Co-located in Silicon Valley and Beijing, Baidu Research brings together top talent from around the world to focus on future-looking fundamental research in artificial intelligence. Our research directions include deep learning, computer vision, speech recognition and synthesis, natural language understanding, data mining and knowledge discovery, business intelligence, artificial general intelligence, high performance computing, robotics and autonomous driving. At Baidu Research, we aim to revolutionize human-machine interfaces with the latest artificial intelligence techniques. Our Deep Voice project was [...] The AAAI (Association for the Advancement of Artificial Intelligence) is one of the world's premiere artificial conferences, with annual summits [...] Today, we are excited to announce the hiring of three world-renowned artificial intelligence scientists, Dr. Kenneth Church, Dr. Jun Huan [...]

Can a new technique known as deep learning revolutionize artificial intelligence, as yesterday's front-page article at the New York Times suggests? There is good reason to be excited about deep learning, a sophisticated "machine learning" algorithm that far exceeds many of its predecessors in its abilities to recognize syllables and images. But there's also good reason to be skeptical. While the Times reports that "advances in an artificial intelligence technology that can recognize patterns offer the possibility of machines that perform human activities like seeing, listening and thinking," deep learning takes us, at best, only a small step toward the creation of truly intelligent machines. Deep learning is important work, with immediate practical applications.

In my last medium post, I discussed how we could use convolutional neural networks for gesture recognition in VR. I concluded that while it was really cool, drawing objects was sometimes more tedious that having a simple menu. What if I used neural networks to anticipate what objects I wanted to place? Think about it like a surgeon's assistant, getting the right tool at the right time, without being asked for it. It seemed like a logical next step.

It's harder than you might think to look at a microscope image of an untreated cell and identify its features. To make cell characteristics visible to the human eye, scientists normally have to use chemicals that can kill the very cells they want to look at. A groundbreaking study shows that computers can see details in images without using these invasive techniques. They can examine cells that haven't been treated and find a wealth of data that scientists can't detect on their own. In fact, images contain much more information than was ever thought possible.

Many of the exciting applications in Machine Learning have to do with images, which means they're likely built using Convolutional Neural Networks (or CNNs). This type of algorithm has been shown to achieve impressive results in many computer vision tasks and is a must-have part of any developer's or data scientist's modern toolkit. This tutorial will walk through the basics of the architecture of a Convolutional Neural Network (CNN), explain why it works as well as it does, and step through the necessary code piece by piece. You should finish this with a good starting point for developing your own more complex architecture and applying CNNs to problems that intrigue you. Thanks is due to ParisTech for the most of the code, and Ujjwal Karn for the intuitive explanation of CNNs. CNNs are a subset of the field of computer vision, which is all about applying computational techniques to visual content.

In the study, Arnaout and her colleagues used deep learning, specifically something called a convolutional neural network, to train an AI system that can classify echocardiograms according to the type of view shown. This classification is a cardiologist's first step when examining an image of the heart.

Artificial intelligence is permeating everybody's lives through the face recognition, voice recognition, image analysis and natural language processing capabilities built into their smartphones and consumer appliances. Over the next several years, most new consumer devices will run AI natively, locally and, to an increasing extent, autonomously. But there's a problem: Traditional processors in most mobile devices aren't optimized for AI, which tends to consume a lot of processing, memory, data and battery on these resource-constrained devices. As a result, AI has tended to execute slowly on mobile and "internet of things" endpoints, while draining their batteries rapidly, consuming inordinate wireless bandwidth and exposing sensitive local information as data makes roundtrips in the cloud. That's why mass-market mobile and IoT edge devices are increasingly coming equipped with systems-on-a-chip that are optimized for local AI processing.

In a very deep network such as a conv net with lots of filters and/or full layers, it seems to me that not all filters/weights are equally important - some could even be useless. Is there a scheme that removes these from the network (to save computation time)? I'm not talking about dropout which temporarily takes them out, I mean permanently take them out based on small gradients, high varience, etc. If anyone knows anything I'd appreciate some links/papers (sorry I'm new to NN).

Simulating human reasoning was the main reason Watson was introduced by IBM but now it has been broadened to include all other forms of AI. Much of the recent hype has been about machine learning that leads to predictive behavior and analysis for enterprises. Slowly, one of the most complex forms of AI, deep learning is also gaining momentum. The neurons in the human brains can connect to other neurons anyhow without any specific pattern. But neural networks using machine learning are a replication of the brain network and consist of more defined connections.