Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark that are covering all main existing hardware configurations.

I recently had the opportunity to participate in a fireside chat session at Forrester's New Tech & Innovation 2018 forum with J.P. Gownder, a vice president and principal analyst at the firm. It was a timely and much-needed discussion on some of the biggest questions today in artificial intelligence (AI) and I hope that the audience walked away with a better understanding of this pivotal and complex technology. For those that were unable to attend the event, this blog post will provide an overview of several of the questions and answers that we looked at in the session. At Petuum, we often talk about the "industrialization" of AI, but this term is likely unfamiliar to most people. I started the session by exploring what we mean when we talk about AI being industrialized in the same way as textiles, electronics, and other products.

It's the beginning of September (and no, I don't know how that happened either), and as the summer lull winds to a close and we prepare for a renewed frenzy of activity, it's a good moment to take stock of the year so far. Last month, two different articles were published looking back over some of the key trends we've seen in 2018 with regard to emerging technology and digital transformation, and comparing them to what was predicted. One was a piece by Forbes contributor Daniel Newman of CMO Network, who revisited his predictions for digital transformation in 2018 in light of the past eight months, to see where we are with some of 2018's most potentially transformative technologies. It takes a broadly optimistic view of the technologies that are meant to be shaking up the digital world in 2018, with a few caveats. The other was by The Register's Andrew Orlowski, written in response to the publication of Gartner's annual'Emerging Technologies Hype Cycle', which revealed that a number of the most "hyped" technologies from last year have vanished from this year's chart.

Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations to be applied directly on encrypted data without requiring a secret key. This enables novel application scenarios where a client can safely offload storage and computation to a third-party cloud provider without having to trust the software and the hardware vendors with the decryption keys. Recent advances in both FHE schemes and implementations have moved such applications from theoretical possibilities into the realm of practicalities. This paper proposes a compact and well-reasoned interface called the Homomorphic Instruction Set Architecture (HISA) for developing FHE applications. Just as the hardware ISA interface enabled hardware advances to proceed independent of software advances in the compiler and language runtimes, HISA decouples compiler optimizations and runtimes for supporting FHE applications from advancements in the underlying FHE schemes. This paper demonstrates the capabilities of HISA by building an end-to-end software stack for evaluating neural network models on encrypted data. Our stack includes an end-to-end compiler, runtime, and a set of optimizations. Our approach shows generated code, on a set of popular neural network architectures, is faster than hand-optimized implementations.

Cutting edge technology and innovation will be adopted as the official show theme for Arabian Travel Market (ATM) 2019, taking place at Dubai World Trade Centre from 28 April – 1 May 2019. According to the latest research conducted by Colliers International, personalisation Artificial Intelligence (AI) could increase hotel revenues by over 10 percent and reduce costs by more than 15 percent – with hotel operators expecting technology such as voice and facial recognition, virtual reality and biometrics to be mainstream by 2025. Further to this, the research estimates 73 percent of manual activities in the hospitality industry have the technical potential for automation, with many global hotel operators including Marriott, Hilton, and Accor already investing in automating elements of their human resources. Danielle Curtis, Exhibition Director ME, Arabian Travel Market, said: "It is important to highlight that the GCC is one of the fastest growing regional hospitality markets on a global scale and an innovative technology-reliant industry. "Its impact on hotels and travel and tourism is multi-dimensional, ranging from voice and facial recognition, chatbots and beacon technology to virtual reality, blockchain and robot concierge.

A house capable of taking on its own life is one way to visualize the concept of smart homes. This living home would tend to your needs and preferences by computation of complex algorithms. An automated house has wide ranging implications. Whether it's aiding in extraneous chores or figuring out solutions for home-security, smart homes offer owners the ability to focus on other high-priority life tasks with less concerns. A smart home that is driven by artificial intelligence continues to push technological boundaries -- here is ONE Tech's look at the 3 biggest elements of successful AI-integrated homes.

Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network. Recently, the research community has showed an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, we provide a detailed overview of the recent efforts to include AI in SDN. Our study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

We develop a Vector Quantized Spectral Clustering (VQSC) algorithm that is a combination of Spectral Clustering (SC) and Vector Quantization (VQ) sampling for grouping Soybean genomes. The inspiration here is to use SC for its accuracy and VQ to make the algorithm computationally cheap (the complexity of SC is cubic in-terms of the input size). Although the combination of SC and VQ is not new, the novelty of our work is in developing the crucial similarity matrix in SC as well as use of k-medoids in VQ, both adapted for the Soybean genome data. We compare our approach with commonly used techniques like UPGMA (Un-weighted Pair Graph Method with Arithmetic Mean) and NJ (Neighbour Joining). Experimental results show that our approach outperforms both these techniques significantly in terms of cluster quality (up to 25% better cluster quality) and time complexity (order of magnitude faster).

This paper surveys recent theoretical advances in convex optimization approaches for community detection. We introduce some important theoretical techniques and results for establishing the consistency of convex community detection under various statistical models. In particular, we discuss the basic techniques based on the primal and dual analysis. We also present results that demonstrate several distinctive advantages of convex community detection, including robustness against outlier nodes, consistency under weak assortativity, and adaptivity to heterogeneous degrees. This survey is not intended to be a complete overview of the vast literature on this fast-growing topic. Instead, we aim to provide a big picture of the remarkable recent development in this area and to make the survey accessible to a broad audience. We hope that this expository article can serve as an introductory guide for readers who are interested in using, designing, and analyzing convex relaxation methods in network analysis.

Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass human-level performance on a number of tasks. As a consequence, deep learning is being extensively used in most of the recent day-to-day applications. However, security of deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can lead the model to misclassify the output. In recent times, different types of adversaries based on their threat model leverage these vulnerabilities to compromise a deep learning system where adversaries have high incentives. Hence, it is extremely important to provide robustness to deep learning algorithms against these adversaries. However, there are only a few strong countermeasures which can be used in all types of attack scenarios to design a robust deep learning system. In this paper, we attempt to provide a detailed discussion on different types of adversarial attacks with various threat models and also elaborate the efficiency and challenges of recent countermeasures against them.