The scope and pace of technological innovation is ascendant, advancing at a pace that is impossible to keep up with. Much ado about artificial intelligence, blockchain, the internet of things, virtual reality, … the list of emerging technologies continues to grow. But for the buzz, all the investment, and all the 2017 roundups and 2018 predictions, we often fail to remember technology's secret. The most powerful disruptions rarely occur from single technologies but well-timed convergence of multiple existing technologies to foster something altogether unprecedented. In my analysis of the impacts of emerging technology, I identify how and where people, organizations, and ecosystems are being transformed by technological convergence.

Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually presupposes existence of a detailed dynamics model for the hot water system. These assumptions lead to suboptimal energy efficiency in the real world. In this paper, we present a novel reinforcement learning based methodology which optimizes hot water production. The proposed methodology is completely generalizable, and does not require an offline step or human domain knowledge to build a model for the hot water vessel or the heating element. Occupant preferences too are learnt on the fly. The proposed system is applied to a set of 32 houses in the Netherlands where it reduces energy consumption for hot water production by roughly 20% with no loss of occupant comfort. Extrapolating, this translates to absolute savings of roughly 200 kWh for a single household on an annual basis. This performance can be replicated to any domestic hot water system and optimization objective, given that the fairly minimal requirements on sensor data are met. With millions of hot water systems operational worldwide, the proposed framework has the potential to reduce energy consumption in existing and new systems on a multi Gigawatt-hour scale in the years to come.

In this work we introduce PHOENICS, a probabilistic global optimization algorithm combining ideas from Bayesian optimization with concepts from Bayesian kernel density estimation. We propose an inexpensive acquisition function balancing the explorative and exploitative behavior of the algorithm. This acquisition function enables intuitive sampling strategies for an efficient parallel search of global minima. The performance of PHOENICS is assessed via an exhaustive benchmark study on a set of 15 discrete, quasi-discrete and continuous multidimensional functions. Unlike optimization methods based on Gaussian processes (GP) and random forests (RF), we show that PHOENICS is less sensitive to the nature of the co-domain, and outperforms GP and RF optimizations. We illustrate the performance of PHOENICS on the Oregonator, a difficult case-study describing a complex chemical reaction network. We demonstrate that only PHOENICS was able to reproduce qualitatively and quantitatively the target dynamic behavior of this nonlinear reaction dynamics. We recommend PHOENICS for rapid optimization of scalar, possibly non-convex, black-box unknown objective functions.

Abstract--We consider the problem of tracking an intruder using a network of wireless sensors. For tracking the intruder at each instant, the optimal number and the right configuration of sensors has to be powered. As powering the sensors consumes energy, there is a trade off between accurately tracking the position of the intruder at each instant and the energy consumption of sensors. This problem has been formulated in the framework of Partially Observable Markov Decision Process (POMDP) [1]. Even for the simplest model considered in [1], the curse of dimensionality renders the problem intractable. We formulate this problem with a suitable state-action space in the framework of POMDP and develop a reinforcement learning algorithm utilizing the Upper Confidence Tree Search (UCT) method to mitigate the state-action space explosion. Through simulations, we illustrate that our algorithm yields good performance and scales well with the increasing state and action space. I. INTRODUCTION The problem of detecting an intruder (Intrusion Detection (ID) problem) using a network of sensors arises in various applications like tracking the movement of wild animals in the forest, house/shop surveillance for safety and security and so on. In this problem, the objective of the ID system is to track one or more intruders moving in the field of a wireless sensor network (WSN). Typically, WSNs operate on limited power supply.

