After learning topic vectors from an auxiliary text corpus via NMF, the decoder is trained so that it is more likely to sample response words from the most correlated topic vectors. One of the main advantages in our architecture is that the user can easily switch the NMF-learned topic vectors so that the chatbot obtains desired topic-awareness. We demonstrate our model by training on a single conversational data set which is then augmented with topic matrices learned from different auxiliary data sets. We show that our topic-aware chatbot not only outperforms the non-topic counterpart, but also that each topic-aware model qualitatively and contextually gives the most relevant answer depending on the topic of question. Another area where deep learning algorithms have been successfully applied is sequence learning, which aims at understanding the structure of sequential data such as language, musical notes, and videos. One example of an application of deep learning in language modeling is conversational chatbots . A chatbot is a program that conducts a conversation with a user by simulating one side of it. Chatbots receive inputs from a user one message, or question, at a time, and then form a response that is sent back to the user. One of the most widely used machine learning techniques for sequence learning is Recurrent Neural Networks (RNN).

What do you think of when you think of schools and colleges? A classroom full of students furiously scribbling down notes while a teacher is droning on about a topic which is "very important for your midterms". Exams are a very important and indispensable part of education. They are important milestones in a student's educational journey, and students are understandably stressed about them. In an academic year, students have to give as many as 12 exams per semester, which means up to 24 exams in one year!

Declaring as much is a favorite line of his whenever someone asks what his two-year-old company, Hypergiant, does. What he means is that he doesn't produce anything as uniform and universal as utensils. Were he a purveyor of tableware, he wouldn't have to spend so much of his time customizing products to individual clients or explaining what can be done with them. Everybody knows what spoons are for. Contrast that with the broadest definition of what Hypergiant does in fact sell--artificial intelligence-enabled software and hardware--and you'll appreciate Lamm's problem. Even many people lacking in technological savvy have heard of AI as a force with the potential to shape much of humanity's future--for better or worse.

I am sure if you are into artificial intelligence and machine learning, at some point you may have thought about what dangers can AI bring into our lives. Ever asked the question of what can possibly be the real threat of artificial intelligence. Now, whether we like it or not, Artificial Intelligence is one of the biggest technological revolutions. A revolution that will lead humanity to the future. Tech giants like Amazon, Facebook, Google are spending millions, if not billions of dollars in AI researches.

The popularity of AI and ML have wide-reaching effects on your enterprise. Here are three important trends driven by AI to look out for next year. As the need for additional AI applications grows, businesses will need to invest in technologies that help them accelerate the data science process. However, implementing and optimizing machine learning models is only part of the data science challenge. In fact, the vast majority of the work that data scientists must perform is often associated with the tasks that preceded the selection and optimization of ML models such as feature engineering -- the heart of data science.

Information systems (IS) are widely used in organisations to improve business performance. The steady progression in improving technologies like artificial intelligence (AI) and the need of securing future success of organisations lead to new requirements for IS. This research in progress firstly introduces the term AI-based services (AIBS) describing AI as a component enriching IS aiming at collaborating with employees and assisting in the execution of work-related tasks. The study derives requirements from ten expert interviews to successful design AIBS following Design Science Research (DSR). For a successful deployment of AIBS in organisations the D&M IS Success Model will be considered to validated requirements within three major dimensions of quality: Information Quality, System Quality, and Service Quality. Amongst others, preliminary findings propose that AIBS must be preferably authentic. Further discussion and research on AIBS is forced, thus, providing first insights on the deployment of AIBS in organisations.

This Q&A with Jake Frazier is based on the first of a series of interviews I'm conducting with thought leaders who take a unified governance approach to increasing the value of information to their businesses while driving down costs. Along with his role as senior managing director at FTI Consulting, Jake is a faculty member of CGOC, a founding member of the Electronic Discovery Reference Model (EDRM), a member of the Sedona Conference and an Advisory Cabinet Member of the Masters Conference. He has authored many articles and white papers on information governance issues and regularly addresses industry groups on the topic. For this article, I asked Jake about the new and complex challenges around the adoption of machine learning (ML) technologies in enterprises. ML offers business users an unprecedented opportunity to take advantage of the massive amount of data they are collecting.

Artificial intelligence is everywhere but it is considered in a wholly ahistorical way. To understand the impact AI will have on our lives, it is vital to appreciate the context in which the field was established. After all, statistics and state control have evolved hand in hand for hundreds of years. Its origins have been traced not only to analytic philosophy, pure mathematics and Alan Turing, but perhaps surprisingly, to the history of public administration. In "The Government Machine: A Revolutionary History of the Computer" from 2003, Jon Agar of University College London charts the development of the British civil service as it ballooned from 16,000 employees in 1797 to 460,000 by 1999.

Instead of cutting marketing budgets, Diageo decides to invest in marketing but to remove inefficient expenditures, and thus improve profits by shifting money investments. Two years ago, the company launched the platform Catalyst, whose main goal is to provide instant data to marketeers and help them create investment-worthy strategies. According to Diageo's global marketing effectiveness director, Adam Ben-Yousef, they have "achieved a more profound shift at the highest level in the belief in marketing investment. Marketing is no longer the first spend to be cut when a market wants to deliver its annual plan." It does that by solving issues and helping companies reach customer satisfaction.

As we are propelled into an even-more digital age, companies and employees are both asking: To what degree will AI replace human intelligence and make jobs obsolete? In the inevitable future of AI, what is the outlook for human employees? Neil Jensen, Vice President, Product Strategy, Workday, identifies trends in areas like the future of work. I asked Jensen to share with us the trends he's seeing in the future-of-work space. John Winsor: What is the future of work as you see it?