With the increasing popularity of video sharing websites such as YouTube and Facebook, multimodal sentiment analysis has received increasing attention from the scientific community. Contrary to previous works in multimodal sentiment analysis which focus on holistic information in speech segments such as bag of words representations and average facial expression intensity, we develop a novel deep architecture for multimodal sentiment analysis that performs modality fusion at the word level. In this paper, we propose the Gated Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model that is composed of 2 modules. The Gated Multimodal Embedding alleviates the difficulties of fusion when there are noisy modalities. The LSTM with Temporal Attention performs word level fusion at a finer fusion resolution between input modalities and attends to the most important time steps. As a result, the GME-LSTM(A) is able to better model the multimodal structure of speech through time and perform better sentiment comprehension. We demonstrate the effectiveness of this approach on the publicly-available Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis (CMU-MOSI) dataset by achieving state-of-the-art sentiment classification and regression results. Qualitative analysis on our model emphasizes the importance of the Temporal Attention Layer in sentiment prediction because the additional acoustic and visual modalities are noisy. We also demonstrate the effectiveness of the Gated Multimodal Embedding in selectively filtering these noisy modalities out. Our results and analysis open new areas in the study of sentiment analysis in human communication and provide new models for multimodal fusion.

Our entire bodies could be swapped out with robotic parts as soon as 2070, says robotics journalist and expert Chris Middleton. He says we're not far from a future where anyone can buy upgraded body parts that provide superhuman powers. 'Biohackers' are already upgrading their bodies with implants such as chips that let them open doors with a wave of the hand, so the predictions aren't too far-fetched. Our entire body could be swapped out with robot parts as soon as 2070, says robotics journalist and expert Chris Middleton. He says we're not far from a future where anyone can buy upgraded body parts that provide superhuman powers (stock image) 'At some point, 50 or 100 years in the future, might a whole human body become replaceable, editable or upgradable?

Well, congratulations everyone - we just about made it through 2017. From leaps forward in Artificial Intelligence and the rise of the interface-less interface, to the controversial role social media played in world events and the emergence of Bitcoin, 2017 was a year of big change and big reflection in the world of brand and technology. Last January, I made a few predictions about what 2017 would hold for designers. Some of them didn't quite come to fruition - hamburger menus are still knocking around, and standards in VR and MR are yet to be defined. But while I never could have predicted some of the craziest news stories that were to emerge throughout the year, there were a few things on the list that ended up having a big impact in the design community.

First published in 1873, the Shinano Mainichi Shimbun is one of Japan's oldest dailies. Headquartered in Nagano, northwest of Tokyo, it claims a morning-edition circulation of 487,000 copies and distribution to 61% of households in Nagano Prefecture. "The third-wave AI is set to become a trend of great relevance, and now is the time to make concerted efforts in improving the newspaper production workflow as well," says Hiroshi Misawa, the paper's managing director. The Shinmai, as it's known, plans to roll out the system in April for its cable TV news summary service, with an eye to speeding up news updates.

I always say that AI is the engine of the fourth industrial revolution. For example, people talk a lot about the changes autonomous vehicles will bring, but you wouldn't have autonomous vehicles without the underpinning of artificial intelligence.

As the Fourth Industrial Revolution disrupts every facet of our lives, business leaders and HR practitioners face a challenging but exciting time. Technologies such as machine learning and predictive analytics will become core to how the working world attracts, retains, and rewards top talent.

Echo Spot brings you everything you love about Alexa, in an all-new stylish and compact design that can show you things. Just ask to see the weather, get the news with a video flash briefing, set a music alarm (Amazon Music, Spotify & TuneIn supported), see lyrics with Amazon Music, see your calendar, browse and listen to Audible audiobooks, and more. Personalise your Spot with a collection of clock faces to suit your style or set a photo background from Prime Photos. Plus, make calls to friends and family between supported Echo devices or the Alexa App, or make video calls to anyone with an Echo Spot, Echo Show or the Alexa App. Echo Spot features second-generation far-field technology with four microphones, beam-forming technology and enhanced noise cancellation, so it can hear you from across the room--even while music is playing.

A terrifying new species of wasp comes equipped with a built-in saw that rivals the claw-like blades of slasher movie murderer Freddie Krueger. The parasitoid insect sports a series of jagged spines along its back, which it uses to slice its way out of its host. No bigger than a sesame seed, the tiny wasp is found in Costa Rica, but has never been spotted in the wild. Dendrocerus scutellaris sports a series of jagged spines along its back (circled in red), which it may use to cut its way out of its host. It is only known from a few preserved insects kept in storage at London's Natural History Museum since 1985.

Hey Guys, I thought about the process and the science behind an image recognizing Neural Network. So I asked my self could it be possible to instead of training the N.N. on pictures of Objects train it on "pictures of the brain" or short clips of the brain activity(?). For example you would show a bunch of persons a picture of a dog (or just tell them to think of one) and at the same time make a clip of their brain acitvities. You then train the N.N. on the data and technically it should than be able to identify if someone is thinking of a dog. Let me now in the comments if this makes sense or if its total bullshit.

If you were compiling a list of the world's 100 oddest objects -- just the weirdest stuff that human civilization has excreted over the millennia -- then you'd have to leave room somewhere for the Voynich manuscript. It's 600 years old, written in a language no one can read, and full of diagrams no one understands. It is a genuine, bonafide, world-class mystery. This is presumably why when newsrooms around the world had a chance this week to publish stories claiming it'd been "decoded by artificial intelligence," they leapt at the opportunity. According to experts, the Voynich manuscript remains as inscrutable as ever.