Most work in automatic programming has focused primarily on the roles of deduction and programming knowledge However, the role played by knowledge of the task domain seems to be at least as important, both for the usability of an automatic programming system and for the feasibility of building one which works on nontrivial problems This perspective has evolved during the course of a variety of studies over the last several years, including detailed examination of existing software for a particular domain (quantitative interpretation of oil well logs) and the implementation of an experimental automatic programming system for that domain The importance of domain knowledge has two important implications: a primary goal of automatic programming research should be to characterize the programming process for specific domains; and a crucial issue to be addressed in these characterizations is the interaction of domain and programming knowledge during program synthesis Used by permission of the International Joint Conferences on Artificial Intelligence; copies of the Proceedings are available from William Kaufmann, Inc, 95 First St., Los Altos, CA 94022 USA. For example, the work of Green (1969) and Waldinger and Lee (1969) in the late 1960s was concerned with the use of a theorem-prover to produce programs. This deductive paradigm continues to be the basis for much research in automatic programming (e.g., Manna & Waldinger 1980, Smith 1983). In the mid 1970's, work on the PSI project (Barstow 1979, Green 1977, Kant 1981) and on the Programmer's Apprentice (Rich 1981) was fundamentally concerned with the codification of knowledge about programming techniques and the use of that knowledge in program synthesis and analysis Work within the knowledge-based paradigm is also continuing (e.g., Barstow 1982, Waters 1981). This article is concerned with the role played by knowledge of the task domain, a role which seems to be at least as important.

As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb? I would take the fame, I suppose, but perhaps the critics are right.

BiographyBhoopathi Rapolu (@bhoopathi) is Head of Analytics, EMEA, at Cyient. He is responsible for technology solutions development, customer engagement, and business development. He has 16 years of experience in business intelligence and technology management. He has conceptualised and delivered technology solutions powered by advanced analytics, primarily for Utilities, Telecom, Aerospace, Transportation, Heavy Engineering and Medical Technologies. Bhoopathi is a speaker, author and blogger on business & emerging technologies.Abstract Automated Areal Asset InspectionsFacebook recognizes over a billion people on this planet. Now, imagine such artificial intelligence (AI) examining the asset condition from drone videos, satellite imagery, etc., and spotting the defects, if any, automatically.

This article is part of Inc42's Startup Watchlist annual series where we list the top startups to watch for 2018 from industries like AI, IoT, Blockchain etc. Explore all the stories from'Startup Watchlist' series here. Once used as a tool for an application, Internet of Things (IoT) has become one of the widest ecosystems today. Currently at the centre stage of industries like energy management, healthcare, logistics, fintech, manufacturing and agritech, IoT, in convergence with AI, has the potential to disrupt all these verticals. Previously dictated by big players like IBM, Google, Intel, Cisco, Ericsson, Apple and Amazon, the IoT space has now become a startup ecosystem enabler across the world. While it was the Internet that drove the emergence of ecommerce startups in the early 2000s, IoT has been facilitating the growth of this decade's tech startups. What lightning does to mushrooms, IoT has done to startups!

There is a critically important dialogue going on across the extended global automotive industry about the future evolution of transportation and mobility. This debate is driven by the convergence of a series of industry-changing forces and mega-trends (see figure 1). Innovative technologies are changing how companies develop and build vehicles. Electric and fuel-cell powertrains tend to offer greater propulsion for lower energy investment at lower emission levels.1 New, lightweight materials enable automakers to reduce vehicle weight without sacrificing passenger safety.2 Further breakthroughs are advancing the introduction of autonomous vehicles; increasingly, daily news reports suggest that driverless cars will soon become a commercial reality.3 We have already seen rapid advances in the "connected car--?--innovations that integrate communications technologies and the Internet of Things to provide valuable services to drivers.4

Summary: Digital Twins is a concept based in IoT but requiring the skills of machine learning and potentially AI. It's not completely new but it is integral to Gartner's vision of the digital enterprise and makes the Hype Cycle for 2017. It's a major enabler of event processing as opposed to traditional request processing. If the concept of Digital Twins is new to you, you need to be looking way over to the left on Gartner's 2017 Hype Cycles of Emerging Technologies. There between Quantum Computing and Serverless PaaS you'll find Digital Twins with a time to acceptance of 5 to 10 years, or more specifically that by 2021, one-half of companies will be using Digital Twins.