Working alongside robots will be part of a new university course aimed at students entering careers as carers, therapists and social workers. The new university programme is designed to help people get comfortable working with'social robots' that will be their colleagues in the future, researchers say. A recent survey found that almost 40 per cent of people are afraid that robots will steal their jobs. Earlier this year NHS officials announced that robots will carry out dementia care within 20 years. Working alongside robots will be part of a new university courses aimed at students entering careers as carers. Scientists from Sligo Institute of Technology in Ireland are testing a'Paro' robot seal that reacts to petting and conversation.

No shortage of articles and reports describe the many problems we face daily as Americans: immigration, natural disasters, wars, racism, sexism, and shootings. It's easy to get lost in the sheer number of crises, paralyzing to even contemplate what do to next. Worse, we can fall into the trap of negativity, overwhelmed by all that must be done to fix our problems. "It's a huge problem in the U.S.," the founder and CEO of Learning Ovations Jay Connor told my coauthor Neil Sahota and I during an interview for our upcoming book, Uber Yourself Before You Get Kodaked: A Modern Primer on A.I. for the Modern Business. "Less than 50 percent of our children are reading at grade level, and if you're dealing with high-need or high-poverty populations, in some locations, like our schools in New York, it's below 20 percent."

Facebook may be willing to foot the bill. On Tuesday, Facebook and Udacity announced the PyTorch Scholarship Challenge, offering students the opportunity to learn how to build, train, and deploy deep learning models. PyTorch is an open source deep learning framework that is growing in popularity among AI researchers due to its ease of use, clean Pythonic API, and flexibility, Stuart Frye, Udacity's vice president of partnerships, wrote in a Tuesday blog post. With PyTorch 1.0, now available in preview release, developers can more easily move from exploration to product development with a single unified framework, Frye wrote. The scholarship program will offer students the chance to learn in-demand deep learning skills with PyTorch, as well as earn a full scholarship to Udacity's Deep Learning Nanodegree program, according to the post.

Mumbai: Sebastian Thrun is a man of many parts. The president and co-founder of e-learning company Udacity, is not only an innovator and computer scientist but also CEO of Kitty Hawk Corporation that makes flying cars and chairman of Cresta.ai--a Germany-born Thrun was earlier a Google VP and Fellow. At Google, he founded Google X and Google's self-driving car team. He is currently also an Adjunct Professor at Stanford University and at Georgia Tech.

Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way. In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.

Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it still lacks of quantitative ways of measuring talent competencies as well as the job's talent requirements. To this end, in this paper, we propose a novel end-to-end data-driven model based on Convolutional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job. To be specific, PJFNN is a bipartite neural network which can effectively learn the joint representation of Person-Job fitness from historical job applications. In particular, due to the design of a hierarchical representation structure, PJFNN can not only estimate whether a candidate fits a job, but also identify which specific requirement items in the job posting are satisfied by the candidate by measuring the distances between corresponding latent representations. Finally, the extensive experiments on a large-scale real-world dataset clearly validate the performance of PJFNN in terms of Person-Job Fit prediction. Also, we provide effective data visualization to show some job and talent benchmark insights obtained by PJFNN.

We present Spider, a large-scale, complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 unique complex SQL queries on 200 databases with multiple tables, covering 138 different domains. We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets. In this way, the task requires the model to generalize well to both new SQL queries and new database schemas. Spider is distinct from most of the previous semantic parsing tasks because they all use a single database and the exact same programs in the train set and the test set. We experiment with various state-of-the-art models and the best model achieves only 14.3% exact matching accuracy on a database split setting. This shows that Spider presents a strong challenge for future research. Our dataset and task are publicly available at https://yale-lily.github.io/spider

It's a great time to get started doing deep learning with Kaggle Kernels! Recently I had the chance to give my first conference talk, at PyOhio in Columbus. I spoke about getting into deep learning, and I used Kaggle Kernels to demo some material from the first lesson of the fast.ai The next day I came across a Medium article called "Learn deep learning with GPU enabled kaggle kernels and fastai MOOC", and I was excited to see more people recognizing the capabilities of this platform. So I thought, if we're going to make it easy for people to get started with deep learning on Kaggle Kernels, why not make the whole fast.ai

How long does it take you to install your complete GPU-enabled deep learning environment including RStudio or jupyter and all your packages? And do you have to do that on multiple systems? In this blog post series I'm going to show you how and why I manage my data science environment with GPU enabled docker containers. How are you managing your data science stack? I was never really satisfied in how I did it.

Here is a list of top 10 Data Science writers on Quora and their selected answers. Next, play around some more and check out the tutorials for Titanic: Machine Learning from Disaster with a slightly more complicated binary classification task (with categorical variables, missing values, etc.), Software Engineer at Arista Networks, Foundation Member and Game Dev at GNOME. You may be wrong but consider it to be a healthy discussion, the interviewer will help you along the way. Its almost never necessary to get the correct answer, most interviewers care about your basics and how you think. There is a lot of value in getting really deep technical expertise.