These four recommendations can help prepare you -- or the novice analyst on your team -- for a career in this burgeoning field. When Aurora Peddycord-Liu started as an analytical education intern at SAS in the summer of 2017, she came with a solid educational background from Worcester Polytechnic Institute and NC State's computer science Ph.D. program. These programs prepared her well for her current position at SAS, where she uses data to derive actionable insights on the design and use of SAS e-learning courses, but she's had to adapt her skill set to face the challenges of a real-world analytics position. To learn how newbie analysts can prepare for their work in this hot new age of machine learning, I spoke with Peddycord-Liu and senior executive, Dan Olley, global CTO at Elsevier. Recommendation #1: Don't be overwhelmed -- just get started Don't be intimidated by the powerful tools at your disposal; find a point to start and dive in.

We live in exponential times, and merely having a digital strategy focused on continuous innovation is no longer enough to thrive in a constantly changing world. To transform an organisation and contribute to building a secure and rewarding networked society, collaboration among employees, customers, business units and even things is increasingly becoming key. Especially with the availability of new technologies such as artificial intelligence, organisations now, more than ever before, need to focus on bringing together the different stakeholders to co-create the future. Big data empowers customers and employees, the Internet of Things will create vast amounts of data and connects all devices, while artificial intelligence creates new human-machine interactions. In today's world, every organisation is a data organisation, and AI is required to make sense of it all.

Chowbotics is packing up Sally the salad making robot and sending it off to college. Well, many colleges actually, as the food robotics startup is set to announce next week a bigger push into the higher education market. Chowbotics told us that this school year, students at multiple colleges and universities in the U.S. will be able to buy salads and breakfast bowls from Sally the robot. Those schools include: Case Western Reserve University in Cleveland, OH; College of the Holy Cross in Worcester, MA; the University of Guelph in Ontario, Canada; Elmira College in Elmira, NY; the University of Memphis in Memphis, TN; and Wichita State University in Wichita, KS. These schools join Marshall University in Huntington, WV, which installed Sally in 2018.

Employers can now practice laying off an ultra-realistic, AI-powered virtual employee in order to develop their soft skills before they have to fire someone in real life. Capable of realistically engaging trainees in conversation and displaying appropriate emotions, poor virtual employee Barry Thompson gets the sack over and over again. However, his reaction -- which can range from calm acceptance to angry and defensive shouting -- varies depending on the user's handing of the scenario. The firm who created Barry have also developed a number of other virtual training scenarios, from negotiation and making sales to giving feedback to subordinates. Barry is a virtual employee created by Talespin Studios.

Machine Learning Yearning is about structuring the development of machine learning projects. The book contains practical insights that are difficult to find somewhere else, in a format that is easy to share with teammates and collaborators. Most technical AI courses will explain to you how the different ML algorithms work under the hood, but this book teaches you how to actually use them. If you aspire to be a technical leader in AI, this book will help you on your way. Historically, the only way to learn how to make strategic decisions about AI projects was to participate in a graduate program or to gain experience working at a company.

In this paper, we propose a new method to build fair Neural-Network classifiers by using a constraint based on the Wasserstein distance. More specifically, we detail how to efficiently compute the gradients of Wasserstein-2 regularizers for Neural-Networks. The proposed strategy is then used to train Neural-Networks decision rules which favor fair predictions. Our method fully takes into account two specificities of Neural-Networks training: (1) The network parameters are indirectly learned based on automatic differentiation and on the loss gradients, and (2) batch training is the gold standard to approximate the parameter gradients, as it requires a reasonable amount of computations and it can efficiently explore the parameters space. Results are shown on synthetic data, as well as on the UCI Adult Income Dataset. Our method is shown to perform well compared with 'ZafarICWWW17' and linear-regression with Wasserstein-1 regularization, as in 'JiangUAI19', in particular when non-linear decision rules are required for accurate predictions.

We are increasingly turning to machine learning systems to support human decisions. While decision makers may be subject to many forms of prejudice and bias, the promise and hope is that machines would be able to make more equitable decisions. Unfortunately, whether because they are fitted on already biased data or otherwise, there are concerns that some of these data driven recommendation systems treat members of different classes differently, perpetrating biases, providing different degrees of utilities, and inducing disparities. The examples that have emerged are quite varied: 1. Criminal justice: courts in the United States use COMP AS--a commercially available algorithm to assess a criminal defendant's likelihood of becoming a recidivist--to help them decide who should receive parole, based on records collected through the criminal justice system. In 2016 ProPublica analyzed COMP AS and "found that black defendants were far more likely than white defendants to be incorrectly judged to be at a higher risk of recidivism, while white defendants were more likely than black defendants to be incorrectly flagged as low risk" [1].

This paper focuses on "tracing player knowledge" in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the paper is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called "Parallel" to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error.

Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the largest challenge dataset for QC, containing 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains. We then show that a BERT-based model trained on this dataset achieves a large (+0.12 MAP) gain compared with previous methods, while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. Finally, we show that using this model's predictions of question topic significantly improves the accuracy of a question answering system by +1.7% P@1, with substantial future gains possible as QC performance improves.

Alexa can read you the latest headlines, give you the weather forecast, and help you find a recipe to make for dinner, but did you know that your Echo device can also assist your student with homework? If you have an Amazon Echo smart speaker at home, like our favorite Echo device, the Echo (2nd Generation), your student has access to a wealth of brain-sharpening, knowledge-enhancing activities to help tackle homework assignments. While kids probably don't need more time interacting with electronics, Alexa offers a variety of skills that can help students with their homework when parents aren't available. To enable a skill on your Echo device say, "Alexa, enable [exact name of skill]." Or, open the Alexa app, tap the menu button, and select "Skills."