The square-faced, three-legged alien shoves and jostles to get at the enormous plant taking over its tiny planet. But each bite just makes the forbidden fruit grow bigger. Suddenly the plant's weight flips the whole sphere upside down and all the little creatures drop into space. Reach in and catch one! Agence, a short interactive VR film from Toronto-based studio Transitional Forms and the National Film Board of Canada, won't be breaking any box office records.

Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs can be misused by adversaries, e.g., by automatically generating fake news and fake product reviews that can look authentic and fool humans. Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both natural language processing (NLP) and machine learning (ML) communities to build accurate detectors for English. Despite the importance of this problem, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and review of this literature to facilitate a comprehensive understanding of this problem. We conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area.

Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log likelihood of ground-truth questions using teacher forcing. However, this training objective is inconsistent with actual question quality, which is often reflected by certain global properties such as whether the question can be answered by the document. As such, we directly optimize for QG-specific objectives via reinforcement learning to improve question quality. We design three different rewards that target to improve the fluency, relevance, and answerability of generated questions. We conduct both automatic and human evaluations in addition to thorough analysis to explore the effect of each QG-specific reward. We find that optimizing on question-specific rewards generally leads to better performance in automatic evaluation metrics. However, only the rewards that correlate well with human judgement (e.g., relevance) lead to real improvement in question quality. Optimizing for the others, especially answerability, introduces incorrect bias to the model, resulting in poor question quality.

Lattices are compact representations that encode multiple hypotheses, such as speech recognition results or different word segmentations. It is shown that encoding lattices as opposed to 1-best results generated by automatic speech recognizer (ASR) boosts the performance of spoken language understanding (SLU). Recently, pretrained language models with the transformer architecture have achieved the state-of-the-art results on natural language understanding, but their ability of encoding lattices has not been explored. Therefore, this paper aims at adapting pretrained transformers to lattice inputs in order to perform understanding tasks specifically for spoken language. Our experiments on the benchmark ATIS dataset show that fine-tuning pretrained transformers with lattice inputs yields clear improvement over fine-tuning with 1-best results. Further evaluation demonstrates the effectiveness of our methods under different acoustic conditions. Our code is available at https://github.com/MiuLab/Lattice-SLU

The knowledge contained in academic literature is interesting to mine. Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets and metrics are used as AI markers for AI literature. These entities can be used to trace the research process described in the bodies of papers, which opens up new perspectives for seeking and mining more valuable academic information. Firstly, the entity extraction model is used in this study to extract AI markers from large-scale AI literature. Secondly, original papers are traced for AI markers. Statistical and propagation analysis are performed based on tracing results. Finally, the co-occurrences of AI markers are used to achieve clustering. The evolution within method clusters and the influencing relationships amongst different research scene clusters are explored. The above-mentioned mining based on AI markers yields many meaningful discoveries. For example, the propagation of effective methods on the datasets is rapidly increasing with the development of time; effective methods proposed by China in recent years have increasing influence on other countries, whilst France is the opposite. Saliency detection, a classic computer vision research scene, is the least likely to be affected by other research scenes.

Intel has confirmed that it is buying SigOpt Inc., an artificial intelligence startup developing software platforms to optimize AI models.

Recognizing them is increasingly hard if not impossible to the untrained human eye. Overall, as most journalistic coverage of the topic tells us, deepfakes -- alongside other AI technologies, machine learning, and online neural networks in general -- are here and will serve to cast a shadow of technological terror over society. As part of media coverage on this topic, our future is deemed dystopian -- humankind has lost the battles to machines and episodes of the TV series "Black Mirror" will pale in comparison with the havoc sowed by technology. In fact, research I conducted with a colleague from the University of Haifa (Yael Oppenheim) has found that most images and narratives that journalists worldwide use to cover these technologies tend to stress destruction, loss, crisis, and fear regarding the future of humanity. From Israel to the U.S., deepfake videos are becoming a major threat to democracy'Every woman on Instagram is exposed': New AI creates nude photos of clothed women It is, however, important to contextualize this alarmist media frenzy.

London, Oct 31 (ONA) – The Sultanate's rating in the "Government Artificial Intelligence Readiness Index 2020" advanced by 11 places, according to a …

Seventy percent of deaths are caused by chronic diseases and associated complications.

A HI-TECH firm that is a stalwart of East Lancashire Chamber of Commerce is sharing in £3million of Euro cash to boost artificial intelligence.