Their neural network software - originally developed for NASA - uses a bio-inspired approach to mimic the way the human brain learns and analyzes its environment. This software enables a variety of smart products - from self-driving cars and industrial drones to toys, consumer electronics and smart cameras - to learn, adapt and interact in real time. For example, toys can learn to identify their owners, security cameras can identify specific threats, drones can learn how to diagnose problems at the tops of cell towers, saving humans considerable danger and drudgery, and self-driving cars can be safer and learn to avoid obstacles.

As artificial intelligence becomes less of an ambiguous marketing buzzword and more of a precise ideology, it's increasingly becoming a challenge to understand all of the AI terms out there. So to kick off the brand new AI Zone, the Editorial Team here at DZone got together to define some of the biggest terms in the world of artificial intelligence for you. Algorithms: A set of rules or instructions given to an AI, neural network, or other machines to help it learn on its own; classification, clustering, recommendation, and regression are four of the most popular types. Artificial intelligence: A machine's ability to make decisions and perform tasks that simulate human intelligence and behavior. Artificial neural network (ANN): A learning model created to act like a human brain that solves tasks that are too difficult for traditional computer systems to solve.

Sony has followed the example of Google, Amazon and Facebook by open sourcing AI software in search for deep learning developers. Sony has followed in the path of Google, Facebook and Amazon, as it opens up access to its deep-learning software tools in an aim to attract artificial intelligence developers. The company announced that it has made its Neural Network Libraries available in open source, giving software engineers and designers access the core libraries for free to develop the necessary deep learning programs. Sony says the neural network design is a core development of any deep learning program and the shift to open source acts as a method to enable the development community to build on the core libraries' programs. The software in Sony's core libraries is written in C 11 and the programming language runs in different environments and operates on Linux, Windows and other platforms.

Neural Networks are a family of Machine Learning techniques modelled on the human brain. Being able to extract hidden patterns within data is a key ability for any Data Scientist and Neural Network approaches may be especially useful for extracting patterns from images, video or speech. The following blog aims to explain at a high level how these methods work and key things to bear in mind. At first these weights should be randomized. In a basic neural network, you train the system by running individual cases through one at a time and updating the weights based on the error.

Growing usage of big data in healthcare industry and imbalance between health workforce and patients is expected to drive the growth of the AI in healthcare market The artificial intelligence (AI) in healthcare market was valued at USD 667.1 million in 2016 and is expected to reach USD 7,988.8 million by 2022, at a CAGR of 52.68% between 2017 and 2022. The growth of this market is driven by the growing usage of Big Data in healthcare industry, ability of AI to improve patient outcomes, imbalance between health workforce and patients, reducing the healthcare costs, growing importance on precision medicine, cross-industry partnerships, and significant increase in venture capital investments in AI in healthcare domain. However, reluctance among medical practitioners to adopt AI-based technologies and ambiguous regulatory guidelines for medical software are the major factors restraining the growth of the AI in healthcare market. Faster calculations and lesser power consumption are the factors driving the growth of the hardware market for AI in healthcare Hardware which includes GPUs, DSPs, FPGAs, and neuromorphic chips is expected to grow at the highest rate in the offering segment of AI in healthcare. The GPU, DSP, and FPGA are widely used to implement the deep learning algorithm.

Yes, but I'm pretty sure there is no direct contact between the Theano guys and this project. I don't know on what level they are collaborating with the other frameworks' teams, but I assume they do, they could be collaborating for updating libgpuarray as well. I do hope we have some progress there as well yes.

Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between their models. This connection is, however, not effectively utilized today, since people usually train the models of two dual tasks separately and independently. In this work, we propose training the models of two dual tasks simultaneously, and explicitly exploiting the probabilistic correlation between them to regularize the training process. For ease of reference, we call the proposed approach \emph{dual supervised learning}. We demonstrate that dual supervised learning can improve the practical performances of both tasks, for various applications including machine translation, image processing, and sentiment analysis.

We propose a number of new algorithms for learning deep energy models from data motivated by a recent Stein variational gradient descent (SVGD) algorithm, including a Stein contrastive divergence (SteinCD) that integrates CD with SVGD based on their theoretical connections, and a SteinGAN that trains an auxiliary generator to generate the negative samples in maximum likelihood estimation (MLE). We demonstrate that our SteinCD trains models with good generalization (high test likelihood), while Stein-GAN can generate realistic looking images competitive with GAN-style methods. We show that by combing SteinCD and SteinGAN, it is possible to inherent the advantage of both approaches.

The recent successes of deep learning are partially attributed to supervised training of networks with large numbers of parameters using large datasets. In computer vision, supervised training of convolutional networks with very large labeled datasets provide state-of-the-art solutions in many applications such as object recognition, image captioning and question answering. While it has been shown that convolutional networks have low generalization error, their generalization capability does not extend to samples which are not adequately represented by the training data. A potential source of mismatch between the training data distribution and new samples is appearance. To a human, the images shown in Figure 3 (top row) unambiguously represent the digits "4", "2" and "6" whereas a convolutional network trained on the original MNIST dataset has a low probability of producing the correct answer for the modified digit images. The reason that a human has an easy time at this task is not because he has previously been exposed to the particular representations of the digits shown in Figure 3, but because he is able to adapt to novel appearances of learned concepts. Invariances to a predetermined set of transformations such as translation, rotation, contrast and noise can be taught to the network via methods such as tangent prop [1] and data augmentation [2]; however, these methods can not adapt to new appearances such as those shown in Figure 3. Similarly, domain adaptation [3, 4] offers a solution only if a sufficient number of images in the target domain are available.

Regression or classification? This is perhaps the most basic question faced when tackling a new supervised learning problem. We present an Evolutionary Deep Learning (EDL) algorithm that automatically solves this by identifying the question type with high accuracy, along with a proposed deep architecture. Typically, a significant amount of human insight and preparation is required prior to executing machine learning algorithms. For example, when creating deep neural networks, the number of parameters must be selected in advance and furthermore, a lot of these choices are made based upon pre-existing knowledge of the data such as the use of a categorical cross entropy loss function. Humans are able to study a dataset and decide whether it represents a classification or a regression problem, and consequently make decisions which will be applied to the execution of the neural network. We propose the Automated Problem Identification (API) algorithm, which uses an evolutionary algorithm interface to TensorFlow to manipulate a deep neural network to decide if a dataset represents a classification or a regression problem. We test API on 16 different classification, regression and sentiment analysis datasets with up to 10,000 features and up to 17,000 unique target values. API achieves an average accuracy of $96.3\%$ in identifying the problem type without hardcoding any insights about the general characteristics of regression or classification problems. For example, API successfully identifies classification problems even with 1000 target values. Furthermore, the algorithm recommends which loss function to use and also recommends a neural network architecture. Our work is therefore a step towards fully automated machine learning.