Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few studies. In this work, we focus on spatial reasoning and explore the spatial understanding of neural models. First, we describe the following two spatial reasoning IQ tests: rotation and shape composition. Using well-defined rules, we constructed datasets that consist of various complexity levels. We designed a variety of experiments in terms of generalization, and evaluated six different baseline models on the newly generated datasets. We provide an analysis of the results and factors that affect the generalization abilities of models. Also, we analyze how neural models solve spatial reasoning tests with visual aids. Our findings would provide valuable insights into understanding a machine and the difference between a machine and human.

Chatbots are being used almost everywhere today from social messaging platforms to integration into websites for booking tickets, finding nearby restaurants, generating leads, buying and selling products. Some chatbots, like Ruuh by Microsoft have been able to deliver human-like conversations using artificial intelligence and deep learning. Chatbots are being used almost everywhere today from social messaging platforms to integration into websites for booking tickets, finding nearby restaurants, generating leads, buying and selling products. Some chatbots, like Ruuh by Microsoft have been able to deliver human-like conversations using artificial intelligence and deep learning. Do you remember Natasha from Hike?

Following the great minds of machine learning can help you discover new things and deepen your knowledge. It's fascinating to learn from the best scientists. Among them, you will find influencers, teachers, business leaders, and even many more. Undeniably their expertise can help to change the world and make it a better place. On this list, you will find not only influencers but also renowned personalities from the world of Data Science.

We are hosting the first DSR demo day of 2020 - online! Nine of our graduates will be presenting their data science & machine learning projects.

Find a hospital taking in coronavirus cases, and you'll most likely find departments often in need of more staff and without enough testing kits. Now one Canadian AI startup is hoping to develop tools that will automatically detect COVID-19 infections from X-rays, and help guide medical professionals on how seriously the infection has taken hold. DarwinAI, which spun out of work at the University of Waterloo, normally works on AI explainability. The company makes a tool that can show why deep-learning modules make the decisions they do, enabling users to correct the inputs that lead to wrong decisions, and fix the architecture or retrain the system to prevent the same mistakes in future. The idea is that, by getting an insight into why AI does what it does, companies can speed up the development of their AI products.

Buying books, electronics or food online is quick and convenient. However even the more e-commerce savvy consumers may hesitate to buy clothes on the Internet. The reason is simple -- there's no virtual fitting room. But don't worry, AI is working on that. Achieving accurate 3D human digitalization requires accurate unclothed human body modelling along with plenty of labelled 3D garment data.

Deep learning continues to be the hottest thing in data science. Deep learning frameworks are changing rapidly. Just five years ago, none of the leaders other than Theano were even around. I wanted to find evidence for which frameworks merit attention, so I developed this power ranking. I used 11 data sources across 7 distinct categories to gauge framework usage, interest, and popularity.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. In 1956, a group of scientists led by John McCarthy, a young assistant-professor of mathematics, gathered at the Dartmouth College, NH, for an ambitious six-week project: Creating computers that could "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." The project kickstarted the field that has become known as artificial intelligence (AI). At the time, the scientists thought that a "2-month, 10-man study of artificial intelligence" would solve the biggest part of the AI equation. "We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer," the first AI proposal read.

With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful. Several testing methodologies have been developed to probe models' syntactic representations. One popular method for determining a model's ability to induce syntactic structure trains a model on strings generated according to a template then tests the model's ability to distinguish such strings from superficially similar ones with different syntax. We illustrate a fundamental problem with this approach by reproducing positive results from a recent paper with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs.

Learning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it difficult to provide supervision during training. In this paper, we introduce a novel approach for learning to navigate from image inputs without external supervision or reward. Our approach consists of three stages: learning a good representation of first-person views, then learning to explore using memory, and finally learning to navigate by setting its own goals. The model is trained with intrinsic rewards only so that it can be applied to any environment with image observations. We show the benefits of our approach by training an agent to navigate challenging photo-realistic environments from the Gibson dataset with RGB inputs only.