We'll admit, we weren't entirely sure what to expect when we agreed to judge the annual Best of CES Awards without an in-person show. How many companies would show up to an online-only show? What would we lose without being able to wander the halls of a massive convention center and see the products up close? As it turns out, we needn't have worried. More than 1,900 brands, big and small, turned up this year, according to the Consumer Technology Association, the industry group that organizes the show each year. What's more, many companies found socially distant ways to show us their latest and greatest in person, ahead of the show. In the end, we had enough fodder for 14 categories covering hardware and services in every sector from home theater to transportation to accessibility tech. We'll announce the winners tomorrow at 4:30pm ET during a ceremony on our virtual stage, which we'll livestream to Engadget.com We're also continuing tradition and opening up voting for our People's Choice Award -- our reader poll is live now and closes tomorrow, ahead of the ceremony. Please be sure to vote, and congrats to all of the finalists! The technology underpinning the Mudra Band might seem fanciful: sensors capture neural electrical impulses in the wrist and map them onto specific movements like a swipe or a tap, essentially letting you control an Apple Watch with subtle finger movements on one hand. There's no doubt the benefit of convenience -- you can operate your watch when your hands are wet or dirty, for instance.

From smartphones to TVs, Sony is known for its impressive range of electronic products. Now, the tech giant is turning its attention to drones, launching a new spin-off brand called Airpeak. Airpeak is said to be the industry's smallest class of drone that can be equipped with Sony's Alpha mirrorless camera system. Sony hopes its new drones will support the creativity of video creators, and is even seeking collaborators to participate in the Airpeak project. Airpeak is said to be the industry's smallest class of drone equipped with Sony's Alpha mirrorless camera system The Airpeak model shown off at CES features a quadcopter design, with two landing gear extensions that retract upwards during flight.

In the current era, people and society have grown increasingly reliant on Artificial Intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, health care, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great efforts of designing more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI's indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation.

Even though the event is virtual, CES delivers no shortage of big screens or nifty gadgets. But let's be real: both of those are lame compared to the robots. Every year, tech lovers are wooed to CES by the prospects of a digital future inching closer towards that of The Jetsons: with cars taking to the skies and robots tending to our needs. We're still years away from the flying cars, but finding robots to help us with everyday chores, and maybe offer us a little companionship, might be closer than we think. Here's a peek at some of the robots at CES hoping to lend a metal hand. CES 2021:Hologram technology inspired by'Star Wars' could bring'new dimension' to smartphones Samsung trotted out not one, but two robots – and they're both absolutely adorable.

Despite the show being wholly digital this year, Panasonic's automotive division still had plenty to show off at CES 2021. On Monday, the company unveiled five futuristic technologies that will help make the autonomous vehicles of tomorrow more capable and more comfortable. Those include wireless Wi-Fi towing cameras, a Dolby Atmos surround sound system, and augmented reality HUDs. First up, Panasonic Automotive has unveiled its "first fully wireless Wi-Fi camera." This ruggedized camera captures 1080p at 60 fps, connects via the vehicle's Wi-Fi network directly to the infotainment system display, and is designed to stick onto the trailer you're towing to provide an unobstructed view of the traffic conditions around the vehicle.

A desirable property of learning systems is to be both effective and interpretable. Towards this goal, recent models have been proposed that first generate an extractive explanation from the input text and then generate a prediction on just the explanation called explain-then-predict models. These models primarily consider the task input as a supervision signal in learning an extractive explanation and do not effectively integrate rationales data as an additional inductive bias to improve task performance. We propose a novel yet simple approach ExPred, that uses multi-task learning in the explanation generation phase effectively trading-off explanation and prediction losses. And then we use another prediction network on just the extracted explanations for optimizing the task performance. We conduct an extensive evaluation of our approach on three diverse language datasets -- fact verification, sentiment classification, and QA -- and find that we substantially outperform existing approaches.

Every Act comes with a new battle pass, which offers rewards in exchange for experience points, gained by playing the game. The new Episode 2 battle pass will cost $10, and include three new skin lines, as well as player cards, gun buddies, titles and sprays. One of the new sets of skins, titled Infinity, will have three color variants, a novelty for weapons in the battle pass. Players who opt not to pay will still receive a few free rewards spaced throughout the pass.

In 2012, economist Robert Gordon published a controversial paper in which he argued that economic growth was largely over, due in no small part to our failure to maintain the engines of innovation in recent decades. A study from the Stanford Institute for Economic Policy Research supported his general thesis and argued that while we're spending even more money on creativity and innovation, our returns are flatlining. And this investment is not only in dollars, as the research revealed roughly 20 times as many people work in R&D today as did in 1930. Why has creating things become so difficult? Researchers from Northwestern University attempt to answer this in a paper that shows a growing percentage of today's creation is what's known as recombination.

Users install many apps on their smartphones, raising issues related to information overload for users and resource management for devices. Moreover, the recent increase in the use of personal assistants has made mobile devices even more pervasive in users' lives. This paper addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and recommendation. The former is the key component of a unified mobile search system: a system that addresses the users' information needs for all the apps installed on their devices with a unified mode of access. The latter, instead, predicts the next apps that the users would want to launch. Here we focus on context-aware models to leverage the rich contextual information available to mobile devices. We design an in situ study to collect thousands of mobile queries enriched with mobile sensor data (now publicly available for research purposes). With the aid of this dataset, we study the user behavior in the context of these tasks and propose a family of context-aware neural models that take into account the sequential, temporal, and personal behavior of users. We study several state-of-the-art models and show that the proposed models significantly outperform the baselines.

To apply neural sequence models such as the Transformers to music generation tasks, one has to represent a piece of music by a sequence of tokens drawn from a finite set of pre-defined vocabulary. Such a vocabulary usually involves tokens of various types. For example, to describe a musical note, one needs separate tokens to indicate the note's pitch, duration, velocity (dynamics), and placement (onset time) along the time grid. While different types of tokens may possess different properties, existing models usually treat them equally, in the same way as modeling words in natural languages. In this paper, we present a conceptually different approach that explicitly takes into account the type of the tokens, such as note types and metric types. And, we propose a new Transformer decoder architecture that uses different feed-forward heads to model tokens of different types. With an expansion-compression trick, we convert a piece of music to a sequence of compound words by grouping neighboring tokens, greatly reducing the length of the token sequences. We show that the resulting model can be viewed as a learner over dynamic directed hypergraphs. And, we employ it to learn to compose expressive Pop piano music of full-song length (involving up to 10K individual tokens per song), both conditionally and unconditionally. Our experiment shows that, compared to state-of-the-art models, the proposed model converges 5--10 times faster at training (i.e., within a day on a single GPU with 11 GB memory), and with comparable quality in the generated music.