The new study of almost 900 dogs aims to help owners pinpoint breathing issues. Breakthroughs, discoveries, and DIY tips sent six days a week. Despite their popularity, for their seemingly helpless-looking eyes and flat faces, short-skulled (or brachycephalic) dogs like the French bulldog often have serious difficulty breathing. A study published today in the journal found that in 12 breeds, a flat face, collapsing nostrils, and rounded physique puts them at a higher risk for developing common breathing conditions. Pekingese and Japanese chins were noted to be the highest risk.

Chihuahuas have about 0.2 percent wolf ancestry, according to a new study. Breakthroughs, discoveries, and DIY tips sent every weekday. While that chihuahua might seem about as similar to a wolf as a shrub is to a mighty redwood tree, some small breeds like the tiny, big-eared chihuahua have some wolf ancestry. New research published today in the journal (), finds that the majority of dogs living today have low but detectable levels of post-domestication wolf ancestry. These genes have likely helped shape multiple characteristics, including personality traits, sense of smell, and body size.

--Convolutional neural networks (CNNs) have succeeded remarkably in various computer vision tasks. However, they are not intrinsically explainable. While feature-level understanding of CNNs reveals where the models looked, concept-based explainability methods provide insights into what the models saw. However, their assumption of linear reconstructability of image activations fails to capture the intricate relationships within these activations. Their Fidelity-only approach to evaluating global explanations also presents a new concern. For the first time, we address these limitations with the novel Transformative Nonlinear Concept Explainer (TraNCE) for CNNs. Unlike linear reconstruction assumptions made by existing methods, TraNCE captures the intricate relationships within the activations. This study presents three original contributions to the CNN explain-ability literature: (i) An automatic concept discovery mechanism based on variational autoencoders (V AEs). This transformative concept discovery process enhances the identification of meaningful concepts from image activations. Based on the investigations on publicly available datasets, we prove that a valid decomposition of a high-dimensional image activation should follow a non-linear reconstruction, contributing to the explainer's efficiency. We also demonstrate quantitatively that, besides accuracy, consistency is crucial for the meaningfulness of concepts and human trust. The code is available at https://github.com/daslimo/TrANCE ONVOLUTIONAL neural networks (CNNs) are widely used in computer vision, achieving notable success in visual classification tasks [1], [2]. However, understanding them at a human level remains a major challenge in artificial intelligence (AI), raising significant concerns about their explainability, especially in promoting ethical AI [3]- [5].

Check out Lambda Labs here: https://lambdalabs.com/papers The paper "Few-Shot Unsupervised Image-to-Image Translation" and its demo is available here: https://nvlabs.github.io/FUNIT/

I remember mentioning to Igor Aleksander -- one of the great AI pioneers and thinkers -- while I was interviewing him for Philosophy Now magazine, that for many people, AI was going to be an unexpected, shrink-wrapped, 2-for-1 deal. What was the unexpected item in the bagging area? Well, if you recognise the phrase in italics, you are probably an experienced user of supermarket self-service checkouts (probably British; feel free to provide the equivalents in French, German, etc.), where the machines seem rather too easily surprised. The extreme short-sightedness that prevents them seeing what is to us entirely foreseeable, and their inflexibility in general, leads almost inevitably to a rather one-sided dialogue concerning the shortcomings of the machine, the designer, the manufacturer, and the store operator, that can be neatly encapsulated in the simple phrase, "Stupid bloody machines!" Alas, Artificial Stupidity is as inevitable as natural stupidity, but we take natural stupidity largely for granted because we know we are all fallible. It's the perfectly ordinary consequence of having soft, squishy brains with a limited capacity for understanding anything, let alone a world we can only dimly perceive.

The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. Existing explanation methods generally create importance rankings in terms of pixels or pixel groups. However, the resulting explanations lack an optimal size, do not consider feature dependence and are only related to one class. Counterfactual explanation methods are considered promising to explain complex model decisions, since they are associated with a high degree of human interpretability. In this paper, SEDC is introduced as a model-agnostic instance-level explanation method for image classification to obtain visual counterfactual explanations. For a given image, SEDC searches a small set of segments that, in case of removal, alters the classification. As image classification tasks are typically multiclass problems, SEDC-T is proposed as an alternative method that allows specifying a target counterfactual class. We compare SEDC(-T) with popular feature importance methods such as LRP, LIME and SHAP, and we describe how the mentioned importance ranking issues are addressed. Moreover, concrete examples and experiments illustrate the potential of our approach (1) to obtain trust and insight, and (2) to obtain input for model improvement by explaining misclassifications.

The field of AI is rapidly advancing, and pretty soon, we will get to the point where we no longer even have to search for something to find it. We will simply be able to point our smartphone cameras at it, and the AI algorithms will take care of the rest. Even though a lot of companies have been at the forefront of adopting this technology into their service offering, for the most part, it is still being used to extract information from a given image. In order to train the AI algorithms to identify objects or people in an image, researchers input lots of annotated data into the system so it can learn to recognize whatever is needed. The image data can be virtually in any form, such as video, views from many cameras, multi-dimensional data, and many other types.

In today's digital dating world, communicating clearly can sometimes get lost in translation. That's why new relationship terms have taken on a life of their own. Here are 5 new dating terms you should know. A young model unlucky in love has spoken about her worst Tinder dates, including the time a date bought her a pet Chihuahua -- and then accidentally killed it. Jodie Weston, 26, has turned off popular dating app Tinder for good after using it for just two weeks in hopes of meeting "Mr. She went on three nightmare dates with three guys – one who fell off his chair because he was leaning so close "his face was nearly buried in my cleavage." A second man took Weston to the cinema to watch the horror flick "The Purge," only to be later confronted by his furious wife. A young model unlucky in love has spoken of her worst Tinder dates, including the time a date bought her a chihuahua and then accidentally killed it. The third, however, was easily Weston's worst dating experience yet. She said the man turned up to her flat in Canary Wharf, London with a Chihuahua as a gift. 'SOFT GHOSTING' IS THE LATEST TERRIBLE BREAKUP TERM Jodie Weston, 26, has turned off popular dating app Tinder for good after using it for just two weeks in hopes of meeting "Mr.

If you want a pet without the responsibility, a robot dog is a viable alternative. No long walks, expensive veterinarian bills, or midnight potty breaks needed. The majority are aimed at kids, but you can find some realistic models designed for seniors. This guide is designed to help you find the best robot dog. Our top choice is the Joy For All Companion Pets Golden Pup.

This popular internet meme demonstrates the alarming resemblance shared between chihuahuas and muffins. These images are commonly shared in presentations in the Artificial Intelligence (AI) industry (myself included). But one question I haven't seen anyone answer is just how good IS modern AI at removing the uncertainty of an image that could resemble a chihuahua or a muffin? For your entertainment and education, I'll be investigating this question today. Binary classification has been possible since the perceptron algorithm was invented in 1957.