You know when parents cry about leaving their kids at school? I would often use my SimpliSafe security camera as a pet camera to peek in on the pups when I wasn't home, but it wasn't enough. I got notifications if they moved around, but when they left the camera's field of vision, I didn't know much else. What if they'd gotten sick in a corner? What if they got into something they shouldn't?

Dogs were first domesticated around 29,000 years ago and have since become one of the most popular species of companion animals around the world. But until now, exactly why the animals became'man's best friend' has remained unclear. Now, scientists from Azabu University in Japan believe they have the answer, having discovered two key gene mutations in dogs. These mutations may have played a role in their domestication by lowering stress and making pups more comfortable interacting with humans, according to the team. Researchers from Duquesne University in Pittsburgh recently found that dogs have similar muscles in their faces to humans, allowing them to form facial expressions close to our own.

We celebrate our pets all year, but May is National Pet Month, and Amazon is kicking it off with a day of deals. Plus, with Mother's Day right around the corner, now is the perfect time to give a gift to the pet parents in your life--yourself included! From toys and treats to TikTok-trending accessories, some of our favorites are on sale. Special offer for Gear readers: Get a 1-Year Subscription to WIRED for $5 ($25 off). This includes unlimited access to WIRED.com and our print magazine (if you'd like). Subscriptions help fund the work we do every day.

The author of Dog Is Love: Why and How Your Dog Loves You responds to Andrew Silverman's "Furgen." It doesn't take any special technology to see that dogs love people. Hildegard von Bingen, in the 11th century, noted that "a certain natural community of behavior binds [the dog] to humans. Therefore, he responds to man, understand him, loves him and likes to stay with him." It could fairly be said that, like Othello, dogs love not wisely, but too well.

Paraphrasing is expressing the meaning of an input sentence in different wording while maintaining fluency (i.e., grammatical and syntactical correctness). Most existing work on paraphrasing use supervised models that are limited to specific domains (e.g., image captions). Such models can neither be straightforwardly transferred to other domains nor generalize well, and creating labeled training data for new domains is expensive and laborious. The need for paraphrasing across different domains and the scarcity of labeled training data in many such domains call for exploring unsupervised paraphrase generation methods. We propose Progressive Unsupervised Paraphrasing (PUP): a novel unsupervised paraphrase generation method based on deep reinforcement learning (DRL). PUP uses a variational autoencoder (trained using a non-parallel corpus) to generate a seed paraphrase that warm-starts the DRL model. Then, PUP progressively tunes the seed paraphrase guided by our novel reward function which combines semantic adequacy, language fluency, and expression diversity measures to quantify the quality of the generated paraphrases in each iteration without needing parallel sentences. Our extensive experimental evaluation shows that PUP outperforms unsupervised state-of-the-art paraphrasing techniques in terms of both automatic metrics and user studies on four real datasets. We also show that PUP outperforms domain-adapted supervised algorithms on several datasets. Our evaluation also shows that PUP achieves a great trade-off between semantic similarity and diversity of expression.