I personally love what Victor Frankenstein attempts (although it meets a terrible end) but I must admit how scary this era of technological development is. Humans are forging ahead in the field of artificial intelligence, quickly replacing manpower as we switch from manual to automatic for a number of tasks. Robots are already powering our homes, working in our labs, picking our songs and now, they will be directing our movies. SEE ALSO: This machine writes poetry and it might be better than you at it! Up next, artificial intelligence is all set to take over major roles in filmmaking.

Farhad Bulbul is with the Department of Mathematics, Jessore University of Science and Technology, Bangladesh (email: farhad@just.edu.bd). Saiful Islam is with the Department of Mathematics, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Bangladesh. Dr. Hazrat Ali is with the Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Pakistan (email: hazratali@cuiatd.edu.pk). Abstract-- In this paper, we present an approach for identification of actions within depth action videos. First, we process the video to get motion history images (MHIs) and static history images (SHIs) corresponding to an action video based on the use of 3D Motion Trail Model (3DMTM). We then characterize the action video by extracting the Gradient Local Auto-Correlations (GLAC) features from the SHIs and the MHIs. The two sets of features i.e., GLAC features from MHIs and GLAC features from SHIs are concatenated to obtain a representation vector for action. Finally, we perform the classification on all the action samples by using the l2-regularized Collaborative Representation Classifier (l2-CRC) to recognize different human actions in an effective way. We perform evaluation of the proposed method on three action datasets, MSR-Action3D, DHA and UTD-MHAD. Through experimental results, we observe that the proposed method performs superior to other approaches. I. INTRODUCTION Research in human action recognition (HAR) is considered as one of the most interesting domains of computer vision. The action recognition system is being extensively applied in human security system, medical science, social awareness, and entertainment [1], [2], [3], [4].. Indeed, to develop an applicable action recognition system, researchers still need to win against the odds due to diversity in human body sizes, appearances, postures, motions, clothing, camera motions, viewing angles, and illumination. In the early stage, the human action recognition system was developed by researchers based on RGB data [5], [6], [7], [8].

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the attributes are not isolated but connected with each other, which forms a knowledge graph (KG). In this paper, we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG. To automatically discover both high-order structure information and semantic information of the KG, we sample from the neighbors for each entity in the KG as their receptive field, then combine neighborhood information with bias when calculating the representation of a given entity. The receptive field can be extended to multiple hops away to model high-order proximity information and capture users' potential long-distance interests. Moreover, we implement the proposed KGCN in a minibatch fashion, which enables our model to operate on large datasets and KGs. We apply the proposed model to three datasets about movie, book, and music recommendation, and experiment results demonstrate that our approach outperforms strong recommender baselines.

Not if David Hanson has anything to do with it. The founder of Hanson Robotics, David has been dreaming of making sentient robots since childhood and while, for many of us, our childhood dreams never come true (I'm still waiting for my invite to become the sixth Spice Girl), Hanson's endless drive to push the field of robotics and AI further has meant that we're closer than ever to awakening consciousness in machines. To put it into context, Hanson is the mastermind behind the eerily lifelike robot Sophia, who uses sophisticated algorithms to track faces and engage in conversations with emotion. Quick witted enough to make Piers Morgan laugh on Good Morning Britain and win a game of rock, paper, scissors on The Tonight Show Starring Jimmy Fallon, Sophia's ability to socialise with humans might freak you out, but you can't deny it's impressive. In 2017, she also became the first robot to be granted citizenship to any country.

WASHINGTON/SAN, FRANCISCO - Some of the biggest names in media and tech are gearing up to move into streaming in what could be a major challenge to market leader Netflix. Apple is expected to make its move with an announcement March 25 on its media plans, with a war chest estimated at some $1 billion and partners including stars like Jennifer Aniston and director J.J. Abrams involved in content. Walt Disney Co. has announced its new streaming service Disney will launch this year, as will another from WarnerMedia, the newly acquired media-entertainment division of AT&T. The new entrants, with more expected, could launch a formidable challenge to Netflix, which has some 140 million paid subscribers in 190 markets, and to other services such as Amazon and Hulu. "It's really going to change the industry," said Alan Wolk, co-founder of the consulting firm TVREV who follows the sector.

She discussed how to use artificial intelligence for enhanced interaction, communication, and greater understanding.

Artificial intelligence (AI) has come into the mainstream in ways many people don't consciously realize.

Despite what mainstream media wants to led us to believe, the current state of Artificial Intelligence is not yet capable of replacing humans.

The key to successfully applying artificial intelligence in the real world is to get up close and personal with the people who'll to use it, executives agreed during a roundtable discussion about A.I. during Fortune's Brainstorm Design conference in Singapore last Thursday. "The reason we all wear black shirts, by the way, is that you can't see the blood splatters on them, because we need to be so close to the action that we're getting covered in blood," said Sean Carney with a smile. Carney, who indeed was wearing a black dress shirt, is chief design officer for Philips, which claims substantial market share for A.I.-driven healthcare tools. Many clinicians worry that new technologies will put them out of a job. It's the wrong mindset, Carney said.

Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (Uria et al., 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation.