Text-to-speech technology has advanced greatly over the past two decades. Once defined by the robotic sounding voices that they produced, text-to-speech voices today can sound just as lifelike as an actual human. Today, making a natural sounding text-to-speech voice is labor intensive and expensive. The two most popular methods, HMM and USS, require hours of recordings from a voice actor. Then, computer programmers with an understanding of linguistics must break down all of that audio into the tiniest possible pieces, called phonemes, and appropriately tag them and define the rules for when each individual unit of speech should be used.

Smart manufacturing refers to using advanced data analytics to complement physical science for improving system performance and decision making. With the widespread deployment of sensors and Internet of Things, there is an increasing need of handling big manufacturing data characterized by high volume, high velocity, and high variety. Deep learning provides advanced analytics tools for processing and analysing big manufacturing data. This paper presents a comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing "smart". The evolvement of deep learning technologies and their advantages over traditional machine learning are firstly discussed.

Machine learning offers considerable potential for streamlining and transforming routine information processes in life sciences -- but could more creative applications of artificial intelligence take things further still? The scope for artificial intelligence in life sciences is potentially significant -- enabling accelerated scientific breakthrough, thanks to new potential to identify the minutest anomalies in unwieldy global data masses, and facilitating greater drug personalisation. Far from hovering on some futuristic horizon, the technology is already available too -- which means pharma companies need to build it into their agendas and plans now, if they don't want to risk sacrificing competitive advantage. From intelligent internet and content searches that adapt to user preferences, to automated personal assistants like Alexa and Siri, and customer care channels such as web chat, AI is already in common use as part of people's routine activities. One of the great appeals of AI is the scope for process automation and acceleration, using clever algorithms that can complete complex tasks previously limited to human capacity.

AI will change radiology, but it won't replace radiologists.

This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components (LECs) that accomplish tasks from classification to control. Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this article presents a survey of many of these recent approaches.

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.