Abstract-- We propose two multimodal deep learning architectures that allow for cross-modal dataflow (XFlow) between the feature extractors, thereby extracting more interpretable features and obtaining a better representation than through unimodal learning, for the same amount of training data. These models can usefully exploit correlations between audio and visual data, which have a different dimensionality and are therefore nontrivially exchangeable. Our work improves on existing multimodal deep learning metholodogies in two essential ways: (1) it presents a novel method for performing cross-modality (before features are learned from individual modalities) and (2) extends the previously proposed cross-connections [1], which only transfer information between streams that process compatible data. Both cross-modal architectures outperformed their baselines (by up to 7.5%) when evaluated on the AVletters dataset. I. INTRODUCTION An interesting extension of unimodal learning consists of deep models which "fuse" several modalities (for example, sound, image or text) and thereby learn a shared representation, outperforming previous architectures on discriminative tasks.

– Any one working within industries like the mobility, fintech, mobile money, payments, banking or InsureTech with little knowledge of data science is actually sitting on gold mine to explore and show what Data Science / AI can do for that company. Today every company on this planet collect vast quantities of data on a daily basis or even per second. For example credit card issuers with every credit card swipe and completed transaction capture critical customer information, In case of mobile payments/money the same thing happen or even in banks same scenarios. However, the raw data alone does not generate the insights needed to drive business decisions or simply not good enough at all. It's the proper analysis of this data that unlocks its true value.

In the last few years, deep neural networks have dominated pattern recognition. They blew the previous state of the art out of the water for many computer vision tasks. Voice recognition is also moving that way. But despite the results, we have to wonder… why do they work so well? In doing so, I hope to make accessible one promising answer as to why deep neural networks work. I think it's a very elegant perspective. A neural network with a hidden layer has universality: given enough hidden units, it can approximate any function. This is a frequently quoted – and even more frequently, misunderstood and applied – theorem.

Since the days of Da Vinci's "Ornithoper", mankind's greatest minds have sought inspiration from the natural world for their technological creations. It's no different in the modern world, where bleeding-edge advancements in machine learning and artificial intelligence have begun taking their design cues from the most advanced computational organ in the natural word: the human brain. Deep learning neural networks -- the likes of which power AlphaGo as well as the current generation of image recognition and language translation systems -- are the best machine learning systems we've developed to date. They're capable of incredible feats but still face significant technological hurdles, like the fact that in order to be trained on a specific skill they require upfront access to massive data sets. What's more if you want to retrain that neural network to perform a new skill, you've essentially got to wipe its memory and start over from scratch -- a process known as "catastrophic forgetting".

It can be difficult to understand how to prepare your sequence data for input to an LSTM model. Often there is confusion around how to define the input layer for the LSTM model. There is also confusion about how to convert your sequence data that may be a 1D or 2D matrix of numbers to the required 3D format of the LSTM input layer. In this tutorial, you will discover how to define the input layer to LSTM models and how to reshape your loaded input data for LSTM models. How to Reshape Input for Long Short-Term Memory Networks in Keras Photo by Global Landscapes Forum, some rights reserved.

A world renowned pioneer in social robotics, Cynthia Breazeal splits her time as an Associate Professor at MIT, where she received her PhD and founded the Personal Robots Group, and Founder and Chief Scientist of Jibo, a personal robotics company with over $85 million in funding. While Breazeal's work has won numerous academic awards, industry accolades, and media attention, she had to fight early skepticism in the 1990s from other experts in robotics and AI. At the time, robots were seen as physical and industrial tools, not social or emotional companions. Her first social robot, Kismet, was unfairly called out in popular press as "useless". Breazeal bucked the trend with a very different vision: "I wanted to create robots with social and emotional intelligence that could work in collaborative partnership with people. In 2-5 years, I see social robots helping families with things that really matter, like education, health, eldercare, entertainment, and companionship." She hopes her work and influence will inspire others to create robots "not only with smarts, but with heart, too."

The secret behind deep learning is not really a secret. What a neural network essentially does, is optimising a function. In this episode I illustrate a number of...

Since the days of Da Vinci's "Ornithoper", mankind's greatest minds have sought inspiration from the natural world for their technological creations. It's no different in the modern world, where bleeding-edge advancements in machine learning and artificial intelligence have begun taking their design cues from the most advanced computational organ in the natural word: the human brain. Deep learning neural networks -- the likes of which power AlphaGo as well as the current generation of image recognition and language translation systems -- are the best machine learning systems we've developed to date. They're capable of incredible feats but still face significant technological hurdles, like the fact that in order to be trained on a specific skill they require upfront access to massive data sets. What's more if you want to retrain that neural network to perform a new skill, you've essentially got to wipe its memory and start over from scratch -- a process known as "catastrophic forgetting". Compare that to the human brain, which learns incrementally rather than bursting forth fully-formed from a sea of data points.

Elon Musk's artificial intelligence research company, OpenAI, is developing a self-learning bot for one of the most complex esports titles: 'Dota 2.' It has already become the ultimate challenge for players, but for top esports pros, it is also a major opportunity. Snoop Dogg and Martha Stewart reenact that famous'Ghost' scene and things get steamy

Robots have written phoney reviews on Yelp that are so convincing they're almost impossible to distinguish from the real thing. Scientists created this articulate artificial intelligence system to show how damaging neural networks can be if they are not monitored properly. If an angry customer or competitor wanted to spam a page with negative reviews it seems one day they could pay a machine to churn fabricated complaints out for them. Researchers believe this type of AI has the ability to dramatically disrupt certain industries. In order to test how convincing robot reviews were, researchers from the University of Chicago got 40 volunteers to see if they could tell the difference between real and fake reviews for 40 restaurants.