But that was before machine learning and big data empowered computers to make their own decisions.

After working on the model building, the next step in the machine learning life cycle is usually the deployment in the real-world scenario to perform …

Researchers from Texas Tech University developed a deep-learning algorithm called a convolutional neural network able to distinguish between the …

Bolle Turns To Artificial Intelligence to Develop New Lens.

Pinpoint is a noticeable member of Reporter Studio, a series of journalist tools launched by Google. The purpose of Reporter Studio is to help journalists "work more efficiently, creatively and safely." And Pinpoint is a tool that Google highlights to use artificial intelligence and machine learning technology to help journalists screen massive investigation materials. Google says Pinpoint can help journalists explore and analyze a large number of documents. It's free and easy to use.

"Light," a former camera company has announced a new depth sensor that could be a game changer, upending LIDAR and computer vision based depth measurement, by producing a combination RGB image and depth map with ranges out to an astonishing 1000 meters. Presuming it works as promised and can be delivered at scale, this is astonishing news. Some of you will know Light as a company that made a computational camera with 16 lenses at 3 different focal lengths. Combining the images let them produce a high resolution image with an adjustable depth of field, hoping to get SLR quality in a flat box. The tested their first product, and while interesting, it was not ready for prime-time. They later got money from Softbank.

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