In the recent years, there has been so much progress in the field of artificial intelligence. Developments in the field have produced countless innovations that help us better in understanding speech, and images, as well as improving how games are played. Now the company who has contributed a lot in this field has created a chip to keep this going. Nvidia announced their new chip, the Tesla P100. It's a chip that is designed to add more power to "deep learning".

Like the large-vocabulary speech recognition paper we looked at yesterday, today's paper has also been described as a landmark paper in the history of deep learning. The ImageNet dataset contains over 1.5 million labeled high-resolution images of objects in roughly 22,000 categories. The annual ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition uses a subset of ImageNet with roughly 1000 images in each of 1000 categories. There are 1.2M training images, 50,000 validation images, and 150,000 testing images. For reporting error rates, a model predicts the top 5 most likely labels.

The following interview is one of many included in the report. As part of my ongoing series of interviews surveying the frontiers of machine intelligence, I recently interviewed Yoshua Bengio. Bengio is a professor with the department of computer science and operations research at the University of Montreal, where he is head of the Machine Learning Laboratory (MILA) and serves as the Canada Research Chair in statistical learning algorithms. The goal of his research is to understand the principles of learning that yield intelligence. Yoshua Bengio: I have been researching neural networks since the '80s.

The recent emergence of a powerful machine-learning technique known as deep learning has made computing giants such as Google, Facebook, and Microsoft even hungrier for data. It's what lets software learn to do things like recognize images or understand language. Yet many problems where deep learning could be most valuable involve data that is hard to come by or is held by organizations that are unwilling to share it. And as Apple CEO Tim Cook puts it, some consumers are already concerned about companies "gobbling up" their personal information. "A lot of people who hold sensitive data sets like medical images are just not going to share them for legal and regulatory concerns," says Vitaly Shmatikov, a professor at Cornell Tech who studies privacy.

FANUC Corp. is teaming up with Cisco, Rockwell Automation and Preferred Networks (PFN) to develop and deploy the FANUC Intelligent Edge Link and Drive (FIELD) system for major automotive manufacturers. The new Industrial Internet of Things (IIoT) platform will expand upon the FANUC ZDT (Zero Downtime) project to connect not only CNC machines and industrial robots, but also peripheral devices and sensors for greater analytical data to improve overall equipment efficiency (OEE) and increase profitability. "With a secure scalable compute approach to analyze this data--from device to the enterprise--users can improve operations and make more informed decisions tailored to meet the needs of their organizations," said Sujeet Chand, CTO of Rockwell Automation. The Allen-Bradley Stratix Ethernet switches from Rockwell Automation will be used to connect FANUC robots to Cisco's ZDT Data Collection software. PFN provides the FIELD system with an open deep-learning framework (Chainer), an IoT Stream Engine (SensorBee) and other machine learning libraries within its Deep Intelligence in-Motion (DIMo) platform.

In this blog post, I'll talk about the Visual Question Answering problem, and I'll also present neural network based approaches for same. The source code for this blog post is written in Python and Keras, and is available on Github. An year or so ago, a chatbot named Eugene Goostman made it to the mainstream news, after having been reported as the first computer program to have passed the famed Turing Test in an event organized at the University of Reading. While the organizers hailed it as a historical achievement, most of the scientific community wasn't impressed. This leads us to the question: Is the Turing Test, in its original form, a suitable test for AI in the modern day?

Open source tools are increasingly important in the data science workflow. Github has become the de facto open source software clearinghouse, hosting all imaginable types of projects. Given the growing adoption of deep learning in academia, research, and hobby, and its increasing role in data science, we are exploring the top deep learning projects available on Github.*

Forget about the meaning of words, forget about grammar, forget about syntax, forget even the very concept of a word. Now let the machine learn everything by itself.

According to Wikipedia, deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. Deep learning is sometimes defined as the intersection between machine learning and artificial intelligence. Many articles on deep learning can be found here.

Artificial Intelligence (AI) is a growing area, and for good reason. But where AI lacks, humans excel. Humans, sometimes to a fault, are great at collaborating and checking in on nearly everything to make sure it works. So doesn't it make sense for the humans and AI to work side by side? This is just what DigitalGenius is doing to provide top-notch customer service for you.