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?

We propose the first deep learning approach for exemplar-based local colorization. Given a reference color image, our convolutional neural network directly maps a grayscale image to an output colorized image. Rather than using hand-crafted rules as in traditional exemplar-based methods, our end-to-end colorization network learns how to select, propagate, and predict colors from the large-scale data. The approach performs robustly and generalizes well even when using reference images that are unrelated to the input grayscale image. More importantly, as opposed to other learning-based colorization methods, our network allows the user to achieve customizable results by simply feeding different references.

One issue might be that many people have moved to ALE & OpenAI's Gym interface for API/environment implementations, and Python for implementation language. Your C library makes Python sound like a very second-class citizen, which is discouraging, and C is increasingly disfavored for its complexity & low-level nature. Just to get started with this, one has to learn the'Cassandra POMDP format', whatever that is, and then deal with C rather than Python. Are there that many people who want to solve MDPs in a tabular form whose preferred language is C and love defining their models in Cassandra POMDP format? You also don't have any impressive use-cases or demos of things which one can do easily in AIToolbox which can't be done elsewhere as easily, or as fast, or at all - what gives me any confidence that this is really mature and I won't simply invest days into learning it only to discover some severe limitation which makes it useless for me?

Those who are working on data parallel distributed training for neural networks, you might have come across this paper in your life. This work solves the communication bottleneck in data parallel DNN training by reducing the data transmission size by sending only gradient values that crosses a threshold. I was fascinated by the ideas in the paper and wanted to quickly try it out. So I made a version of DGC on MXNet. I tried training ResNet-110 on CIFAR-10 with DGC.