In February, an artificial intelligence lab cofounded by Elon Musk informed the world that its latest breakthrough was too risky to release to the public. OpenAI claimed it had made language software so fluent at generating text that it might be adapted to crank out fake news or spam. On Thursday, two recent master's graduates in computer science released what they say is a re-creation of OpenAI's withheld software onto the internet for anyone to download and use. Aaron Gokaslan, 23, and Vanya Cohen, 24, say they aren't out to cause havoc and don't believe such software poses much risk to society yet. The pair say their release was intended to show that you don't have to be an elite lab rich in dollars and PhDs to create this kind of software: They used an estimated $50,000 worth of free cloud computing from Google, which hands out credits to academic institutions.

We often use machine learning to try to uncover patterns in data. In order for those patterns to be useful they should be meaningful and express some underlying structure. This can be seen in the Euclidean-inspired loss functions we use for generative models as well as for regularization. However, global geometry, which is the focus of Topology, also deals with meaningful structure, the only difference being that the structure is global instead of local. Topology is at present less exploited in machine learning, which is also why it is important to make it more available to the machine learning community at large. Still, topology applied to real world data using persistent homology has started to find applications within machine learning (including deep learning), but again, compared to its sibling local geometry, it is heavily underrepresented in these domains. In this post, we provide a high-level description of how our TopologyLayer allows (in just a few lines of PyTorch) for backpropagation through Persistent Homology computations and provides instructive, novel, and useful applications within machine learning and deep learning.

A new report as a GPU For Deep Learning Market that includes a comprehensive analysis of the Worldwide Market.

The new product enables enterprises to virtualize AI, machine learning and deep learning workloads, either on premise or as part of VMware Cloud on AWS.

While machine learning today is dominated by deep neural network research, in the 1990s neural approaches were not recognized as reliable for real-world applications. Back then, researchers put their efforts into kernel methods and support vector machines (SVM). One of the most notable and respected contributors to kernel methods and SVM is John Shawe-Taylor, a professor at University College London (UK) and Director of the Centre for Computational Statistics and Machine Learning (CSML). His main research area is Statistical Learning Theory, but his contributions range from neural networks to machine learning and graph theory. Shawe-Taylor has published over 300 papers with over 42000 citations.

In our previous articles, we have talked about Time Series Forecasting and Recurrent Neural Network. We explored what it is and how it is important in the class of Machine Learning algorithms. We even implemented a simple LSTM Network to evaluate its performance on the MNIST dataset. In this tutorial, we will take it a little further by forecasting a real-world data. The cryptocurrency market has seen its rise and fall in the past few years.

We all hear these terms being thrown around and often used interchangeably; some of us tag along without knowing what they mean, or we might see them as buzzwords, and others claim to know -- and do -- what these terms really entail. Note that the distinctions between these terms aren't clear-cut, but this will give a sense of the general uses of the terms, how they are related to one another, and how all are threaded together by data science. Artificial Intelligence describes machines that can perform tasks resembling those of humans. So AI implies machines that artificially model human intelligence. AI systems help us manage, model, and analyze complex systems.

Fremont, CA: Cybersecurity, otherwise known as information technology security, refers to the act of protecting the data, systems, networks, and programs from digital attacks. In the existing connected world, the necessity to secure systems is rising. It is evident from the prevailing cyberattacks in different industry sectors like health care, government agencies, education institutions, and energy. According to the UK government's Cyber Security Breaches Survey 2019, 32% of businesses have identified cybersecurity breaches or attacks in the past 12 months. Besides, with the advent of new devices that overhaul people, security risks are evolving, and cyberattackers are becoming more innovative in implementing these attacks.

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.

A team of researchers at UCLA developed a cancer-detection mechanism based on deep learning technologies. The research team developed a device for detecting the presence of cancer cells in the blood. The project was accomplished in collaboration with NantWorks, and it spans into new areas of cancer research. This is the most distinct function of the device, and it could facilitate quick extraction of cancer cells. This device could also help in accentuating blood cancer treatments.