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Building Better Deep Learning Requires New Approaches Not Just Bigger Data

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In its rush to solve all the world's problems through deep learning, Silicon Valley is increasingly embracing the idea of AI as a universal solver that can be rapidly adapted to any problem in any domain simply by taking a stock algorithm and feeding it relevant training data. The problem with this assumption is that today's deep learning systems are little more than correlative pattern extractors that search large datasets for basic patterns and encode them into software. While impressive compared to the standards of previous eras, these systems are still extraordinarily limited, capable only of identifying simplistic correlations rather than actually semantically understanding their problem domain. In turn, the hand-coded era's focus on domain expertise, ethnographic codification and deeply understanding a problem domain has given way to parachute programming in which deep learning specialists take an off-the-shelf algorithm, shove in a pile of training data, dump out the resulting model and move on to the next problem. Truly advancing the state of deep learning and way in which companies make use of it will require a return to the previous era's focus on understanding problems rather than merely churning canned models off assembly lines.


Paige touts paper in Nature Medicine on AI for pathology

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Pathology artificial intelligence (AI) software developer Paige is highlighting a paper published July 15 in Nature Medicine that indicates the company's technology can be used to develop AI algorithms with "near-perfect accuracy" for analyzing pathology slides for prostate cancer, skin cancer, and breast cancer. In the paper, Chief Scientific Officer Thomas Fuchs, PhD, of Memorial Sloan Kettering Cancer Center and colleagues describe how a series of deep-learning algorithms for clinical decision support in pathology were developed with an automated training and testing technique. Fuchs is the senior author on the paper, with his student Gabriele Campanella as the first author. The deployment of clinical decision support for pathology has been hindered by the need to curate large, manually annotated datasets to test and train AI algorithms, the authors noted. Instead, Campanella et al present a system in which algorithms are trained using only the reported diagnoses.


How to Implement Wasserstein Loss for Generative Adversarial Networks

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The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a loss function that correlates with the quality of generated images. It is an important extension to the GAN model and requires a conceptual shift away from a discriminator that predicts the probability of a generated image being "real" and toward the idea of a critic model that scores the "realness" of a given image. This conceptual shift is motivated mathematically using the earth mover distance, or Wasserstein distance, to train the GAN that measures the distance between the data distribution observed in the training dataset and the distribution observed in the generated examples. In this post, you will discover how to implement Wasserstein loss for Generative Adversarial Networks. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.


Neo4j: How a lack of context awareness is hampering AI development

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What do we mean when we say'context'? In essence, context is the information that frames something to give it meaning. Taken on its own, a shout could be anything from an expression of joy to warning. In the context of a structured piece of on-stage Grime, it's what made Stormzy's appearance at Glastonbury the triumph it was. The problem is that context doesn't come free – it has to be discovered.


Academia's Facial Recognition Datasets Illustrate The Globalization Of Today's Data

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This week's furor over FaceApp has largely centered on concerns that its Russian developers might be compelled to share the app's data with the Russian government, much as the Snowden disclosures illustrated the myriad ways in which American companies were compelled to disclose their private user data to the US government. Yet the reality is that this represents a mistaken understanding of just how the modern data trade works today and the simple fact that American universities and companies routinely make their data available to companies all across the world, including in Russia and China. In today's globalized world, data is just as globalized, with national borders no longer restricting the flow of our personal information - trend made worse by the data-hungry world of deep learning. Data brokers have long bought and sold our personal data in a shadowy world of international trade involving our most intimate and private information. The digital era has upended this explicit trade through the interlocking world of passive exchange through analytics services.


AI fails to recognize these nature images 98% of the time

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You can't fool all the people all the time, but a new dataset of untouched nature photos seems to confuse state-of-the-art computer vision models all but two-percent of the time. AI just isn't very good at understanding what it sees, unlike humans who can use contextual clues. The new dataset is a small subset of ImageNet, an industry-standard database containing more than 14 million hand-labeled images in over 20,000 categories. The purpose of ImageNet is to teach AI what an object is. If you want to train a model to understand cats, for example, you'd feed it hundreds or thousands of images from the "cats" category.


3 Levels of Deep Learning Competence

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Deep learning is not a magic bullet, but the techniques have shown to be highly effective in a large number of very challenging problem domains. This means that there is a ton of demand by businesses for effective deep learning practitioners. The problem is, how can the average business differentiate between good and bad practitioners? As a deep learning practitioner, how can you best demonstrate that you can deliver skillful deep learning models? In this post, you will discover the three levels of deep learning competence, and as a practitioner, what you must demonstrate at each level.


How to Get Started With Generative Adversarial Networks (7-Day Mini-Course)

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Generative Adversarial Networks, or GANs for short, are a deep learning technique for training generative models. The study and application of GANs are only a few years old, yet the results achieved have been nothing short of remarkable. Because the field is so young, it can be challenging to know how to get started, what to focus on, and how to best use the available techniques. In this crash course, you will discover how you can get started and confidently develop deep learning Generative Adversarial Networks using Python in seven days. Note: This is a big and important post. You might want to bookmark it. How to Get Started With Generative Adversarial Networks (7-Day Mini-Course) Photo by Matthias Ripp, some rights reserved.


Video classification with Keras and Deep Learning - PyImageSearch

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In this tutorial, you will learn how to perform video classification using Keras, Python, and Deep Learning. This tutorial will serve as an introduction to the concept of working with deep learning in a temporal nature, paving the way for when we discuss Long Short-term Memory networks (LSTMs) and eventually human activity recognition. To learn how to perform video classification with Keras and Deep learning, just keep reading! Videos can be understood as a series of individual images; and therefore, many deep learning practitioners would be quick to treat video classification as performing image classification a total of N times, where N is the total number of frames in a video. Video classification is more than just simple image classification -- with video we can typically make the assumption that subsequent frames in a video are correlated with respect to their semantic contents.


Is FaceApp an evil plot by 'the Russians' to steal your data? Not quite Arwa Mahdawi

The Guardian

Over the last few days the #faceappchallenge has taken over social media. This "challenge" involves downloading a selfie-editing tool called FaceApp and using one of its filters to digitally age your face. You then post the photo of your wizened old self on the internet and everyone laughs uproariously. You get a small surge of dopamine from gathering a few online likes before existential ennui sets in once again. On Monday, as the #faceappchallenge went viral, Joshua Nozzi, a software developer, warned people to "BE CAREFUL WITH FACEAPP….it Some media outlets picked this claim up and privacy concerns about the app began to mount. Concern escalated further when people started to point out that FaceApp is Russian. "The app that you're willingly giving all your facial data to says the company's location is in Saint-Petersburg, Russia," tweeted the New York Times's Charlie Warzel. And we all know what those Russians are like, don't we? They want to harvest your data for nefarious ...