A text-generating artificial intelligence (AI) algorithm whose creators initially deemed too dangerous to release – given its ability to churn out fake news, spam and misinformation after feasting on a mere headline – has been unleashed. So far, so good, says the research lab, OpenAI. In a blog post last week, the lab said that the researchers have seen "no strong evidence of misuse" of the machine-learning language model, which is called GPT-2… at least, not yet. While we've seen some discussion around GPT-2's potential to augment high-volume/low-yield operations like spam and phishing, we haven't seen evidence of writing code, documentation, or instances of misuse […] We acknowledge that we cannot be aware of all threats, and that motivated actors can replicate language models without model release. Exactly how convincing is the output?

In this pattern, learn how to create and deploy deep learning models by using a Jupyter Notebook in an IBM Watson Studio environment. You also create deep learning experiments with hyperparameters optimization by using a Watson Studio GUI for monitoring different runs, then select the best model for deployment. Computer vision is on the rise, and there might be scenarios where a machine must classify images based on their class to aid in the decision-making process. In this code pattern, we demonstrate how to do multiclass classification (with three classes) by using IBM Watson Studio and IBM Deep Learning as a Service. We use yoga postures data to identify the class given an image.

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It is always fun and educational to read deep learning scientific papers. Especially if it is in the area of the current project that you are working on. However, often these papers contain architectures and solutions that are hard to train. Especially if you want to try out, let's say, some of the winners of ImageNet Large Scale Visual Recognition (ILSCVR) competition. I can remember reading about VGG16 and thinking "That is all cool, but my GPU is going to die".

To increase the accuracy of the analysis, deep sequencing data were filtered; target sequences with deep sequencing read counts below 200 and background indel frequencies above 8% were excluded as similarly performed previously (21). DNase-sequencing (DNase-seq) narrow peak data from ENCODE (36) were used to calculate chromatin accessibility as previously described (21). For each target site, 23 bases of the PAM plus protospacer sequence were aligned to the hg19 human reference genome using bowtie (41). Only the target sites that overlapped with DNase-seq narrow peaks were considered as DNase I hypersensitive target sites. We divided the Endo_Cas9 dataset into paired subsets by stratified random sampling from strata of DHS and non-DHS sites so that a similar ratio of DHS/non-DHS sites was assigned to each subset.

As a business technologist, I am beyond excited about the possibilities of artificial intelligence, machine learning and deep learning ("AI") and all of the …

Through this partnership, the companies will accelerate breakthroughs in AI and power OpenAI's efforts to create artificial general intelligence (AGI). The resulting enhancements to the Azure platform will also help developers build the next generation of AI applications. The companies will focus on building a computational platform in Azure of unprecedented scale, which will train and run increasingly advanced AI models, include hardware technologies that build on Microsoft's supercomputing technology, and adhere to the two companies' shared principles on ethics and trust. This will create the foundation for advancements in AI to be implemented in a safe, secure and trustworthy way and is a critical reason the companies chose to partner together. Over the past decade, innovative applications of deep neural networks coupled with increasing computational power have led to continuous AI breakthroughs in areas such as vision, speech, language processing, translation, robotic control and even gaming.

--Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this paper, we present a two-layer online learning framework for robust anomaly detection (RAD) in the presence of unreliable anomaly labels, where the first layer is to filter out the suspicious data, and the second layer detects the anomaly patterns from the remaining data. T o adapt to the online nature of anomaly detection, we extend RAD with additional features of repetitively cleaning, conflicting opinions of classifiers, and oracle knowledge. We online learn from the incoming data streams and continuously cleanse the data, so as to adapt to the increasing learning capacity from the larger accumulated data set. Moreover, we explore the concept of oracle learning that provides additional information of true labels for difficult data points. We specifically focus on three use cases, (i) detecting 10 classes of IoT attacks, (ii) predicting 4 classes of task failures of big data jobs, (iii) recognising 20 celebrities faces. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98% for IoT device attacks (i.e., 11%), up to 84% for cloud task failures (i.e., 20%) under 40% noise, and up to 74% for face recognition (i.e., 28%) under 30% noisy labels. The proposed RAD is general and can be applied to different anomaly detection algorithms. Anomaly detection is one of the core operations for enforcing dependability and performance in modern distributed systems [29], [44]. Anomalies can take various forms including erroneous data produced by a corrupted IoT device or the failure of a job executed in a datacenter [6], [7], [47]. Dealing with this issue has often been done in recent art by relying on machine learning-based classification algorithms over system logs [11], [13] or backend collected data [17], [46]. This work has been partly supported by the IRS (Initialtive de Recherche Strat egique) program DA TE. This work has been partly funded by the Swiss National Science Foundation NRP75 project 407540 167266 and TU Delft technology fellowship. As workloads at real systems are highly dynamic over time, it is even more challenging to predict anomalies that can not be easily distinguished from the system dynamics, compared to the systems with static workloads. In this context, a rising concern when applying classification algorithms is the accessibility to a reliable ground truth for anomalies [9].

Federated Learning enables training of a general model through edge devices without sending raw data to the cloud. Hence, this approach is attractive for digital health applications, where data is sourced through edge devices and users care about privacy. Here, we report on the feasibility to train deep neural networks on the Raspberry Pi4s as edge devices. A CNN, a LSTM and a MLP were successfully trained on the MNIST data-set. Further, federated learning is demonstrated experimentally on IID and non-IID samples in a parametric study, to benchmark the model convergence. The weight updates from the workers are shared with the cloud to train the general model through federated learning. With the CNN and the non-IID samples a test-accuracy of up to 85% could be achieved within a training time of 2 minutes, while exchanging less than $10$ MB data per device. In addition, we discuss federated learning from an use-case standpoint, elaborating on privacy risks and labeling requirements for the application of emotion detection from sound. Based on the experimental findings, we discuss possible research directions to improve model and system performance. Finally, we provide best practices for a practitioner, considering the implementation of federated learning.

Recurrent neural networks (RNNs) have gained a great deal of attention in solving sequential learning problems. The learning of long-term dependencies, however, remains challenging due to the problem of a vanishing or exploding hidden states gradient. By exploring further the recently established connections between RNNs and dynamical systems we propose a novel RNN architecture, which we call a Hamiltonian recurrent neural network (Hamiltonian RNN), based on a symplectic discretization of an appropriately chosen Hamiltonian system. The key benefit of this approach is that the corresponding RNN inherits the favorable long time properties of the Hamiltonian system, which in turn allows us to control the hidden states gradient with a hyperparameter of the Hamiltonian RNN architecture. This enables us to handle sequential learning problems with arbitrary sequence lengths, since for a range of values of this hyperparameter the gradient neither vanishes nor explodes. Additionally, we provide a heuristic for the optimal choice of the hyperparameter, which we use in our numerical simulations to illustrate that the Hamiltonian RNN is able to outperform other state-of-the-art RNNs without the need of computationally intensive hyperparameter optimization.