Data is required to fuel machine learning and artificial intelligence and provide the insights to increase productivity and customer relations.

For CenturyLink, customer surveys play an important role in understanding the customer experience. But, in the past, without the ability to analyze the unstructured data in these surveys, customer service teams couldn't drill down to identify the nature and characteristics of issues. "Using unstructured data is really important for us because that's where the real detail -- the meat -- comes in," said Beth Ard, vice president of customer experience at the Louisiana-based network services provider. "When you do scoring, for example, you don't have enough actionable insight to make the changes that you need without it." Most business analytics processes require clean and well-structured data, but enterprises are increasingly managing unstructured data formats -- emails, chat transcripts, audio and video, and social media posts.

Machine learning today not only recommends the things you can buy, or content you can watch, but it is doing wonders in other domains as well. This time it is on a mission to find out something untouched. There are different elements in music that trigger emotion in humans. And machine learning is trying to find out just that -- how music affects brain activity, physiological response, and human-reported behaviour. New research by scholars from the University of Southern California is trying to figure out the elements in a song that triggers different emotions in a human.

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.