If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. The holidays are underway, and Christmas is inching closer and closer. If you've still got shopping to do, Free Shipping Day is the perfect time to get a lot done. However, if you're looking to pick up a tech gadget or smart device for a loved one, there are a few great options to be found on Amazon.

"Star Wars: Episode VII The Last Jedi" opens in theaters today./Courtesy of Walt Disney. A film called the Empire Strikes Back set the bar for sci-fi sequels. It's still, to this day, considered by many people to be the best Star Wars film. When 2015's Force Awakens debuted to somewhat surprising critical acclaim, expectations for its sequel to follow in Empire's footsteps were unavoidable. This was the daunting task director Rian Johnson faced with The Last Jedi, the latest Star Wars installment to hit theaters.

Accenture Uses Artificial Intelligence to Help the Elderly Better Navigate Their Care and Improve Their Well-Being LAS VEGAS and LONDON; Nov. 28, 2017 – Accenture (NYSE: ACN) has completed a pilot program that uses artificial intelligence (AI) and the ease of voice to help older people manage the daunting challenges of navigating their care delivery and well-being. The Accenture Liquid Studio in London developed an AI-powered platform (the Accenture Platform) that can learn user behaviors and preferences and suggest activities to support the overall physical and mental health of individuals ages 70 and older. The Accenture platform, which runs on the Amazon Web Services (AWS) cloud, includes a'Family and Carer' portal that lets family and caregivers check on the individual's daily activities, such as whether they have taken their medication or made new requests for caregivers. The Accenture platform can also spot abnormalities in behavior and alert family or friends, based on user defined permissions. Elder Care Pilot: Accenture Uses AI to Help Navigate Elder Care Other services provided by the Accenture platform helped participants find local events as well as potential new friends, encouraging them to become more active and social.

For the past few years, devices and services powered by artificial intelligence have been the most popular and interesting items at tech shows around the globe. This will surely be true in 2018, but there is already a noticeable shift in the market. First, mainstream consumer products are increasingly integrating AI, and not just high-end, premium products. Second, many of these products have intelligence built into the edge device, instead of relying on the cloud. These trends are improving accessibility, privacy, and effectivity of intelligent applications, making them more widely adopted. With the world's biggest consumer electronics and technology trade show right around the corner, here's a look at the innovative and interesting products that will garner attention this year, and the technology that enables them.

Streaming services have changed the way in which we experience content. While recommendation systems previously focused on presenting you with content you might want to purchase for later consumption, modern streaming platforms have to focus instead on recommending content you can, and will want to, enjoy in the moment. Since any piece of content is immediately accessible, the streaming model enables new methods of discovery in the form of personalized radios or recommendation playlists, in which the focus is now more on generating sequences of similar songs that go well together. With now over 700 million songs streamed every month, Anghami is the leading music streaming platform in the MENA region. What this also means, is that the amount of data generated by all those streams proves to be an invaluable training set that we can use to teach machine learning models to better understand user tastes, and improve our music recommendations.

USA Network has renewed its cult-hit series Mr. Robot for season 4, just as season 3 is about to air its finale episode. Mr. Robot season 3 threw several big twists at viewers, including some shocking character deaths, and a game-changing event in the form of a massive terrorist attack, carried out by Elliot's alter-ego Mr. Robot, Tyrell Wellick, The Dark Army hackers, and Angela. Needless to say, the latter has not been coping well. The season 3 finale will see Elliot striking back against the Dark Army, while The Dark Army has their sights set on Elliot. The fates of side characters like Tyrell, Darlene, and Angela's sanity also hang in the balance.

The Dangers of Artificial Intelligence - Robot Sophia jokes and makes fun of Elon Musk - A.I. 2017 - 2ndEarth Alternative (22/04/2017) Robots are more advanced than many of us may think. They are fully capable of having real world conversations, and have the mental and cognitive structures to eventually surpass humanity in 3-5 years according to Hansen Robotic chief scientist Ben Goertzel. Elon Musk with his Artificial Intelligence company OpenAI, which studies the potential dangers associated with A.I. has been adamantly warning humanity that Artificial Intelligence poses one of the biggest impending dangers humanity must confront in the coming years. The Russians are also in the A.I. game, and have essentially built a Terminator like war robot termed Fedor. They have a robot that doesn't just accurately shoots targets, but also drives a car, which puts a whole new terms to the concepts of Self driving cars.

The news is likely to be divisive across a dance music community already locked in terminal (and, often, insufferable) arguments as to what constitutes a real DJ. You only need to Google phrases like'sync button' for evidence. The last version of the programme had already taken major steps towards allowing anyone to mix singles seamlessly, proving popular thanks to support from Spotify and the way it uses the new MacBook Pro Touch Bar, one of the biggest changes in the Apple laptop portfolio for some time. This new update can now scan tracks to find the perfect intro and outro based on what's already playing, advising on ideal fade timings while filtering and EQing to suit, and can even search your library to make recommendations as to what will sound best next. The news comes not long after we wrote about the world's first machine learning producer arriving, SKYGGE, two months after Saudi Arabia gave a'female' robot citizenship (despite the fact women in the country can't vote) and Burning Man revealed its theme for next year's festival will be Artificial Intelligence, and in the wake of Facebook pulling the plug on developing computers that can write their own code after a language was created that nobody, aside from the machines, could understand.

Music highlights are valuable contents for music services. Most methods focused on low-level signal features. We propose a method for extracting highlights using high-level features from convolutional recurrent attention networks (CRAN). CRAN utilizes convolution and recurrent layers for sequential learning with an attention mechanism. The attention allows CRAN to capture significant snippets for distinguishing between genres, thus being used as a high-level feature. CRAN was evaluated on over 32,000 popular tracks in Korea for two months. Experimental results show our method outperforms three baseline methods through quantitative and qualitative evaluations. Also, we analyze the effects of attention and sequence information on performance.

Noise is an inherent issue of low-light image capture, one which is exacerbated on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance. However, there are two downsides of long exposures: (a) bright regions can exceed the sensor range, and (b) camera and scene motion will result in blurred images. Another way of gathering more light is to capture multiple short (thus noisy) frames in a "burst" and intelligently integrate the content, thus avoiding the above downsides. In this paper, we use the burst-capture strategy and implement the intelligent integration via a recurrent fully convolutional deep neural net (CNN). We build our novel, multiframe architecture to be a simple addition to any single frame denoising model, and design to handle an arbitrary number of noisy input frames. We show that it achieves state of the art denoising results on our burst dataset, improving on the best published multi-frame techniques, such as VBM4D and FlexISP. Finally, we explore other applications of image enhancement by integrating content from multiple frames and demonstrate that our DNN architecture generalizes well to image super-resolution.