Theatrical improvisation (impro or improv) is a demanding form of live, collaborative performance. Improv is a humorous and playful artform built on an open-ended narrative structure which simultaneously celebrates effort and failure. It is thus an ideal test bed for the development and deployment of interactive artificial intelligence (AI)-based conversational agents, or artificial improvisors. This case study introduces an improv show experiment featuring human actors and artificial improvisors. We have previously developed a deep-learning-based artificial improvisor, trained on movie subtitles, that can generate plausible, context-based, lines of dialogue suitable for theatre (Mathewson and Mirowski 2017). In this work, we have employed it to control what a subset of human actors say during an improv performance. We also give human-generated lines to a different subset of performers. All lines are provided to actors with headphones and all performers are wearing headphones. This paper describes a Turing test, or imitation game, taking place in a theatre, with both the audience members and the performers left to guess who is a human and who is a machine. In order to test scientific hypotheses about the perception of humans versus machines we collect anonymous feedback from volunteer performers and audience members. Our results suggest that rehearsal increases proficiency and possibility to control events in the performance. That said, consistency with real world experience is limited by the interface and the mechanisms used to perform the show. We also show that human-generated lines are shorter, more positive, and have less difficult words with more grammar and spelling mistakes than the artificial improvisor generated lines.

The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, document retrieval, fact extraction, and claim classification. In this paper, we present our claim verification pipeline approach, which, according to the preliminary results, scored third in the shared task, out of 23 competing systems. For the document retrieval, we implemented a new entity linking approach. In order to be able to rank candidate facts and classify a claim on the basis of several selected facts, we introduce two extensions to the Enhanced LSTM (ESIM).

With the growth of the internet, the number of fake-news online has been proliferating every year. The consequences of such phenomena are manifold, ranging from lousy decision-making process to bullying and violence episodes. Therefore, fact-checking algorithms became a valuable asset. To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source. However, most of the widely used Web indicators have either been shut-down to the public (e.g., Google PageRank) or are not free for use (Alexa Rank). Further existing databases are short-manually curated lists of online sources, which do not scale. Finally, most of the research on the topic is theoretical-based or explore confidential data in a restricted simulation environment. In this paper we explore current research, highlight the challenges and propose solutions to tackle the problem of classifying websites into a credibility scale. The proposed model automatically extracts source reputation cues and computes a credibility factor, providing valuable insights which can help in belittling dubious and confirming trustful unknown websites. Experimental results outperform state of the art in the 2-classes and 5-classes setting.

Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM). Without a doubt, synthetic imagery bears a vast potential due to scalability in terms of amounts of data obtainable without tedious manual ground truth annotations or measurements. Here, we present a dataset with the aim of providing a higher degree of photo-realism, larger scale, more variability as well as serving a wider range of purposes compared to existing datasets. Our dataset leverages the availability of millions of professional interior designs and millions of production-level furniture and object assets -- all coming with fine geometric details and high-resolution texture. We render high-resolution and high frame-rate video sequences following realistic trajectories while supporting various camera types as well as providing inertial measurements. Together with the release of the dataset, we will make executable program of our interactive simulator software as well as our renderer available at https://interiornetdataset.github.io. To showcase the usability and uniqueness of our dataset, we show benchmarking results of both sparse and dense SLAM algorithms.

Saint Louis University (SLU) has rolled out 2,300 Alexa-powered Echo Dot virtual assistants to all of its student living spaces to provide answers to university-related queries about events, speakers on campus and more. The university also plans to extend use of the artificial intelligence assistant into classrooms and meeting rooms in future and aims to use the technology to support workplace productivity for its faculty staff, according to CIO, David Hakanson. Students arriving at SLU this month can access a custom skill that answers questions relating to university services, such as "When does the library open?" or "Where is the registrar's office?" Already, 130 university-related queries can be answered using the Alexa, and the university is working on more. The idea is that Alexa can reduce the time it takes for students to access information online from seconds to minutes.

After spending more than 100 hours testing LED TVs, including new 2018 models, we think the TCL 6-Series--available in both a 55-inch and 65-inch size--is the best value we have ever seen in a TV series. It produces images with noticeably more detail, brightness, and color than most TVs that cost hundreds more--in fact, even when viewed side-by-side with TVs that cost twice as much, we still prefer the TCL. After the success of the 2017 version, we had high hopes for the 2018 TCL 6-Series TVs, and overall they have delivered. They offer superb performance for their price, including high dynamic range support for both HDR10 and Dolby Vision formats that looks incredible in use. They also include our favorite built-in streaming media interface from Roku, so you don't need a separate device. With excellent performance and no serious flaws, the TCL 6-Series is an easy recommendation. If you want a more accurate image with better motion clarity and you're willing to spend more than twice as much as the TCL for it, you should consider the Sony X900F. The Sony also comes in 49-inch, 55-inch, 65-inch, 75-inch, and 85-inch versions for those looking for a bigger screen than you can get from TCL. With HDR content, its highlights are even brighter and more saturated than TCL's. The price increase is steeper than the image quality increase, though. The TCL is easier to set up, however, and the Sony's Android TV interface, though it offers useful voice search, is harder to use than TCL's Roku interface. I've been reviewing TVs and home theater equipment since 2008. I am an ISF Level II Certified Calibrator, so I am aware of what makes for a good TV image and how to get those things out of a TV. I have all the necessary test equipment and software to provide objective measurements to back up my subjective opinions. Additionally, I enlisted my non-videophile neighbors to take a look at our finalists to make sure our priorities were in line with what normal people look for in a TV.

Found this article about 25 top world AI companies, do you know more names that should be on list? Or you think something is not right in this list, leave comments.

There is a huge difference between computer engineer and computer scientist. You should take basic math courses because if your foundation is strong anything you built upon it will become invincible. If you go for AI courses from the very beginning you may learn to code some models but you'd find it hard to grasp the maths behind it. Mathematics is the heart of AI and without it you'll become a mere zombie. Subjects like data structures and OOP are also necessary to learn.

Most people who get into AI have a computer science education, so then it should be pretty easy to get into software engineering. If you come to AI from e.g. a cognitive science background, it may be harder. The other way around is a bit trickier in my opinion. You may be able to find work as a software engineer at an AI institute, where you support the researchers by realizing their vision. If you want to become an AI researcher yourself, that kind of experience would probably help.

I recently came across an article of a Japan store using face recognition to identify possible shoplifters. My immediate hunch was that the technique they are using is the study of body pose because a person could tell the difference if a thief is being suspicious by looking at how he behaves. So I believe we can train a neural net to do the exact same thing. But I could not figure out how to represent this information in a neural net as we need to encode the information of a time unit in order to succinctly determine that a person is suspicious. Anyone knows any similar project and mind to shed a light?