neural network


AI-first approach across industries will transform us: Microsoft CEO Satya Nadella

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Recent advances in Artificial Intelligence (AI) have been "pretty stunning" but what the humanity is going to see soon will be even more profound across the spectrum, Microsoft CEO Satya Nadella has stressed. Addressing thousands of partners at the'Microsoft Inspire' event here on Wednesday, Nadella said that the potential is for us to be able to turn every industry into an AI-first industry, be it retail, healthcare or agriculture. "It's happening because of the ability to provision lots of computing capability, to have lots of data, and these new techniques of algorithm promise around the deep neural net in particular," Nadella said. "For us to be able to turn every industry into an AI-first industry, whether it's retail or healthcare or agriculture, we want to be able to make sure that they can take their data, in a security-privacy preserving way, convert that into AI capability that they get the return on. Wherever there is data, the computer will migrate to data.


Feature-wise transformations

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The notion of a secondary network predicting the parameters of a primary network is also well exemplified by HyperNetworks, which predict weights for entire layers (e.g., a recurrent neural network layer). From this perspective, the FiLM generator is a specialized HyperNetwork that predicts the FiLM parameters of the FiLM-ed network. The main distinction between the two resides in the number and specificity of predicted parameters: FiLM requires predicting far fewer parameters than Hypernetworks, but also has less modulation potential. The ideal trade-off between a conditioning mechanism's capacity, regularization, and computational complexity is still an ongoing area of investigation, and many proposed approaches lie on the spectrum between FiLM and HyperNetworks (see Bibliographic Notes).


Using Deep Learning to automatically rank millions of hotel images

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For each hotel we receive dozens of images and face the challenge of choosing the most "attractive" image for each offer on our offer comparison pages, as photos can be just as important for bookings as reviews. Given that we have millions of hotel offers, we end up with more than 100 million images for which we need an "attractiveness" assessment. We addressed the need to automatically assess image quality by implementing an aesthetic and technical image quality classifier based on Google's research paper "NIMA: Neural Image Assessment". NIMA consists of two Convolutional Neural Networks (CNN) that aim to predict the aesthetic and technical quality of images, respectively. The models are trained via transfer learning, where ImageNet pre-trained CNNs are fine-tuned for each quality classification tasks.


A Project Based Introduction to TensorFlow.js

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In this post we introduce how to use TensorFlow.js by demonstrating how it was used in the simple project Neural Titanic. In this project, we show how to visualize the evolution of the predictions of a single layer neural network as it is being trained on the tabular Titanic Dataset for the task of binary classification of passenger survival. Here's a look at what we will produce and a link to the live demo: This article assumes that you have a basic understanding of modern frontend JavaScript development and a general awareness of basic machine learning topics. Please let me know in the comments if you have any questions. Neural networks are finally getting their rightfully deserved day in the sun after many years of development by a rather small research community spearheaded by the likes of Geoffrey Hinton, Yoshua Bengio, Andrew Ng, and Yann LeCun. Traditional statistical models do very well on structured data, i.e. tabular data, but have notoriously struggled with unstructured data like images, audio, and natural language.


Huawei now wants developers to exploit its artificial intelligence capabilities

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After having tested the waters with artificial intelligence in its top-end devices, Huawei will now open up its platform for developers to exploit it to the fullest. "Developers need to understand how AI works so they can build those capabilities into their apps. For us, the biggest challenge is natural interaction, and direct service access, when it comes to AI," Huawei's director of AI James Lu told indianexpress.com The telecom giant and smartphone manufacturer plans to hold a global developer conference on the lines of Apple's WWDC soon and hopes to rope in more developers, who will use the company's Application Programming Interface (APIs) in their apps. However, this seems to the natural progression of what has been a clear focus on AI for Huawei.


Eagle-eyed machine learning algorithm outdoes human experts

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Artificial intelligence is now so smart that silicon brains frequently outthink people. Computers operate self-driving cars, pick friends' faces out of photos on Facebook, and are learning to take on jobs typically entrusted only to human experts. Researchers from the University of Wisconsin–Madison and Oak Ridge National Laboratory have trained computers to quickly and consistently detect and analyze microscopic radiation damage to materials under consideration for nuclear reactors. And the computers bested humans in this arduous task. "Machine learning has great potential to transform the current, human-involved approach of image analysis in microscopy," says Wei Li, who earned his master's degree in materials science and engineering this year from UW–Madison.


Deep Learning-Based Method Predicts Non-Coding Mutation Effects, Disease Risk

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New York (GenomeWeb) – Researchers from Princeton University and the Flatiron Institute's Center for Computational Biology have developed a deep learning approach that they say can predict the effects of genetic variants in noncoding regions on gene expression in specific tissues as well as on disease risk.


DeepMind have created an IQ test for AI, and it didn't do too well

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Finally, they tested the systems. In some cases, they used test problems with the same abstract factors as the training set -- like both training and testing the AI on problems that required it to consider the number of shapes in each image. In other cases, they used test problems incorporating different abstract factors than those in the training set. For example, they might train the AI on problems that required it to consider the number of shapes in each image, but then test it on ones that required it to consider the shapes' positions to figure out the right answer.


Artificial neural networks now able to help reveal a brain's structure

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The function of the brain is based on the connections between nerve cells. In order to map these connections and to create the connectome, the "wiring diagram" of a brain, neurobiologists capture images of the brain with the help of three-dimensional electron microscopy. Up until now, however, the mapping of larger areas has been hampered by the fact that, even with considerable support from computers, the analysis of these images by humans would take decades. Scientists from Google AI and the Max Planck Institute of Neurobiology describe a method based on artificial neural networks that is able to reconstruct entire nerve cells with all their elements and connections almost error-free from image stacks. This milestone in the field of automatic data analysis could bring us much closer to mapping and in the long term also understanding brains in their entirety.


Moving closer to completely optical artificial neural network: Optical training of neural networks could lead to more efficient artificial intelligence

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"Using an optical chip to perform neural network computations more efficiently than is possible with digital computers could allow more complex problems to be solved," said research team leader Shanhui Fan of Stanford University. "This would enhance the capability of artificial neural networks to perform tasks required for self-driving cars or to formulate an appropriate response to a spoken question, for example. It could also improve our lives in ways we can't imagine now." An artificial neural network is a type of artificial intelligence that uses connected units to process information in a manner similar to the way the brain processes information. Using these networks to perform a complex task, for instance voice recognition, requires the critical step of training the algorithms to categorize inputs, such as different words.