Recently, Dynamic Business attended the 2-day Gartner ReImagine HR event to hear the new trends, reports and insights from HR industry leaders and share them with our readers. HR leaders from all over Australia came together to uncover and discuss the latest HR and leadership trends, best practice, challenges and opportunities. One of those discussions was from Jonathan Tabah, Director at Gartner, about what employees really think about AI, and we've got all the best bits wrapped up for you below. The reality is, whether you or your employees are aware of it or not, we are already using lots of examples of AI in our everyday lives. Do you use Google Maps to figure out how to get somewhere?

Automation and innovation in the work within business is necessary for the reinvention of the system landscapes. The same is possible with the machine learnings together with the help of the artificial intelligence platform. The industries in the recent time are in the tremendous need of the artificial intelligence platform to increase automation, machine interaction and to save time. Furthermore, problem-solving, social intelligence and general intelligence can also be achieved with the help of the artificial intelligence platform. The artificial intelligence platform market is expected to grow during the forecast period due to growth in adoption of cloud based application and services.

Network attack is a significant security issue for modern society. From small mobile devices to large cloud platforms, almost all computing products, used in our daily life, are networked and potentially under the threat of network intrusion. With the fast-growing network users, network intrusions become more and more frequent, volatile and advanced. Being able to capture intrusions in time for such a large scale network is critical and very challenging. To this end, the machine learning (or AI) based network intrusion detection (NID), due to its intelligent capability, has drawn increasing attention in recent years. Compared to the traditional signature-based approaches, the AI-based solutions are more capable of detecting variants of advanced network attacks. However, the high detection rate achieved by the existing designs is usually accompanied by a high rate of false alarms, which may significantly discount the overall effectiveness of the intrusion detection system. In this paper, we consider the existence of spatial and temporal features in the network traffic data and propose a hierarchical CNN+RNN neural network, LuNet. In LuNet, the convolutional neural network (CNN) and the recurrent neural network (RNN) learn input traffic data in sync with a gradually increasing granularity such that both spatial and temporal features of the data can be effectively extracted. Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.

The existing image feature extraction methods are primarily based on the content and structure information of images, and rarely consider the contextual semantic information. Regarding some types of images such as scenes and objects, the annotations and descriptions of them available on the web may provide reliable contextual semantic information for feature extraction. In this paper, we introduce novel semantic features of an image based on the annotations and descriptions of its similar images available on the web. Specifically, we propose a new method which consists of two consecutive steps to extract our semantic features. For each image in the training set, we initially search the top $k$ most similar images from the internet and extract their annotations/descriptions (e.g., tags or keywords). The annotation information is employed to design a filter bank for each image category and generate filter words (codebook). Finally, each image is represented by the histogram of the occurrences of filter words in all categories. We evaluate the performance of the proposed features in scene image classification on three commonly-used scene image datasets (i.e., MIT-67, Scene15 and Event8). Our method typically produces a lower feature dimension than existing feature extraction methods. Experimental results show that the proposed features generate better classification accuracies than vision based and tag based features, and comparable results to deep learning based features.

As AI algorithms play a bigger role in decision making, how do qualities like ethics, compassion, and inclusion get programmed into the code? On this edition of Bytemarks Café, a talk about the gathering of thought leaders in Hawai'i to discuss how to move the technology agenda. The event is called TechForce 2019, and its aim is to bring together leaders from key sectors to accelerate tech readiness in our islands. On this edition of Bytemarks Café, a discussion about a novel new project that projects a 3D hologram from Hawaii to American Samoa. The project is called Holo Campus, and is the delivery of University of Hawai'i lectures over the trans-Pacific fiber optic broadband network to the Pacific Islands.

It admittedly sounds a little like Big Brother, that a robot can tell significant things about your personality merely by looking into your eyes. Yet, that is the hiring territory that we are fast approaching – although we may not be sitting across from androids in interviews anytime soon. The use of artificial intelligence in making HR decisions is, while fraught with peril, not without its promising aspects. In an era when it is increasingly difficult for businesses to unearth the best job candidates, we may yet see the day when technology makes it possible to separate good from bad in the blink of an eye. Despite caveats about security and privacy, relying on AI would appear to be a method far superior to digging through a pile of resumes or asking ice-breaking questions like, "What's the last book you read?" Hiring good people – people who are talented, agreeable and work well with their co-workers – goes a long way toward nipping workplace conflicts in the bud.

G4 instances provide the industry's most cost-effective machine learning inference for applications, like adding metadata to an image, object detection, recommender systems, automated speech recognition, and language translation. G4 instances also provide a very cost-effective platform for building and running graphics-intensive applications, such as remote graphics workstations, video transcoding, photo-realistic design, and game streaming in the cloud. Machine learning involves two processes that require compute – training and inference. Training entails using labeled data to create a model that is capable of making predictions, a compute-intensive task that requires powerful processors and high-speed networking. Inference is the process of using a trained machine learning model to make predictions, which typically requires processing a lot of small compute jobs simultaneously, a task that can be most cost-effectively handled by accelerating computing with energy-efficient NVIDIA GPUs.

Finextra spoke to Amit Bhute, SVP & global head of the banking and financial services practice and Soumyendu Kishore Pal, co-head of the capital markets practice at Virtusa about what is driving digital transformation in financial services today. Bhute says that the rise of digitally native neobanks has showcased a significant gap in the market. "Banking legacy infrastructure that was built between the 1960s and 1980s is crumbling and is now unable to meet the demands of increasingly real-time and data-intensive customer demands. Banks have realised that sticking to the status quo is no longer enough and simply meeting regulatory demands will not support growth." Traditional banks must focus their digital transformation strategies on innovation, otherwise new revenue will captured by the digitally savvy neobanks.

Swiping is no longer the only way to find matches on Tinder. In a choose-your-own-adventure style series set to be rolled out next month, users will be able to match with other dating hopefuls by clicking their way through an interactive narrative. 'Swipe Night,' as Tinder is calling it, will air on October 6 and is designed to match users based on the choices they make during a short ''first-person apocalyptic adventure.' All of the episodes will be'live', so-to-speak, with each being available for viewing only between the hours of 6 pm and midnight during a respective users' local time. The series will consist of short five-minute videos during which users are periodically given seven seconds to choose what happens next.

Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, especially with a limited training dataset. In images, DA is usually based on heuristic transformations, like geometric or color transformations. Instead of using predefined transformations, our work learns data augmentation directly from the training data by learning to transform images with an encoder-decoder architecture combined with a spatial transformer network. The transformed images still belong to the same class but are new, more complex samples for the classifier. Our experiments show that our approach is better than previous generative data augmentation methods, and comparable to predefined transformation methods when training an image classifier.