In the article "A Data-Driven Approach to Violinmaking," the Chilean physicist and luthier Sebastian Gonzalez (post-doc researcher) and the professional mandolin player Davide Salvi (Ph.D. student) show how a simple and effective neural network is able to predict the vibrational be-havior of violin plates. The ability to predict the sound of a violin design, can truly be a game changer for violin makers, as not only will it help them do better than the'grand masters,' but it will also help them explore the potential of new designs and materials. Politecnico di Milano researchers developed a model that describes the violin's outline as the conjunction of arcs of nine circles. Thanks to this representation and an efficient model of the curvature of the plate, based on the renowned'Messiah' violin by Stradivarius, researchers were able to draw a violin plate as a function of 35 parameters. By randomly changing such parameters, such as radii and center position of the circles, arching, thickness, mechanical characteristics of the wood, etc., they built a dataset of violins, which includes shapes that are very similar to those used in violin making, but also designs that had never been seen before.

To diversify and enrich generated dialogue responses, knowledge-grounded dialogue has been investigated in recent years. Despite the success of the existing methods, they mainly follow the paradigm of retrieving the relevant sentences over a large corpus and augment the dialogues with explicit extra information, which is time- and resource-consuming. In this paper, we propose KnowExpert, an end-to-end framework to bypass the retrieval process by injecting prior knowledge into the pre-trained language models with lightweight adapters. To the best of our knowledge, this is the first attempt to tackle this task relying solely on a generation-based approach. Experimental results show that KnowExpert performs comparably with the retrieval-based baselines, demonstrating the potential of our proposed direction.

Dialogue systems are a popular Natural Language Processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learning-based due to the outstanding performance. In this survey, we mainly focus on the deep learning-based dialogue systems. We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. Specifically, from the angle of model type, we discuss the principles, characteristics, and applications of different models that are widely used in dialogue systems. This will help researchers acquaint these models and see how they are applied in state-of-the-art frameworks, which is rather helpful when designing a new dialogue system. From the angle of system type, we discuss task-oriented and open-domain dialogue systems as two streams of research, providing insight into the hot topics related. Furthermore, we comprehensively review the evaluation methods and datasets for dialogue systems to pave the way for future research. Finally, some possible research trends are identified based on the recent research outcomes. To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present in the area of dialogue systems and dialogue-related tasks, extensively covering the popular frameworks, topics, and datasets. Keywords: Dialogue Systems, Chatbots, Conversational AI, Task-oriented, Open Domain, Chit-chat, Question Answering, Artificial Intelligence, Natural Language Processing, Information Retrieval, Deep Learning, Neural Networks, CNN, RNN, Hierarchical Recurrent Encoder-Decoder, Memory Networks, Attention, Transformer, Pointer Net, CopyNet, Reinforcement Learning, GANs, Knowledge Graph, Survey, Review

Advances in AI are making their way across all products and services we interact with. Our cars are outfitted with tools that trigger automatic breaking, platforms such as Netflix proactively suggest recommendations for viewing, Alexa and Google can predict our search needs, and Spotify can recommend songs and curate listening lists much better than you or I can. Although the advantages of AI in our daily lives are undeniable, people are concerned about its dangers. Inadequate physical security, economic losses, and ethical issues are just a few examples of the damage AI could cause. In response to AI dangers, the European Union is working on a legal framework to regulate artificial intelligence.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Fisher Stevens really regrets appearing as an Indian character in brownface for the 1986 movie "Short Circuit" and its subsequent sequel. Stevens, who was born in Chicago, plays Ben Jabituya in the science fiction comedy about two scientists whose advanced robot gains sentience. While the Johnny 5 robot character is still revered as a staple of 1980s comedies, Stevens' role and the subsequent darkening of his skin to appear as an Indian man is often criticized to this day for portraying a stereotype and taking a role away from an Indian actor.

Larger language models have higher accuracy on average, but are they better on every single instance (datapoint)? Some work suggests larger models have higher out-of-distribution robustness, while other work suggests they have lower accuracy on rare subgroups. To understand these differences, we investigate these models at the level of individual instances. However, one major challenge is that individual predictions are highly sensitive to noise in the randomness in training. We develop statistically rigorous methods to address this, and after accounting for pretraining and finetuning noise, we find that our BERT-Large is worse than BERT-Mini on at least 1-4% of instances across MNLI, SST-2, and QQP, compared to the overall accuracy improvement of 2-10%. We also find that finetuning noise increases with model size and that instance-level accuracy has momentum: improvement from BERT-Mini to BERT-Medium correlates with improvement from BERT-Medium to BERT-Large. Our findings suggest that instance-level predictions provide a rich source of information; we therefore, recommend that researchers supplement model weights with model predictions.

Climate change is a burning issue of our time, with the Sustainable Development Goal (SDG) 13 of the United Nations demanding global climate action. Realizing the urgency, in 2015 in Paris, world leaders signed an agreement committing to taking voluntary action to reduce carbon emissions. However, the scale, magnitude, and climate action processes vary globally, especially between developed and developing countries. Therefore, from parliament to social media, the debates and discussions on climate change gather data from wide-ranging sources essential to the policy design and implementation. The downside is that we do not currently have the mechanisms to pool the worldwide dispersed knowledge emerging from the structured and unstructured data sources. The paper thematically discusses how NLP techniques could be employed in climate policy research and contribute to society's good at large. In particular, we exemplify symbiosis of NLP and Climate Policy Research via four methodologies. The first one deals with the major topics related to climate policy using automated content analysis. We investigate the opinions (sentiments) of major actors' narratives towards climate policy in the second methodology. The third technique explores the climate actors' beliefs towards pro or anti-climate orientation. Finally, we discuss developing a Climate Knowledge Graph. The present theme paper further argues that creating a knowledge platform would help in the formulation of a holistic climate policy and effective climate action. Such a knowledge platform would integrate the policy actors' varied opinions from different social sectors like government, business, civil society, and the scientific community. The research outcome will add value to effective climate action because policymakers can make informed decisions by looking at the diverse public opinion on a comprehensive platform.

At this year's Oscar's, Denmark's Another Round won Best International Feature Film Oscar, as well as a Best Director nomination for Thomas Vinterberg. Considering all its success, of course Hollywood has plans to remake it in English. But that might be a thing of the past soon. Not because people are finally going to get over their aversion to subtitles; but because deepfake technology can now be used to match lips to dubbed dialogue. A post shared by Flawless (@flawless.true)

Following on from this, we've picked out seven specific trends that we've seen change or accelerate during the recent lockdown, and that we think you should watch out for over the next 12-24 months. So here's our list of seven post-pandemic trends to look out for in 2021 – and beyond. This trend is easy to predict and is one that – hopefully – you have already begun implementing in your campaigns. It's reported that by 2022, 82% of global internet traffic will come from video streaming and downloads. What's more, 72% of businesses have reported that video increased their conversion rates.

Watching meaningful movies such as Up or Slumdog Millionaire can help us feel more prepared to deal with challenges and want to be a better person, a new study shows. Ohio State University researchers created two lists of films made after 1985 with high viewer ratings - one with meaningful and one with less meaningful movies. They then had 1,098 volunteers watch either the meaningful or less meaningful list of movies, before filling in a survey on their thoughts and reaction to the films. Watching meaningful films - those that we find moving and poignant - can make us feel more prepared to deal with life's challenges, the authors found. The team say this could explain why people turn to movies that make them both sad and happy and explore difficult subjects they may not always find uplifting.