As datasets capturing human choices grow in richness and scale--particularly in online domains--there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom. In this work we introduce the Pairwise Choice Markov Chain (PCMC) model of discrete choice, an inferentially tractable model that does not assume any of the above axioms while still satisfying the foundational axiom of uniform expansion, a considerably weaker assumption than Luce's choice axiom. We show that the PCMC model significantly outperforms both the Multinomial Logit (MNL) model and a mixed MNL (MMNL) model in prediction tasks on both synthetic and empirical datasets known to exhibit violations of Luce's axiom. Our analysis also synthesizes several recent observations connecting the Multinomial Logit model and Markov chains; the PCMC model retains the Multinomial Logit model as a special case.

The idea of artificial intelligence (AI) has always seemed futuristic, but today there is no shortage of companies offering AI solutions. However, does AI offer tangible business solutions, especially in datacenters? Take for example, the case of monitoring energy usage in a datacenter. In a typical datacenter, the manager has to keep track of hundreds of computer room air conditioners (CRACs) with thermostats and manually adjust them. But, if an AI system was in place, it could learn via the algorithm to automatically adjust the cooling depending on different times of the day along with different utilization rates.

Some in-video questions and practice quizzes will help you to review the material, with no effect on the final grade. A mandatory peer-graded assignment is also present, for weeks from 1 to 9, and it will expand on the lectures' topics, letting you reach a deeper understanding. The nine peer-graded assignments will make up for 50% of the grade, while the other half will come from a final exam, after the last lecture. In the tutorial we will use the 3x3 pebble game to understand the essential concepts of Monte Carlo techniques (detailed balance, irreducibility, and a-periodicity), and meet the celebrated Metropolis algorithm. Finally, the homework session will let you understand some useful aspects of Markov-chain Monte Carlo, related to convergence and error estimations.

WIRE)--The Industrial Internet Consortium (IIC), the world's leading organization transforming business and society by accelerating the adoption of the Industrial Internet of Things (IIoT), today announced the Smart Factory Machine Learning for Predictive Maintenance Testbed. The testbed is led by two companies, Plethora IIoT, a company, designing and developing cutting-edge answers for Industry 4.0, and Xilinx, the leading provider of All Programmable technology. This innovative testbed explores machine-learning techniques and evaluates algorithmic approaches for time-critical predictive maintenance. This knowledge leads to actionable insight enabling companies to move away from traditional preventative maintenance to predictive maintenance, which minimizes unplanned downtime and optimizes system operation. This would ultimately help manufacturers increase availability, improve energy efficiency and extend the lifespan of high-volume CNC manufacturing production systems.

About a week ago, Stanford University researchers posted online a study on the latest dystopian AI: They'd made a machine learning algorithm that essentially works as gaydar. After training the algorithm with tens of thousands of photographs from a dating site, the algorithm could, for example, guess if a white man in a photograph was gay with 81 percent accuracy. They wanted to protect gay people. "[Our] findings expose a threat to the privacy and safety of gay men and women," wrote Michal Kosinski and Yilun Wang in the paper. They built the bomb so they could alert the public about its dangers.

Four members of our research team spent the past week at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017) in Copenhagen, Denmark. The conference handbook can be found here and the proceedings can be found here. The program consisted of two days of workshops and tutorials and three days of main conference. Videos of the conference talks and presentations can be found here.The conference was superbly organized, had a great venue, and a social event with fireworks. With 225 long papers, 107 papers, and 9 TACL papers accepted, there was a clear uptick of submissions compared to last year.

Today's blog post was inspired by PyImageSearch reader, Emmanuel. Emmanuel emailed me after last week's tutorial on object detection with deep learning OpenCV and asked: I really enjoyed last week's blog post on object detection with deep learning and OpenCV, thanks for putting it together and for making deep learning with OpenCV so accessible. I want to apply the same technique to real-time video. What is the best way to do this? How can I achieve the most efficiency?

One night in late July 2014, a journalist from the Chinese newspaper Southern Weekly interviewed a 17-year-old Chinese girl named Xiaoice (pronounced Shao-ice). The journalist, Liu Jun, conducted the interview online, through the popular social networking platform Weibo. LJ: So many people make fun of you and insult you, why don't you get mad? Xiaoice: You should ask my father. LJ: What if your father leaves you one day unattended?

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

Millions of children around the world don't have access to basic education, such as reading, writing and arithmetic skills, and the problem is only getting worse. XPRIZE is looking to do something about it. Today, the organization announced the five finalists for the Global Learning XPRIZE; each will receive a $1 million. This particular challenge is designed around teaching basic learning to children around the world. The competition wanted easily scalable software solutions that would be relatively simple to implement.