One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream modules are affected by earlier modules, and the performance of the entire system is not robust to the accumulated errors. This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues. Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks. The reinforcement learning based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket booking domain show that our end-to-end system not only outperforms modularized dialogue system baselines for both objective and subjective evaluation, but also is robust to noises as demonstrated by several systematic experiments with different error granularity and rates specific to the language understanding module.

This article was posted by SmileJet on Dev Battles. She paints and writes poetry. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. Now, tech companies large and small are racing to make this a reality. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing.

Artificial intelligence (AI) is a theme that's been frequently dramatized by the entertainment industry. We see it in the form of humanoid figures with superhuman intelligence. These AI marvels work 24/7, nearly at the speed of light, yet they never get tired or complain. What's more, they can remember every detail and learn from every experience, so they constantly get smarter. This understanding of AI won't actually help businesses leverage its full potential but it's certainly effective in creating the fear that AI is about to replace us in the job market.

We have reported on the use of artificial intelligence (AI) in music before, but things are about to change. And the change appears to be positive. To be honest a lot of the AI generated music that we've heard so far has been far from good or innovative. It has either been computer generated attempts to create classical dance or pop tunes, or far out space sounding things that tends to be more experimental than listenable. Now a new generation of bands and acts are active in the AI space, but with the difference that they have a more deliberate strategy in how to incorporate AI into their music creation process. So this new generation of musicians are creatively engaging with algorithmic processes to make some of the most futuristic and genre-bending music coming out right now.

Welcome to the first entry in our "Innovations for Educators" series, spotlighting interesting technologies that have the potential to amplify and complement the work done by educators. Artificial intelligence (AI) is all around us. From self-driving cars to voice and facial recognition technologies to computers that can compose music, AI stands to offer unprecedented convenience in our personal lives. At the same time, AI is also transforming the world of work. From helping lawyers scan hundreds of documents and predicting which are the most useful to a case, to helping doctors analyze massive amounts of data to develop treatment plans for patients, AI can perform in seconds tasks that would normally take hours of human effort.

If an algorithm is set to learning some policy about how to interact with the world to achieve a specific task, what is there to be learned from the algorithm's solution? For example, have new physical or biological principles governing the robustness of - or tradeoffs in locomotion strategies been learned from analysis of the learned movement patterns of the Deepmind walkers? I'm a biology PhD student and I've been wondering how my field could take advantage of advances in machine learning to move biology forward. It's one thing to be able to make predictions, but it seems to me that reinforcement learning approaches offer the potential for machines to act as scientists themselves.

Newswire) 8x8, Inc. (NYSE:EGHT), a leading provider of global cloud communications and customer engagement solutions, today announced key appointments to accelerate the company's Artificial Intelligence (AI) and Machine Learning capabilities, and expand its human resources organization globally. The team will lead the company's efforts to leverage big data, analytics and machine learning to allow companies to gain deep, actionable insights and improve customer experiences. Dr. Ali Arsanjani was formerly the Founder and Chief Technology Officer of Analytics and Machine Learning at Deep Context, a deep-learning startup. Prior to Deep Context, he was a Distinguished Engineer and Chief Technology Officer for Analytics and Machine Learning at IBM. Ali was responsible for worldwide enablement of highly customized solutions that combined real-time, unstructured content and structured analytics and machine learning to solve customer's complex problems while at IBM. He is a recognized authority in the AI industry and has chaired and participated in numerous machine learning research bodies, including The Open Group, and is responsible for co-leading the SOA Reference Architecture, SOA Maturity Model and Cloud Computing Architecture standards.

Earlier this month, when we set about to demystify some of the worst business jargon at the World Economic Forum in Davos, we could not have imagined it would hit so many of our readers' raw nerves. Hundreds felt compelled to get in touch with their own submissions, some unprintable, but the best of which we have "outlined" below. There was, of course, plenty of criticism of our selections, with many objecting to the singling out of "benchmarking" - a term that has been in use in many disciplines for several decades - and a passionate debate about the precise meaning of "negative feedback loops", more of which later. But perhaps the wittiest critique came from Charles Crowe, who maintains that "all these explanations lack granularity and do not contain metrics sufficient to let us know if we need a new paradigm". We have taken that on board, Charles.

As artificial intelligence (AI) technology goes mainstream, its potential to reshape sectors remains years away, says Moody's Investors Service. AI is a broad concept covering many different applications and models. After decades of slow progress, AI technologies have advanced meaningfully in recent years on increases in computing power, large and growing datasets and improvements in underlying algorithms. From large multinationals to small startups, companies are investing heavily, and real-world applications are becoming mainstream. While there are many applications for AI, high tech and media, automotives, financial services and manufacturing are particularly intense users at this stage.

A startup using artificial intelligence (AI) to tackle the proliferation of fake news and extremist content online will expand after closing a $1m (£720,000) seed funding round that attracted major US backers. London-based Factmata's funding round was led by tech entrepreneur Mark Cuban, but also attracted high-profile investors such as Twitter co-founder Biz Stone, internet entrepreneur Sunil Paul and, more recently, Craigslist founder Craig Newmark. The company will put the cash towards research and development, product development and expanding its team beyond machine-learning specialists. The round was initially closed last August but soon reopened to close formally in the last couple of weeks. Read more: Twitter fails to give MPs "straight answers" on Russian influence on Brexit "Every day there's an article or there's someone talking about fake news at the World Economic Forum, so I wanted to keep the round open so people could come in quite late in the process," founder and chief executive Dhruv Ghulati told City A.M. Factmata is developing both business and consumer-facing products, such as an anti-fake news platform for journalists, researchers and the public to use, as well as collaborating globally with advertisers and businesses that can use its algorithms to sift through and identify fake news, spoof sites and hate content. Ghulati, who was recently named one of Forbes' 30 Under 30 On the B2B side, this would help advertisers find junk content on potential sites they would be advertising on.