School district Superintendents Dan Stepenosky and Art Javis weigh in on reopening schools amid the coronavirus pandemic. As COVID-19 cases surge across the country, millions of students are once again shifting to all-remote learning. Between Sunday, Nov. 22 and Monday, Nov. 23, the percentage of students exclusively attending school online jumped from 36.9 to 40 percent. Once again, school leaders and government officials are scrambling to figure out logistics. But there's a huge remote learning side effect they've yet to consider: Student loneliness.

Internet companies frequently have murky policies when it comes to sexuality, and that apparently extends to dating apps like Bumble. BuzzFeed News reports that Bumble took down photos of artist Cali Rockowitz wearing a bralette due to a policy banning swimsuit and underwear photos when indoors. Apparently, a bikini top or shirtless look is fine as long as the shots were "taken outside." And no, you can't edit the photos to fake an outdoor shot. A spokesperson told BuzzFeed the policy took effect in 2016 after both complaints from users as well as research.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. That's where audio messages come in. You speak to your phone and send a voice message straight to your friends and family. I don't know about you, but I ignore incoming calls more often than not with the never-ending robocalls.

Artificial Intelligence (AI) is not just a buzzword, but a crucial part of the technology landscape. AI is changing every industry and business function, which results in increased interest in its applications, subdomains and related fields. This makes AI companies the top leaders driving the technology swift. AI helps us to optimise and automate crucial business processes, gather essential data and transform the world, one step at a time. From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence. As big enterprises are busy acquiring or merging with other emerging inventions, small AI companies are also working hard to develop their own intelligent technology and services. By leveraging artificial intelligence, organizations get an innovative edge in the digital age. AI consults are also working to provide companies with expertise that can help them grow. In this digital era, AI is also a significant place for investment. AI companies are constantly developing the latest products to provide the simplest solutions. Henceforth, Analytics Insight brings you the list of top 100 AI companies that are leading the technology drive towards a better tomorrow. AEye develops advanced vision hardware, software, and algorithms that act as the eyes and visual cortex of autonomous vehicles. AEye is an artificial perception pioneer and creator of iDAR, a new form of intelligent data collection that acts as the eyes and visual cortex of autonomous vehicles. Since its demonstration of its solid state LiDAR scanner in 2013, AEye has pioneered breakthroughs in intelligent sensing. Their mission was to acquire the most information with the fewest ones and zeros. This would allow AEye to drive the automotive industry into the next realm of autonomy. Algorithmia invented the AI Layer.

So you just got a smart speaker as a holiday present. Now what to do with them? You've come to the right place. On command, by saying "Hey Siri," for the HomePod, "Hey Google" for the Nest Audio or "Alexa," on Echo speakers, you can instruct them to play music of your choice, either via a subscription service, or more generically, as part of a themed radio station via the Pandora service. Amazon's speakers play music from Amazon Music, Spotify, Apple Music, Pandora and iHeartRadio, while Apple plays just from Apple Music and Pandora.

In this paper, we investigate the recommendation task in the most common scenario with implicit feedback (e.g., clicks, purchases). State-of-the-art methods in this direction usually cast the problem as to learn a personalized ranking on a set of items (e.g., webpages, products). The top-N results are then provided to users as recommendations, where the N is usually a fixed number pre-defined by the system according to some heuristic criteria (e.g., page size, screen size). There is one major assumption underlying this fixed-number recommendation scheme, i.e., there are always sufficient relevant items to users' preferences. Unfortunately, this assumption may not always hold in real-world scenarios. In some applications, there might be very limited candidate items to recommend, and some users may have very high relevance requirement in recommendation. In this way, even the top-1 ranked item may not be relevant to a user's preference. Therefore, we argue that it is critical to provide a dynamic-K recommendation, where the K should be different with respect to the candidate item set and the target user. We formulate this dynamic-K recommendation task as a joint learning problem with both ranking and classification objectives. The ranking objective is the same as existing methods, i.e., to create a ranking list of items according to users' interests. The classification objective is unique in this work, which aims to learn a personalized decision boundary to differentiate the relevant items from irrelevant items. Based on these ideas, we extend two state-of-the-art ranking-based recommendation methods, i.e., BPRMF and HRM, to the corresponding dynamic-K versions, namely DK-BPRMF and DK-HRM. Our experimental results on two datasets show that the dynamic-K models are more effective than the original fixed-N recommendation methods.

It's been a few years since Alexa first started listening to me everywhere. After an initial honeymoon with an original Echo Dot (a device I still maintain is the best alarm clock ever), I've slowly filled my house with Amazon's review units. These days, I mostly break out Google- and Apple-made speakers for comparison testing. The fact is, Amazon's smart speakers work so well, there has never been a reason to switch my whole system. In terms of price, compatibility, audio quality--and now, thanks to a sleek, round redesign, looks--the new Echo remains one of the best smart speakers for most people.

One way to build out a smart home is to buy lots of components--sensors, smart bulbs, security cameras, speakers, and whatnot--and connect them all to a hub that helps them communicate with each other and with you, via your smartphone. But let's be real: That can involve spending a lot of money and investing a lot of time. If your wants and needs are simpler, just a few relatively inexpensive products will deliver most of the conveniences a high-end smart home can deliver, and on a much more modest budget. And if you make sure those smart home products are compatible with each other, you'll build a solid foundation that you can expand over time. The key is knowing which smart home products don't depend on a smart home hub to operate.

It's been more than a year since Amazon introduced the Echo Frames to the world, and this month, the connected glasses finally became widely available for purchase. For $250, you get hands-free access to Alexa wherever you go. Plus, you can take calls, play music and hear your notifications through the device's open-ear speakers, while still being aware of your surroundings. To be clear, though, that's pretty much all the Echo Frames do; there's no display or camera. Still, there's potential here, especially for people who already wear glasses and want to interact with their phone without touching it.

We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling. Specifically, we encode syntactic knowledge into the Transformer encoder by jointly training it to predict syntactic parse ancestors and part-of-speech of each token via multi-task learning. Our model is based on self-attention and feed-forward layers and does not require external syntactic information to be available at inference time. Experiments show that on two benchmark datasets, our models with only two Transformer encoder layers achieve state-of-the-art results. Compared to the previously best performed model without pre-training, our models achieve absolute F1 score and accuracy improvement of 1.59% and 0.85% for slot filling and intent detection on the SNIPS dataset, respectively. Our models also achieve absolute F1 score and accuracy improvement of 0.1% and 0.34% for slot filling and intent detection on the ATIS dataset, respectively, over the previously best performed model. Furthermore, the visualization of the self-attention weights illustrates the benefits of incorporating syntactic information during training.