Trying to shoot a selfie video, but can't stay in the frame? An artificially intelligent camera could soon help. On Sunday, January 6, Remo Technology teased the Obsbot Tail, what the company calls the first A.I.-powered autonomous director camera. The camera-gimbal combo uses A.I. to keep the subject in the frame. The Obsbot Tail mixes a 4K 60 fps camera with a three-axis mechanical gimbal and a 3.5x optical zoom lens.

Google is offering US schoolchildren the chance to design their own Doodle to appear on its homepage. The Google Doodle sees the Silicon Valley search giant periodically replace its familiar logo with a sketch, often animated, to celebrate a public figure on an anniversary associated with them or their achievements. Doing so offers an opportunity to champion figures from the arts and sciences who have distinguished themselves through innovation or by blazing a trail for others and deserve to be better known. This year's theme is "hope", with entrants asked to submit a design based on their personal wishes for the future. Kids who would like to get involved have until 8pm Pacific Time on 19 March 2019 to upload a .jpg

The study of register in computational language research has historically been divided into register analysis, seeking to determine the registerial character of a text or corpus, and register synthesis, seeking to generate a text in a desired register. This article surveys the different approaches to these disparate tasks. Register synthesis has tended to use more theoretically articulated notions of register and genre than analysis work, which often seeks to categorize on the basis of intuitive and somewhat incoherent notions of prelabeled 'text types'. I argue that an integration of computational register analysis and synthesis will benefit register studies as a whole, by enabling a new large-scale research program in register studies. It will enable comprehensive global mapping of functional language varieties in multiple languages, including the relationships between them. Furthermore, computational methods together with high coverage systematically collected and analyzed data will thus enable rigorous empirical validation and refinement of different theories of register, which will have also implications for our understanding of linguistic variation in general.

The ability to detect that something has changed in an environment is valuable, but often only if it can be accurately conveyed to a human operator. We introduce Viewpoint Invariant Change Captioning, and develop models which can both localize and describe via natural language complex changes in an environment. Moreover, we distinguish between a change in a viewpoint and an actual scene change (e.g. a change of objects' attributes). To study this new problem, we collect a Viewpoint Invariant Change Captioning Dataset (VICC), building it off the CLEVR dataset and engine. We introduce 5 types of scene changes, including changes in attributes, positions, etc. To tackle this problem, we propose an approach that distinguishes a viewpoint change from an important scene change, localizes the change between "before" and "after" images, and dynamically attends to the relevant visual features when describing the change. We benchmark a number of baselines on our new dataset, and systematically study the different change types. We show the superiority of our proposed approach in terms of change captioning and localization. Finally, we also show that our approach is general and can be applied to real images and language on the recent Spot-the-diff dataset.

One noninvasive way to study frog communities is by analyzing long-term samples of acoustic material containing calls. This immense task has been optimized by the development of Machine Learning tools to extract ecological information. We explored a likelihood-ratio audio detector based on Gaussian mixture model classification of 10 frog species, and applied it to estimate presence-absence in audio recordings from an actual amphibian monitoring performed at Yasun ı National Park in the Ecuadorian Amazonia. A modified filter-bank was used to extract 20 cepstral features that model the spectral content of frog calls. Experiments were carried out to investigate the hyperparameters and the minimum frog-call time needed to train an accurate GMM classifier. With 64 Gaussians and 12 seconds of training time, the classifier achieved an average weighted error rate of 0.9% on the 10-fold cross-validation for nine species classification, as compared to 3% with MFCC and 1.8% with PLP features. For testing, 10 GMMs were trained using all the available training-validation dataset to study 23.5 hours in 141, 10-minute long samples of unidentified real-world audio recorded at two frog communities in 2001 with analog equipment. To evaluate automatic presence-absence estimation, we characterized the audio samples with 10 binary variables each corresponding to a frog species, and manually labeled a subset of 18 samples using headphones. The one-vs-all Receiver Operating Characteristics curves were used to tune the likelihood-ratio detector per class in order to set operating points that minimize false positives while still allowing moderately noisy calls to be detected. A recall of 87.5% and precision of 100% with average accuracy of 96.66% suggests good generalization ability of the algorithm, and provides evidence of the validity of this approach Finally, we applied the algorithm to the available corpus, and show its potentiality to gain insights into the temporal reproductive behavior of frogs. Introduction In long term ecological studies, it is important to quantify changes that occur on biodiversity and the ecosystem as a whole. Large scale temporal and spatial studies to understand the natural and anthropogenic induced population dynamics are demanded by the scientific community. In addition, recent anuran population declines around the world have motivated studies to gain an understanding of the phenomenon [1].

The Vive Pro Eye is the next evolution of HTC's virtual reality headset line, and its shiniest new feature is integrated eye-tracking. HTC America General Manager Dan O'Brien revealed the Vive Pro Eye at CES, explaining how built-in eye-tracking can benefit industries from auto technology to fitness and gaming. Integrated eye-tracking enables foveated rendering, a technique that creates sharper, more realistic images by blurring the scenery around the user's gaze. This means there's less power being spent on things that users aren't actually looking at. Integrated eye-tracking removes the need for controllers in certain scenarios, such as menu navigation, according to HTC.

What are some concepts which many people who use Machine Learning / Artificial Intelligence get "wrong" or misunderstand? What are some good articles (research and "lay") on the subject? What might be some good or interesting "polling" questions to tease these sorts of things out? Note: I would love and/or encourage anyone to take the result of this discussion and turn it into a peer-reviewed-caliber paper. I've been thinking about it, but realistically, I probably won't do it, so knock yourself out!

Abstract: Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged.

In the last decade, nothing has begun to change business quite like Artificial Intelligence. What used to be science fiction is now a straight fact, as organizations the world over have begun leveraging AI for many unrelated processes. The advantage of AI is that simple, rote tasks can be automated, freeing up flesh-and-blood humans to take on more creative and innovative tasks. In this way, everything is looking at becoming more efficient. Consider the standard analogue recruitment process.

David Fincher's next project for Netflix is taking a sharp left turn. The famed director is working with Deadpool's Tim Miller on Love, Death, and Robots, a mature-themed animated anthology series. The show will revolve around 18 short stories of varying length (from 5 to 15 minutes each), each with their own film crews using distinctive art styles ranging from classic 2D to realistic CG. The producers aren't saying much about the content, but have noted the episodes will cover genres like comedy, horror and tragedy. You can expect subjects as strange as "alien spiders" and "sentient dairy," in case you thought the producers might play it safe.