In a new report launched by Hays today, 'Recruitment Remodelled' examines the evolution of the recruitment industry where the traditional human-centric skills of matching candidates with organisations are now working hand-in-hand with data science, machine learning, predictive analytics and other digital tools and technologies. Alistair Cox, Hays plc CEO, says, "In my ten years as CEO of Hays, I have never seen the recruitment and staffing industry evolving as rapidly as it is today. Technology, the dynamics of the digital world, and the advent of data science and machine learning are fuelling these changes. "For business, the fundamental underlying issue of finding the best talent for their organisation hasn't changed, but what has evolved across our industry is how to manage this process in an age where technology has brought new ways of finding top-quality talent." The traditional approach to recruitment - defined by Hays as'Advertise & Apply' - has relied upon organisations promoting their vacancies across multiple channels to solicit applications from jobseekers. This long-established model is no longer enough in today's digital world as it is primarily directed at the active, rather than the passive, jobseeker community. In addition, the ease at which candidates can respond to online job advertisements has led to an unwieldy process with high volumes of responses being received, many of which prove to be unsuitable for the role. A new recruitment model has been developed and deployed by Hays - defined as'Find & Engage' - that combines the best practice recruitment techniques and established candidate relationships with the new opportunities presented by digital technology, data science and machine learning. This approach is designed to maximise the likelihood of organisations finding the best talent by enabling them to search beyond those active jobseekers, and reach deep into a much wider pool of passive candidates. Alistair added, "There has always been a real art to recruiting the best talent, built around the development of trusted relationships and an ability to assess the compatibility of a candidate for the vacancy.

The latest Android smartphone from Chinese upstart OnePlus hopes to tempt users looking for the new all-screen experience, but at less than half the price of an iPhone X. The 5T is a revamped version of the 5 launched in June and marks the second time the company has updated its smartphone line more than once in a year. According to co-founder Carl Pei, this is because "when we've got new technology ready to go we don't want to leave our users with older devices". Launched in New York, the 5T is essentially a OnePlus 5 on the inside, with the same Qualcomm Snapdragon 835 processor, memory and storage options, fast-charging system and Oxygen OS software based on last year's Android 7.1.1, It even costs the same, priced at £449, undercutting rivals by at least several hundred pounds. But the 1080p AMOLED screen has been stretched to the now popular 18:9 ratio, filling the front of the display with thin bezels at the top, bottom and sides.

Dash Charge – a feature first introduced in the OnePlus 3 – also features in the 5T, allowing the device to gain a day's power in just 30 minutes of charging, even while using GPS of games One of the key new features in the OnePlus 5T is facial recognition, known as Face Unlock, which has been added to the OxygenOS operating system. Dash Charge – a feature first introduced in the OnePlus 3 – also features in the 5T, allowing the device to gain a day's power in just 30 minutes of charging, even while using GPS or games. The OnePlus 5T is available in both Slate Grey and Midnight Black, with 64GB and 128GB versions. It will be available in the US and in Europe from November 21, with prices starting from £499/$499/€499. The OnePlus 5T is available in both Slate Grey and Midnight Black, with 64GB and 128GB versions.

Personal robots, such as Amazon Echo and Google Home, have come a long way in recent years. But fundamentally, they're still stationary speakers whose defining expression is a light that turns on when you speak. It's not just that he--and I use the term he here, because that's how Jibo refers to himself--looks like something straight out of a Pixar movie, with a big, round head and a face that uses animated icons to convey emotion. It's not just that his body swivels and swerves while he speaks, as if he's talking with his nonexistent hands. It's not just that he can giggle and dance and turn to face you, wherever you are, as soon as you say, "Hey, Jibo."

Sure, Facebook has "M", Google has "Google Now", and Siri's voice isn't always that of a woman. But it does feel worth noting that (typically male-dominated) engineering groups routinely give women's names to the things you issue commands to. Is artificial intelligence work about Adams making Eves? The response to this critique is usually about the voices people trust and find easy to understand. Adrienne LaFrance over at The Atlantic does a good job discussing those points, so go read her article.

It is no coincidence that Artificial Intelligence and Machine Learning are used or will be used for such technologies that will define how the real future of many industries. However, one can not and should not talk about the future, but of the present. In the field of communication and marketing, a lot of algorithm-based tools can be used daily, which can suggest the best thing to do, lighten up the work, or be able to help us in more basic tasks, writing a message in the best possible way. Think also of customer care services, which in many cases consist of real chatbots that can answer many different questions or needs. The artificial intelligence has come to our lives for some time now, even outside the scope of work, if you think of apps that we listen to music, play online, or allow us to get in touch with our friends.

Many efforts have been devoted to training generative latent variable models with autoregressive decoders, such as recurrent neural networks (RNN). Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech. We unify successful ideas from recently proposed architectures into a stochastic recurrent model: each step in the sequence is associated with a latent variable that is used to condition the recurrent dynamics for future steps. Training is performed with amortized variational inference where the approximate posterior is augmented with a RNN that runs backward through the sequence. In addition to maximizing the variational lower bound, we ease training of the latent variables by adding an auxiliary cost which forces them to reconstruct the state of the backward recurrent network. This provides the latent variables with a task-independent objective that enhances the performance of the overall model. We found this strategy to perform better than alternative approaches such as KL annealing. Although being conceptually simple, our model achieves state-of-the-art results on standard speech benchmarks such as TIMIT and Blizzard and competitive performance on sequential MNIST. Finally, we apply our model to language modeling on the IMDB dataset where the auxiliary cost helps in learning interpretable latent variables. Source Code: \url{https://github.com/anirudh9119/zforcing_nips17}

Generating music medleys is about finding an optimal permutation of a given set of music clips. Toward this goal, we propose a self-supervised learning task, called the music puzzle game, to train neural network models to learn the sequential patterns in music. In essence, such a game requires machines to correctly sort a few multisecond music fragments. In the training stage, we learn the model by sampling multiple non-overlapping fragment pairs from the same songs and seeking to predict whether a given pair is consecutive and is in the correct chronological order. For testing, we design a number of puzzle games with different difficulty levels, the most difficult one being music medley, which requiring sorting fragments from different songs. On the basis of state-of-the-art Siamese convolutional network, we propose an improved architecture that learns to embed frame-level similarity scores computed from the input fragment pairs to a common space, where fragment pairs in the correct order can be more easily identified. Our result shows that the resulting model, dubbed as the similarity embedding network (SEN), performs better than competing models across different games, including music jigsaw puzzle, music sequencing, and music medley. Example results can be found at our project website, https://remyhuang.github.io/DJnet.

There are a whole host of apps and games that are aimed at teaching kids to code, but not all of them come with stellar IP partnerships. But that's just what Bloxels, Pixel Press's video game building system aimed at teaching kids how to be content creators, has. Today, Pixel Press and the toy company Mattel announced that the companies were partnering to release a Star Wars edition of Bloxels. You can buy it today for $50 at retailers nationwide. Bloxels aim to help kids learn about the mechanics of video game design and storytelling by putting them in control of creating a game.

A few weeks ago, Jochen Leidner, Director of Research at Thomson Reuters, discussed and dissected artificial intelligence (AI). Leidner offered some case studies, conducted by his team, to demonstrate successful ways of building AI products. However, before getting into specifics, Leidner gave a very brief explanation of what AI is and what it does. "It automates things that we deemed, maybe, not automatable a couple of years ago – if it works, or to an extent." Dr. Leidner, who is also the Royal Academy of Engineering Visiting Professor of Data Analytics at the University of Sheffield, made the point very early on that AI is a tool to be utilized with other forms of intelligence.