Contextual embeddings, such as ELMo and BERT, move beyond global word representations like Word2Vec and achieve ground-breaking performance on a wide range of natural language processing tasks. Contextual embeddings assign each word a representation based on its context, thereby capturing uses of words across varied contexts and encoding knowledge that transfers across languages. In this survey, we review existing contextual embedding models, cross-lingual polyglot pre-training, the application of contextual embeddings in downstream tasks, model compression, and model analyses.

Forget robot teachers, adaptive intelligent tutors and smart essay marking software -- these are not the future of artificial intelligence in education but merely a step along the way. The real power that AI brings to education is connecting our learning intelligently to make us smarter in the way we understand ourselves, the world and how we teach and learn. For the first time we will be able to extend, develop and measure the complexity of human intelligence -- an intellect that is more sophisticated than any AI. This will revolutionise the way we think about human intelligence. We take much of our intelligence for granted.

Artificial Intelligence is coming for education. It's not going to replace college faculty or teaching as we know it. Instead, AI is going to give faculty superpowers, extending their reach and expanding their time. A good teacher is a role model, a sage, able to become what the student needs. Teaching is too personal, too human, to be turned over to AI.

There are many online courses about data science and machine learning that will guide you through a theory and provide you with some code examples and an analysis of very clean data. However, in order to start practising data science, it is better if you challenge a real-life problem. Digging into the data in order to find deeper insights. This blogpost will guide you through the main steps of building a data science project from scratch. It is based on a real-life problem -- what are the main drivers of rental prices in Berlin?

We present a novel framework that can combine multi-domain learning (MDL), data imputation (DI) and multi-task learning (MTL) to improve performance for classification and regression tasks in different domains. The core of our method is an adversarial autoencoder that can: (1) learn to produce domain-invariant embeddings to reduce the difference between domains; (2) learn the data distribution for each domain and correctly perform data imputation on missing data. For MDL, we use the Maximum Mean Discrepancy (MMD) measure to align the domain distributions. For DI, we use an adversarial approach where a generator fill in information for missing data and a discriminator tries to distinguish between real and imputed values. Finally, using the universal feature representation in the embeddings, we train a classifier using MTL that given input from any domain, can predict labels for all domains. We demonstrate the superior performance of our approach compared to other state-of-art methods in three distinct settings, DG-DI in image recognition with unstructured data, MTL-DI in grade estimation with structured data and MDMTL-DI in a selection process using mixed data.

March is Women's History Month in the US, the UK and Australia, a time to honour women's sometimes underrated contributions to society. According to the US National Women's History Museum, Women's History Month started in 1978 as a local "Women's History Week" celebration in California, with organizers selecting the week to correspond with the March 8 International Women's Day. The US Congress in 1987 passed Public Law 100-9 designating March as the Women's History Month. The past few decades have seen a steady increase in the number of women studying and excelling in the STEM fields. But this is not so in computer science -- the number of women studying or pursuing a career in computer science has been decreasing since around 1990.

The bots are coming and having borne witness to at least Six Terminator movies, you are understandably terrified. Not only are marketers ensuring you that these learned machines are going to automate your training so effectively that you may no longer need employees, but they have the gall to hide it all behind acronyms like AI/ML. Never fear, Dr. Allen Partridge, Head of Evangelism for Adobe Digital Learning Products is here to gently walk you through the future tech forest, carefully sorting out the helpful modern miracles from the dystopic fantasies. In this session, you will learn: What can you actually do with AI/ML today? What kind of problems are best suited for AI/ML?

I had been looking for a good book to recommend to my "Introduction to Data Science" classes at UCLA as a text to use once my class completes … sort of the next step after learning the basics. That's why I was looking forward to reviewing the new 3rd edition of the widely acclaimed title "Python Machine Learning" by Sebastian Raschka, Vahid Mirjalili. The book is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a useful resource you'll keep coming back to as you fill up your data science toolbox. I knew I was going to like it the minute I started thumbing through the pages and saw some mathematics.

This tutorial is a free preview from The course titled Practical Deep Learning with Keras and Python, and it is available in The Complete Data Science Course Bundle on my website. In this course you will learn how to apply machine learning techniques to real world problems, including how to build a complete pipeline using Keras and Python. If you enjoy this video and would like to continue learning with us, feel free to check out this 6 course bundle which will take you from beginner to advanced in data science, machine learning and neural networks. I will include timestamps and links to additional resources in the description of this video. Without further ado, let's get started!

Everyone is talking about artificial intelligence (AI), but few understand how it can be used to improve our everyday access to justice as citizens. In a fun and informative talk, Phillip Djwa will examine the advantages--and limitations--of AI and its application to legal issues people face every day. How far can technology go to help us? Using a chatbot built as a case study, Djwa looks at the state of the industry, issues of built-in bias of computer systems and the general fear we have of being taken over by AI machines like SkyNet. Drew Jackson, head of the People's Law School's chatbot project, will join Djwa.