DUBAI: We live in an age in which talking to a machine is no longer a futuristic concept. Many activities and fields are being shaped by innovations in artificial intelligence (AI) and technology, a phenomenon that is fundamentally altering the nature of human interaction, consumption of information and, more controversially, the nature of employment. Adoption of innovations such as voice recognition, virtual agents and biometrics could be the only way for humans to survive in the coming age of automation. Amid the surge in human-machine cooperation, many people nevertheless are eager to find out what the future holds. Will robots outsmart humans and steal their jobs?

This paper explores sequential modeling of polyphonic music with deep neural networks. While recent breakthroughs have focussed on network architecture, we demonstrate that the representation of the sequence can make an equally significant contribution to the performance of the model as measured by validation set loss. By extracting salient features inherent to the dataset, the model can either be conditioned on these features or trained to predict said features as extra components of the sequences being modeled. We show that training a neural network to predict a seemingly more complex sequence, with extra features included in the series being modeled, can improve overall model performance significantly. We first introduce TonicNet, a GRU-based model trained to initially predict the chord at a given time-step before then predicting the notes of each voice at that time-step, in contrast with the typical approach of predicting only the notes. We then evaluate TonicNet on the canonical JSB Chorales dataset and obtain state-of-the-art results.

The composer classification question has been posed for a variety of corpora, from Renaissance composers [2,3], to the narrow (and challenging) case of Haydn and Mozart string quartets [5, 8, 12, 22], and to various collections of classical era composers (most of the other papers discussed in Section 2). In this work we study an expansive collection of scores, from 13th century sacred music by Guillaume Du Fay to 20th century ragtimes by Scott Joplin. A major challenge of this task is learning from limited data. While the corpus considered here is larger than most, this is largely due to the number of composers considered (19): for specific composers, we have at most 466 scores (Bach) and as few as 22 (Japart). Small datasets are an inherent problem for composer classification: the corpus used in this work contains, for example, all of the Bach chorales and all of the Mozart string quartets. We cannot resurrect these composers and have them write us more scores to include in our corpus. This situation contrasts starkly with many learning problems, where substantial progress can be made by collecting massive datasets and exhaustively training an expressive model (usually a deep neural network) with "big data."

How can you distinguish between satire and fake news? It usually comes down to semantic and linguistic differences, but the nuances can be tough to spot. That's why researchers at George Washington University, Amazon AWS AI, and startup AdVerifai investigated a machine learning approach to classifying misleading speech. They say the AI model they developed, which outperformed the baseline, lays the groundwork for the study of additional linguistic features. Their work follows that of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), which earlier this year architected an AI model that could determine whether a source is accurate or politically prejudiced.

Telecom company Bharti Airtel on Monday announced the'acquihire' of Gurgaon based Quikmile, a start up focused on building tech-enabled logistics platforms for India. The company did not disclose the size of the'acquihire' deal, which typically entails acquisition of people or teams. "Quikmile's team will now be a part of Airtel X Labs – Airtel's digital innovation factory, which focuses on IoT, Digital Engineering, Artificial Intelligence, and Machine Learning," Airtel said in a statement. Airtel X Labs team comprises of professionals from India and abroad, the statement said, adding that it is building advanced digital capabilities to serve the emerging needs of Digital India and is rapidly scaling up its teams and scope of work. Quikmile, which started operations in 2018, aims to harness the power of digital technology to transform India's highly disorganized logistics sector and bring efficiency, quality, and safety to fleet owners.

It used to be that the big beat the small--today the fast beat the slow. Fast teams keep their talent engaged, ship faster, and beat the competition to market. Microservices let you increase your engineering speed and agility. Using microservices allowed SoundCloud to reduce a standard release cycle from 65 days all the way down to 16. How did they accomplish this?

The next Industrial Revolution is upon us, and scientists, entrepreneurs, and policymakers are warning of an imminent paradigm shift in the future of work. In partnership with the Council on Foreign Relations, VICE talks to industry leaders and laborers to learn how radical developments in automation and artificial intelligence are set to change the world of work as we know it. From the food we eat to the clothes we wear, VICE's Future of Work explores technology's threat to the American worker and the ways we are responding. Check out VICE News for more: http://vicenews.com Follow VICE News here: Facebook: https://www.facebook.com/vicenews

In Japan, one restaurant is exploring artificial intelligence (AI) robotics technology to enable paralyzed employees to remotely pilot robotic waiters. JPMorgan Chase, Microsoft and Ford are hosting virtual career fairs tailored to the needs of neurodiverse candidates. Enterprise Rent-A-Car integrated braille-reader technology into its reservations system for blind employees. Using AI to increase accessibility at work is one of the Gartner Top 10 strategic predictions for 2020 and beyond. The predictions examine how technology is changing the definition of what it means to be human, and IT leaders must be prepared to adapt in a changing environment.

A new generation of smart speakers greatly improves on the device's sound quality. A new generation of smart speakers greatly improves on the device's sound quality. As the audio engineer for the Tiny Desk concert series, of course I obsess over how our concerts are experienced -- so when I watch someone pull up a session on their smartphone, laptop or tablet, with those tiny and tinny speakers, my heart sinks a little. I'm thrilled people love these concerts as much as we love making them, but they sound so much better when played on a decent sound system, or on headphones. Sure, the concerts sound OK on a mono phone speaker, but you'd be amazed by what you're missing once you've heard the audio mix on a device that can actually reproduce the low and high ends of the frequency spectrum, where all the chest-thumping bass and shimmering cymbals live.

A Honeywell smart thermostat is seen above. If you compare the inside of a modern home to one from about 25 years ago, you're going to notice some stark differences -- not just the phone book on the kitchen counter. Rapid advancements in tech over the past two decades have had an impact on everything from the way we communicate to the conveniences of home life. While you expect some household staples to change from generation to generation, things that were part of an average home for decades are now unnecessary. That's because just about any common household gadget can be replaced with a smarter device.