Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. CES 2021 will be a virtual event this year as the latest in tech vies for attention, while dealing with the coronavirus pandemic impacting the globe. "Masks of all kinds, voice assistants mature, 5G will power everything, more 8K TV sets, [and] robots…for more practical purposes," Greg Kahn, CEO GK Digital Ventures and the Internet of Things Consortium, told Fox News. Fox News has compiled a list of some of the technology that could be front and center virtually starting Jan. 11.

OpenAI, the company co-founded by Elon Musk and backed by Microsoft, has already mastered Dota 2 and the art of writing fake news. Now, it has reached another milestone with DALL-E (a portmanteau of "Wall-E" and "Dali"), an AI app that can create an image out of nearly any description. For example, if you ask for "a cat made of sushi" or a "high quality illustration of a giraffe turtle chimera," it will deliver those things, often with startlingly good quality (and sometimes not). DALL-E can create images based on a description of its attributes, like "a pentagonal green clock," or "a collection of glasses is sitting on a table." In the latter example, it places both drinking and eye glasses on a table with varying degrees of success.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Even Drew Barrymore struggles in the dating world. The 45-year-old star welcomed comedian Nikki Glaser on her talk show on Monday and they talked about dating, which the actress said is "one of my favorite subjects ever to dissect." The two first chatted about dating apps, and Glaser explained that "guys do not come across great on dating apps."

Security cameras and facial recognition technology are on the rise in China. In 2018, People's Daily, the media mouthpiece of China's ruling Communist Party, claimed on English-language Twitter that the country's facial recognition system was capable of scanning the faces of China's 1.4 billion citizens in just one second. German journalist Kai Strittmatter speaks fluent Mandarin and has studied China for more than 30 years. He says it's not clear whether or not the Chinese government is capable of using facial recognition software in the way it claims. But he adds, on a certain level, the veracity of the claim isn't important.

Upon film premiere, a major form of speculation concerns the relative success of the film. This relativity is in particular regards to the film's original budget, as many a time have big-budget blockbusters been met with exceptional success as met with abject failure. So how does one predict the success of an upcoming film? In this paper, we explored a vast array of film data in an attempt to develop a model that could predict the expected return of an upcoming film. The approach to this development is as follows: First, we began with the MovieLens dataset having common movie attributes along with genome tags per each film. Genome tags give insight into what particular characteristics of the film are most salient. We then included additional features regarding film content, cast/crew, audience perception, budget, and earnings from TMDB, IMDB, and Metacritic websites. Next, we performed exploratory data analysis and engineered a wide range of new features capturing historical information for the available features. Thereafter, we used singular value decomposition (SVD) for dimensionality reduction of the high dimensional features (ex. genome tags). Finally, we built a Random Forest Classifier and performed hyper-parameter tuning to optimize for model accuracy. A future application of our model could be seen in the film industry, allowing production companies to better predict the expected return of their projects based on their envisioned outline for their production procedure, thereby allowing them to revise their plan in an attempt to achieve optimal returns.

We study the problem of off-policy evaluation for slate bandits, for the typical case in which the logging policy factorizes over the slots of the slate. We slightly depart from the existing literature by taking Bayes risk as the criterion by which to evaluate estimators, and we analyze the family of 'additive' estimators that includes the pseudoinverse (PI) estimator of Swaminathan et al.\ (2017; arXiv:1605.04812). Using a control variate approach, we identify a new estimator in this family that is guaranteed to have lower risk than PI in the above class of problems. In particular, we show that the risk improvement over PI grows linearly with the number of slots, and linearly with the gap between the arithmetic and the harmonic mean of a set of slot-level divergences between the logging and the target policy. In the typical case of a uniform logging policy and a deterministic target policy, each divergence corresponds to slot size, showing that maximal gains can be obtained for slate problems with diverse numbers of actions per slot.

We investigate large-scale latent variable models (LVMs) for neural story generation -- an under-explored application for open-domain long text -- with objectives in two threads: generation effectiveness and controllability. LVMs, especially the variational autoencoder (VAE), have achieved both effective and controllable generation through exploiting flexible distributional latent representations. Recently, Transformers and its variants have achieved remarkable effectiveness without explicit latent representation learning, thus lack satisfying controllability in generation. In this paper, we advocate to revive latent variable modeling, essentially the power of representation learning, in the era of Transformers to enhance controllability without hurting state-of-the-art generation effectiveness. Specifically, we integrate latent representation vectors with a Transformer-based pre-trained architecture to build conditional variational autoencoder (CVAE). Model components such as encoder, decoder and the variational posterior are all built on top of pre-trained language models -- GPT2 specifically in this paper. Experiments demonstrate state-of-the-art conditional generation ability of our model, as well as its excellent representation learning capability and controllability.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Every once in a while, it's crucial to step back from the immediate mess and gossip and all the little things we tend to focus on each day to look at our world from a 30,000-foot view, to project what we should expect moving forward. As we enter a new decade, this seems like the perfect time to think about and prepare for what may come over the next 10 years both at home and abroad. To state the obvious, this is not an exact science.

Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents. Recently, there has been a surge in the amount of research literature on OpenQA, particularly on techniques that integrate with neural Machine Reading Comprehension (MRC). While these research works have advanced performance to new heights on benchmark datasets, they have been rarely covered in existing surveys on QA systems. In this work, we review the latest research trends in OpenQA, with particular attention to systems that incorporate neural MRC techniques. Specifically, we begin with revisiting the origin and development of OpenQA systems. We then introduce modern OpenQA architecture named ``Retriever-Reader'' and analyze the various systems that follow this architecture as well as the specific techniques adopted in each of the components. We then discuss key challenges to developing OpenQA systems and offer an analysis of benchmarks that are commonly used. We hope our work would enable researchers to be informed of the recent advancement and also the open challenges in OpenQA research, so as to stimulate further progress in this field.

A fatally ill man tries to secure the future of his family in a near-future world where the toxicity of the sun forces people to stay inside during the daytime in LX 2048, starring James D'Arcy (Agent Carter, Homeland). It's a flawed yet thought-provoking, surreal science-fiction film, chock-full of big ideas on our relationship to technology and what it means to be human, all beautifully anchored by D'Arcy's fantastic performance. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. D'Arcy plays Adam Bird, a married father of three on the brink of divorce from his wife, Reena (Anna Brewster). The year is 2048, and people are largely living indoors during the day because the sunlight is powerful enough to scald human skin instantly.