After playing through some Billie Eilish tracks in Beat Saber, soon you'll also be able to kick back and listen to a limited-edition Echo Studio sporting the cover of her latest album, "Happier Than Ever." Beyond the beige fabric and Eilish's visage, the $230 speaker is no different than the standard $200 Echo Studio. That's a shame if you were hoping for some sort of upgrade, but if it's any consolation, we adored the Echo Studio's beefy hardware when it launched two years ago. It's one of the few smart speakers built for 3D Audio, and it has more than enough power to blast all of your favorite tunes. The Billie Eilish Limited-Edition Echo Studio also comes with a six-month subscription to Amazon Music, typically a $48 value.

Machine learning, with advancements in natural language processing and deep learning, has been actively used in studying political bias on social media. But the key challenge to model political bias is the requirement of human effort to label the seed social media posts to train machine learning models. Although very effective, this approach has disadvantages in the time-consuming data labeling process and the cost to label significant data for machine learning models is significantly higher. The web offers invaluable data on political bias starting from biased news media outlets publishing articles on socio-political issues to biased user discussions about several topics in multiple social forums. In this work, we introduce a novel approach to label political bias for social media posts directly from US congressional speeches without any human intervention for downstream machine learning models.

Low-rank matrix approximation is one of the central concepts in machine learning, with applications in dimension reduction, de-noising, multivariate statistical methodology, and many more. A recent extension to LRMA is called low-rank matrix completion (LRMC). It solves the LRMA problem when some observations are missing and is especially useful for recommender systems. In this paper, we consider an element-wise weighted generalization of LRMA. The resulting weighted low-rank matrix approximation technique therefore covers LRMC as a special case with binary weights. WLRMA has many applications. For example, it is an essential component of GLM optimization algorithms, where an exponential family is used to model the entries of a matrix, and the matrix of natural parameters admits a low-rank structure. We propose an algorithm for solving the weighted problem, as well as two acceleration techniques. Further, we develop a non-SVD modification of the proposed algorithm that is able to handle extremely high-dimensional data. We compare the performance of all the methods on a small simulation example as well as a real-data application.

Artificial intelligence (AI) is increasingly adopted in many industries and our day-to-day lives.

If you've been using smartphones for the past quinquennial or even more, you'll understand exactly where this is coming from. I'm sorry, but if you're on your first or second smartphone, you were already born into this to begin with. You see, going back in time all the way to the Nokia 808 PureView, it was the phone that revolutionized smartphone photography. It was the first mobile device on the market with a whopping 40MP camera sensor. The Nokia Lumia 1020 followed, with the same size sensor and Zeiss lens.

New York-based Blackbird.AI has closed a $10 million Series A as it prepares to launched the next version of its disinformation intelligence platform this fall. The Series A is led by Dorilton Ventures, along with new investors including Generation Ventures, Trousdale Ventures, StartFast Ventures and Richard Clarke, former chief counter-terrorism advisor for the National Security Council. Existing investor NetX also participated. Blackbird says it'll be used to scale up to meet demand in new and existing markets, including by expanding its team and spending more on product dev. The 2017-founded startup sells software as a service targeted at brands and enterprises managing risks related to malicious and manipulative information -- touting the notion of defending the "authenticity" of corporate marketing.

There is a growing number of vendors big and small going hard to the hoop to make processors for artificial intelligence workloads.

When you read the word'robot', what comes into your mind? Most people think of a'metal man', a large humanoid figure with a square head, rather like the Tin Man in the film The Wizard of Oz or R2D2 in Star Wars. But if you ask people whether they have come across a robot in their own lives, they will usually describe a robot vacuum cleaner, or lawnmower. Successful robots keep it simple. Robots lawnmowers and vacuum cleaners are really not amazingly intelligent.

'Fox & Friends Weekend' co-host Pete Hegseth reacts to the U.S. drone that killed civilians instead of ISIS-K members in Afghanistan. After previously avoiding the botched U.S. drone strike that killed Afghan civilians instead of terrorists, both CNN and NBC's Sunday morning news shows dedicated just seconds of coverage to the Biden foreign policy blunder. On Friday, the Pentagon confirmed that the Aug. 28 drone strike was a "tragic mistake" that resulted in ten dead civilians, including seven children, which was meant to be in response to the Aug. 26 terrorist attack outside the Kabul airport that killed 13 U.S. servicemen. This came one week after the New York Times published a stunning visual investigation that came to the same conclusion. The Biden administration had announced that "two high profile" ISIS-K fighters who were dubbed as "planners and facilitators" of the suicide bombing were killed in the strike.

With the development of social platforms, people are more and more inclined to combine into groups to participate in some activities, so group recommendation has gradually become a problem worthy of research. For group recommendation, an important issue is how to obtain the characteristic representation of the group and the item through personal interaction history, and obtain the group's preference for the item. For this problem, we proposed the model GIP4GR (Graph Neural Network with Interaction Pattern For Group Recommendation). Specifically, our model use the graph neural network framework with powerful representation capabilities to represent the interaction between group-user-items in the topological structure of the graph, and at the same time, analyze the interaction pattern of the graph to adjust the feature output of the graph neural network, the feature representations of groups, and items are obtained to calculate the group's preference for items. We conducted a lot of experiments on two real-world datasets to illustrate the superior performance of our model.