It was not long ago that Artificial Intelligence (AI) was only in the realm of science fiction. Today, it has become a reality and is only growing more prominent in many different industries every day. This includes the internet as AI in search engine technology has been around for a few years. The algorithms used to rank pages have been affected considerably by AI already and that trend will continue into the foreseeable future. Currently, Google's RankBrain, an AI process used help set search engine rankings, is having a major impact which is only expected to expand.

Yes, but I'm pretty sure there is no direct contact between the Theano guys and this project. I don't know on what level they are collaborating with the other frameworks' teams, but I assume they do, they could be collaborating for updating libgpuarray as well. I do hope we have some progress there as well yes.

Given a task of predicting $Y$ from $X$, a loss function $L$, and a set of probability distributions $\Gamma$ on $(X,Y)$, what is the optimal decision rule minimizing the worst-case expected loss over $\Gamma$? In this paper, we address this question by introducing a generalization of the principle of maximum entropy. Applying this principle to sets of distributions with marginal on $X$ constrained to be the empirical marginal from the data, we develop a general minimax approach for supervised learning problems. While for some loss functions such as squared-error and log loss, the minimax approach rederives well-knwon regression models, for the 0-1 loss it results in a new linear classifier which we call the maximum entropy machine. The maximum entropy machine minimizes the worst-case 0-1 loss over the structured set of distribution, and by our numerical experiments can outperform other well-known linear classifiers such as SVM. We also prove a bound on the generalization worst-case error in the minimax approach.

Announcing our new Foundation for Deep Learning acceleration MIOpen 1.0 which introduces support for Convolution Neural Network acceleration -- built to run on top of the ROCm software stack! Install the ROCm MIOpen implementation (assuming you already have the'rocm' and'rocm-opencl-dev" package installed): For just OpenCL development

One of Unity Labs' missions is identifying and supporting cutting edge research. For 2017, we have identified a slate of research topics we are currently working on in the areas not limited to VR and AR authoring tools, Game AI, and Graphics. It's our vision to advance the next generation of 3D interactive entertainment content authoring. Over the past weeks, we've joined forces with Unity's AI & Machine Learning Group to identify and support graduate researchers specifically working on research challenges in Machine Learning for games. The current perception of Machine Learning in the games industry is surrounding the potential that learning has to offer the gaming world.

It is impossible to accurately predict the jobs of the future, says Mark Scott, the secretary of the NSW Department of Education, but schools will need to prepare the next generations of students for a world that will be dominated by intelligent machines. "Children are now facing a more uncertain future than any child has faced since the Industrial Revolution," Mr Scott said. Mr Scott this week gave a speech to the Trans-Tasman Business Circle where he outlined publicly for the first time his vision for education of the future. It was the springboard for the launch of work being done within the department to prepare students for a fast-changing world. The department is commissioning research and papers from the world's leading experts and educators in the areas of artificial intelligence and education systems of the future.

Seven-year-old Chen Jiahao has a problem sum he can't solve and he can't wait to get home from school to pose the question to his all-knowing maths tutor. His tutor is amazing, the boy says. Just snap a photograph of the question and the tutor will provide every possible approach to solve the problem, step by step – all in a split second. It is, in fact, an app that draws on artificial intelligence (AI) technology to solve challenging maths problems for primary school children. And it's just one of many AI-enabled apps Jiahao uses daily on his mother's phone.

Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. Overall, it is a nice course and provides an introduction to some of the modern topics in deep learning. However, there are instances where the student has to do lots of extra work in order to understand the topics covered in full detail. One of the assignments in the course is to study the Neural Probabilistic Language Model (The related article can be downloaded from here). An example dataset, as well as a code written in Octave (equivalently Matlab) are provided for the assignment.

Tree methods are commonly used in data science to understand patterns within data and to build predictive models. The term Tree Methods covers a variety of techniques with different levels of complexity but my aim is to highlight three I find useful. To set the problem up let's assume we have a census dataset containing age, education, employment status and so on. Given all this information we want to see if we can predict whether a person earns more than $50k per year. How can tree methods help us?

Created by a team of Stanford psychologists and AI experts, Woebot uses brief daily chat conversations, mood tracking, curated videos, and word games to help people manage mental health. Scientists who recently looked at text-chat as a supplement to videoconferencing therapy sessions observed that the texting option actually reduced interpersonal anxiety, allowing patients to more fully disclose and discuss issues shrouded in shame, guilt, and embarrassment. Yesterday, Darcy and a team of co-authors at Stanford published a peer-reviewed study in the Journal of Medical Internet Research, Mental Health that randomized 70 college students and asked them to engage with Woebot or a self-help e-book for two weeks. But using those results to claim it can significantly reduce depression may expose Woebot to legal liabilities that bots in supporting roles have managed to avoid.