As many jobs are disappearing to automation, the latest profession to also start seeing the future may be dermatology. Stanford University researchers have developed a deep convolutional neural network, an artificial intelligence technique for building a knowledge set, to learn how to spot suspect cancer lesions. Today this process is manual and prone to errors of subjectivity. Dermatologists simply look through a dermatoscope and judge based on their education and experience. The Stanford system was given 130,000 images of skin lesions simply labeled with previously established diagnoses that included more than 2,000 diseases.

"Nanodegree" is a registered trademark of Udacity. Udacity is not an accredited university and we don't confer traditional degrees. Udacity Nanodegree programs represent collaborations with our industry partners who help us develop our content and who hire many of our program graduates.

Deep neural networks have evolved to be the state-of-the-art technique for machine learning tasks ranging from computer vision and speech recognition to natural language processing. However, deep learning algorithms are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, deep compression significantly reduces the computation and storage required by neural networks. For example, for a convolutional neural network with fully connected layers, such as Alexnet and VGGnet, it can reduce the model size by 35x-49x. Even for fully convolutional neural networks such as GoogleNet and SqueezeNet, deep compression can still reduce the model size by 10x.

Businesses that adopt artificial intelligence technology to help with jobs like automating call center activity must also consider giving employees education and training so that those who are displaced by innovation can still work. In short, companies should realize that innovation can cause human pain and that they should do something to minimize it. Accenture joins the countless other analysts, technologists, and researchers who claim that the rise of artificial intelligence technologies like deep learning is ushering a new age. Deep learning, when done right, can help developers build software that can sift through mountains of data, recognize patterns, and take action. Companies like Amazon (amzn) and Google (goog) are using AI to improve their digital assistants, those voice operated helpers on smartphones and home automation hubs, said Accenture chief technology officer Paul Daugherty during a Wednesday media event.

IBM today took the wraps off a new release of PowerAI, the prepackaged bundle of deep learning frameworks that debuted last fall. With the addition of Google's TensorFlow framework, the company says its AI business model is starting to resemble the Linux distributor Red Hat. "In a sense, PowerAI makes IBM the Red Hat of deep learning," Sumit Gupta, the vice president of IBM's High Performance Computing & Data Analytics business, told Datanami. In the same way, instead of going to TensorFlow or Caffe or other websites [for deep learning frameworks], they want an enterprise-level distribution." The first release of PowerAI included "optimized" versions of Caffe-bvlc, Caffe-ibm, Caffe-nv, DIGITS, Torch, and Theano. With this release, the software includes TensorFlow 0.12, as well as Chainer, a deep learning framework that's very popular in Japan. Gupta says PowerAI--which is free and distributed as a binary for Ubuntu Linux (sorry, Red Hat)--is resonating with IBM clients, particularly those who have bought the "Minsky" Power8 servers to run deep learning and machine learning workloads on. "Enterprise customers prefer not to go to an open source website and download software and build it.

To complement existing offerings like Sparkling Water and Steam, H2O.ai is releasing Deep Water, a new tool to help businesses make deep learning a part of everyday operations. Deep Water will open up new possibilities for the TensorFlow, MXNet and Caffe communities to engage with H2O.ai. This also means that the GPU is set to become a greater part of business operations for the entire Fortune 500, not just tech companies. SriSatish Ambati, CEO of H2O.ai, says his company has found a sweet spot with predictive analytics. Ambati gave me the example of an insurance provider using H2O to analyze images of roofs and provide insights for preventative maintenance.

Recurrent neural networks (RNNs) have drawn interest from machine learning researchers because of their effectiveness at preserving past inputs for time-varying data processing tasks. To understand the success and limitations of RNNs, it is critical that we advance our analysis of their fundamental memory properties. We focus on echo state networks (ESNs), which are RNNs with simple memoryless nodes and random connectivity. In most existing analyses, the short-term memory (STM) capacity results conclude that the ESN network size must scale linearly with the input size for unstructured inputs. The main contribution of this paper is to provide general results characterizing the STM capacity for linear ESNs with multidimensional input streams when the inputs have common low-dimensional structure: sparsity in a basis or significant statistical dependence between inputs. In both cases, we show that the number of nodes in the network must scale linearly with the information rate and poly-logarithmically with the ambient input dimension. The analysis relies on advanced applications of random matrix theory and results in explicit non-asymptotic bounds on the recovery error. Taken together, this analysis provides a significant step forward in our understanding of the STM properties in RNNs.

Deep learning in 2017 is magical. We get to apply immensely complex algorithms to equally complex problems without having to spend all our time writing the algorithms ourselves. Instead, thanks to libraries like TensorFlow and Keras, we get to focus on the fun stuff: model architecture, parameter tuning and data augmentation. Today, we'll explore one such application of deep learning. We'll use the Udacity self-driving car nanodegree program simulator to train a generalized steering model in under 100 lines of code.

Are you familiar with deep learning? Deep learning describes the ability for artificial intelligence (AI) algorithms to learn from our behavior using brain-like structures called neural networks, and it's changing the field of human resources in significant ways. AI programs can predict outcomes based on past experiences fed into the program. Because AI can recognize patterns and analyze data at light speed, it can help HR directors make decisions with greater confidence. From finding and recruiting prospects to streamlining employee assessment processes, machine learning and AI can make it easier for HR executives to do their jobs better--and today's technology is only the beginning.

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words. Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it's vital we understand this new language, and what it's increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it. The language is a new class of machine learning known as deep learning, and the "whispered word" was a computer's use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat.