies on neural NLG are primarily focused on surface-level realizations with limited emphasis on logical inference, an important aspect of human thinking and language. In this paper, we suggest a new NLG task where a model is tasked with generating natural language statements that can be \emph{logically entailed} by the facts in an open-domain semi-structured table. To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset \cite{chen2019tabfact} featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w.r.t.\ logical inference. The new task poses challenges to the existing monotonic generation frameworks due to the mismatch between sequence order and logical order. In our experiments, we comprehensively survey different generation architectures (LSTM, Transformer, Pre-Trained LM) trained with different algorithms (RL, Adversarial Training, Coarse-to-Fine) on the dataset and made following observations: 1) Pre-Trained LM can significantly boost both the fluency and logical fidelity metrics, 2) RL and Adversarial Training are trading fluency for fidelity, 3) Coarse-to-Fine generation can help partially alleviate the fidelity issue while maintaining high language fluency. The code and data are available at \url{https://github.com/wenhuchen/LogicNLG}.

Citation parsing, particularly with deep neural networks, suffers from a lack of training data as available datasets typically contain only a few thousand training instances. Manually labelling citation strings is very time-consuming, hence synthetically created training data could be a solution. However, as of now, it is unknown if synthetically created reference-strings are suitable to train machine learning algorithms for citation parsing. To find out, we train Grobid, which uses Conditional Random Fields, with a) human-labelled reference strings from 'real' bibliographies and b) synthetically created reference strings from the GIANT dataset. We find that both synthetic and organic reference strings are equally suited for training Grobid (F1 = 0.74). We additionally find that retraining Grobid has a notable impact on its performance, for both synthetic and real data (+30% in F1). Having as many types of labelled fields as possible during training also improves effectiveness, even if these fields are not available in the evaluation data (+13.5% F1). We conclude that synthetic data is suitable for training (deep) citation parsing models. We further suggest that in future evaluations of reference parsers both evaluation data similar and dissimilar to the training data should be used for more meaningful evaluations.

Semantic Question Answering (QA) is the key technology to facilitate intuitive user access to semantic information stored in knowledge graphs. Whereas most of the existing QA systems and datasets focus on entity-centric questions, very little is known about the performance of these systems in the context of events. As new event-centric knowledge graphs emerge, datasets for such questions gain importance. In this paper we present the Event-QA dataset for answering event-centric questions over knowledge graphs. Event-QA contains 1000 semantic queries and the corresponding English, German and Portuguese verbalisations for EventKG - a recently proposed event-centric knowledge graph with over 970 thousand events.

Our comprehensive guide to how chatbots will develop in 2020 and beyond. Artificial intelligence is the hottest talking point for business users looking to improve their efficiency, deliver new ideas and take the next steps in the transition to a digital enterprise. AI and chatbots are helping democratise business, empower startups and help build new partnerships, something that every organisation needs to prepare for. "Every business is a technology business" was one of the mantras of the decade just concluded. Every company across every vertical and market started working and communicating with smartphones, using cloud services to open up their data and adopted as-a-service solutions to reduce the cost of doing business and broaden their business base and the opportunities for workers. Ten years ago, specialists were needed to manage databases and build websites. Now anyone with a plan can build an entire company out of off-the-shelf parts, sell across the world without leaving their desk. They can pick advice from a huge range of sources to grow the business and partner with a massive range of organisations to deliver whatever they sell. Now as we move into the 2020s, enterprises and startups alike are taking the next step, adopting AI and bringing smart services into their organisations. It has already started with chatbots and analytics tools, but is already expanding to business-enabling technology, using a mix of machine learning, deep learning, computer vision, natural language processing, machine reasoning (MR), and deep or strong AI. Companies will continue to deploy AI for intelligent robotic process automation, computer vision tasks, and machine learning applications.

Text-to-image synthesis is the task of generating images from text descriptions. Image generation, by itself, is a challenging task. When we combine image generation and text, we bring complexity to a new level: we need to combine data from two different modalities. Most of recent works in text-to-image synthesis follow a similar approach when it comes to neural architectures. Due to aforementioned difficulties, plus the inherent difficulty of training GANs at high resolutions, most methods have adopted a multi-stage training strategy. In this paper we shift the architectural paradigm currently used in text-to-image methods and show that an effective neural architecture can achieve state-of-the-art performance using a single stage training with a single generator and a single discriminator. We do so by applying deep residual networks along with a novel sentence interpolation strategy that enables learning a smooth conditional space. Finally, our work points a new direction for text-to-image research, which has not experimented with novel neural architectures recently.

Blue whales swim to the surface to feed on krill as it helps them to conserve energy, according to a new study that involved amazing drone footage of the mammals. Experts from Oregon State University found that feeding on the ocean's surface plays an important role in the hunt for food among New Zealand blue whales. Blue whales are the largest mammals on Earth and have to carefully balance the cost of energy they get from food with the cost of energy used in getting the food. Researchers say the marine mammals forage for krill in areas where they are densely packed and found near the surface of the water to cut their dive time. The Oregon team found that the blue whales do this to conserve on the energetic costs of feeding such as diving, holding their breath or opening their mouths.

It could be argued that scientists create superpowers in their labs. If Aram Galstyan, director of the Artificial Intelligence Division at the USC Viterbi Information Sciences Institute (ISI) had to pick just one superpower, it would be the ability to predict the future. What will be the daily closing price of Japan's Nikkei 225 index at the end of next week? How many 6.0 or stronger earthquakes will occur worldwide next month? Galstyan and a team of researchers at USC ISI are building a system to answer such questions.