Task-oriented dialog systems have been applied in various tasks, such as automated personal assistants, customer service providers and tutors. These systems work well when users have clear and explicit intentions that are well-aligned to the systems' capabilities. However, they fail if users intentions are not explicit. To address this shortcoming, we propose a framework to interleave non-task content (i.e. everyday social conversation) into task conversations. When the task content fails, the system can still keep the user engaged with the non-task content. We trained a policy using reinforcement learning algorithms to promote long-turn conversation coherence and consistency, so that the system can have smooth transitions between task and non-task content. To test the effectiveness of the proposed framework, we developed a movie promotion dialog system. Experiments with human users indicate that a system that interleaves social and task content achieves a better task success rate and is also rated as more engaging compared to a pure task-oriented system.

I have a multi-class machine learning problem for which I will try different methods on such as logistic regression, decision trees, multilayer perceptron etc. The observations in the data set have an attribute which is an index from 1-5 which defines how important it is that a certain observation gets correctly classified (index 1 very important, 5 not important at all). Question 1: How should I emphasize to the models that the lower index observations have greater importance? I am thinking of duplicating these observations so the models fit the lower index observations more well, what other approaches are possible? Question 2: What performance evaluation criterias can I use to find the models that predict these low index observations well?

A mother was on the phone, crying. She could not contain her gratitude: her son had just been accepted to university thanks, in part, to a new startup called SchoolApply. "We really want to open up the world for students," says Daniel Bjarne, CEO and Co-Founder of SchoolApply, which uses artificial intelligence (AI) and search functionality to help students from anywhere in the world connect with educational programs abroad that best match their needs and potential. Demand for such a service is growing fast. By 2025, some eight million students are expected to have to travel to other countries to study.

Federico Antoni is managing partner at ALLVP, an early-stage VC based in Mexico. He is a lecturer in management at the Stanford Graduate School of Business. "Over the last couple of years, a billion new people have joined the super-connected world. Billions more around the developing world, now, walk with a high-speed computer in their pockets. And yet, they don't have a bank account, a formal education or access to most of the services we take for granted in the U.S. Imagine the possibilities… imagine how you can change the lives of billions of people."

The village is poor, even by the standards of rural Kenya. To get there, you follow a power line along a series of unmarked roads. Eventually, that power line connects to the school at the center of town, the sole building with electricity. Homesteads fan out into the hilly bramble, connected by rugged paths. There is just one working water tap, requiring many local women to gather water from a pit in jerrycans.

With the recent machine learning boom, more and more algorithms have become available that perform exceptionally well on a number of tasks. But knowing beforehand which algorithm will perform best on your specific problem is often not possible. If you had infinite time at your disposal, you could just go through all of them and try them out. The following post shows you a better way to do this, step by step, by relying on known techniques from model selection and hyper-parameter tuning. Before we get in too deep, we want to make sure we brushed up on the basics. In specific, we should know that there are three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Udacity students are constantly impressing us with their skill, ingenuity, and their knowledge of the most obscure features in Slack. Here are 6 blog posts that will astound you, and 1 think-piece that will blow your mind. Sujay's managed his data in a few clever ways for the traffic sign classifier project. First, he converted all of his images to grayscale. Then he skewed and augmented them.

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the underlying constrained optimization problem is formulated as a machine learning task involving both training and operational modes, with the goal of learning the sought multipliers in a fast and efficient manner. To this end, an order-optimal offline learning approach is developed first for batch training, and it is then generalized to the online setting with a procedure termed learn-and-adapt. The novel resource allocation protocol permeates benefits of stochastic approximation and statistical learning to obtain low-complexity online updates with learning errors close to the statistical accuracy limits, while still preserving adaptation performance, which in the stochastic network optimization context guarantees queue stability. Analysis and simulated tests demonstrate that the proposed data-driven approach improves the delay and convergence performance of existing resource allocation schemes.

Researchers at Microsoft and Cambridge University have built a highly sophisticated computer called DeepCoder that can now allow machines to write their own programs. This is aimed to make job easier for users who don't know programming languages well enough to use them efficiently, or even help people having no experience in writing simple coding programs. "All of a sudden people could be so much more productive," says Armando Solar-Lezama at the Massachusetts Institute of Technology, who was not involved in the work. "They could build systems that it [would be]impossible to build before." The paper, DeepCoder: Learning to Write Programs, is a basic system with some limitations according to the researchers.

China has now overtaken Japan for having more robots than anywhere else in the world. The Chinese government is concerned about an ageing population and the rising cost of human labour making Chinese products less competitive. It is giving over $100 Billion in subsidies for companies to replace more human workers with robots. It's hoped that the workers that will no longer work in the factories will move to the growing service sector, in part to help look after the ageing population. Countries In the west have already lost large numbers of manufacturing jobs to china but we shouldn't get too smug about our white-collar jobs in high tech, administration, clerical and production being safe from A.I. systems in the future.