When the AI boom came a-knocking, Intel wasn't around to answer the call. Now, the company is attempting to reassert its authority in the silicon business by unveiling a new family of chips designed especially for artificial intelligence: the Intel Nervana Neural Network Processor family, or NNP for short. The NNP family is meant as a response to the needs of machine learning, and is destined for the data center, not your PC. Intel's CPUs may still be a stalwart of server stacks (by some estimates, it has a 96 percent market share in data centers), but the workloads of contemporary AI are much better served by the graphical processors or GPUs coming from firms like Nvidia and ARM. Consequently, demand for these companies' chips has skyrocketed.
Cisco today announced a new portfolio of predictive services that aims to help customers spot and predict IT failures through the use of AI. The lineup includes Business Critical Services, which uses analytics, automation, compliance and security tools to prevent system failures. There's also a new High-value Services product group that provides software and network support to help with scale, onboarding, analytics, and efficiency. Central to Cisco's pitch for the new services is that there's a growing digital skills gap in IT. Therefore, businesses need more AI-based tools to augment human ability in a way that fosters innovation while minimizing the impact of the skills gap.
What is an artificial neural network? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article. An artificial neural network (ANN) is a computational nonlinear model based on the neural structure of the brain that is able to learn to perform tasks like classification, prediction, decision-making, visualization, and others just by considering examples.
I discovered this neural network architecture (that I have named as Absolute Neural Network) while pondering over the thought: "Do we really use different parts of the brain while imagining something that we memorised?". I went on and investigated this question further and finally found the answer. We use the same part of the brain in the reverse direction while visualising an entity that we memorised in the forward direction. Key findings: 1.) A feed forward neural network can learn in both directions forward and backward. Seems ReLUs were just half correct.)
What if you could create an accurate summary of a lengthy article at the touch of a button? What if you could quickly scroll through a bibliography, filtered to show only the citations relevant to your needs? What if you could get your research out into the world faster, and have that knowledge built upon sooner? Science and technology are generating more data than ever faster than ever, so it's getter harder and harder to keep up and manage this information. Therefore, it's crucial to find ways to automate the discovery and interpretation of the information we need – and only that information.
Back in May, Google revealed its AutoML project; artificial intelligence (AI) designed to help them create other AIs. Now, Google has announced that AutoML has beaten the human AI engineers at their own game by building machine-learning software that's more efficient and powerful than the best human-designed systems. An AutoML system recently broke a record for categorizing images by their content, scoring 82 percent. While that's a relatively simple task, AutoML also beat the human-built system at a more complex task integral to autonomous robots and augmented reality: marking the location of multiple objects in an image. For that task, AutoML scored 43 percent versus the human-built system's 39 percent.
White-collar automation has become a common buzzword in debates about the growing power of computers, as software shows potential to take over some work of accountants and lawyers. Artificial-intelligence researchers at Google are trying to automate the tasks of highly paid workers more likely to wear a hoodie than a coat and tie--themselves. In a project called AutoML, Google's researchers have taught machine-learning software to build machine-learning software. In some instances, what it comes up with is more powerful and efficient than the best systems the researchers themselves can design. Google says the system recently scored a record 82 percent at categorizing images by their content.
As an Indian guy living in the US, I have a constant flow of money from home to me and vice versa. If the USD is stronger in the market, then the Indian rupee (INR) goes down, hence, a person from India buys a dollar for more rupees. If the dollar is weaker, you spend less rupees to buy the same dollar. If one can predict how much a dollar will cost tomorrow, then this can guide one's decision making and can be very important in minimizing risks and maximizing returns. Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.
Sophisticated tools capable of collecting and analyzing massive data sets and then displaying the results in visual form are no longer an option. They are becoming a necessity. On a daily basis, thousands upon thousands of monitoring stations around the world collect vast quantities of air quality data for use in spotting pollution problems, analyzing air quality trends, and guiding effective responses. To date, these monitoring stations have served as digital eyes and ears trained on the planet's atmosphere. But all of that seems likely to change in the not-too-distant future as evolving networks of air sensors that are just now beginning to be deployed around the globe result in an avalanche of data, all of which has the very real potential to overwhelm those trying to make sense of it.
In this step-by-step Keras tutorial, you'll learn how to build a convolutional neural network in Python! In fact, we'll be training a classifier for handwritten digits that boasts over 99% accuracy on the famous MNIST dataset. Before we begin, we should note that this guide is geared toward beginners who are interested in applied deep learning. Our goal is to introduce you to one of the most popular and powerful libraries for building neural networks in Python. That means we'll brush over much of the theory and math, but we'll also point you to great resources for learning those.