This speech was given by Dr Larry Marshall at the AFR Innovation Summit on Monday 30 July 2018. I would like to begin by acknowledging the Gadigal people of the Eora nation as the Traditional Owners of the land that we are on today, and pay my respect to their Elders past and present. It's great to be back at the AFR Innovation Summit, and thank you to our morning speakers for setting a strong focus on the power of innovation to shape our future. Before I was the Chief Executive of Australia's national science agency, I was an inventor, an entrepreneur, a venture capitalist and of course a kid. I grew up in a time where people were obsessed with the power of the computer and its ability to replace humans, our parents told us to study computer programing.

In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable. Topic modeling is an effective technique for summarizing a collection of documents to find the main themes among them and to classify other documents that have a similar mixture of co-occurring words. We show how grounding a topic model with an ontology, extracted from a glossary of important domain phrases, improves the topics generated and makes them easier to understand. We apply and evaluate this method to the climate science domain. The result improves the topics generated and supports faster research understanding, discovery of social networks among researchers, and automatic ontology generation.

Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications. However, many confusing manmade objects and intricate fine-structured objects make it very difficult to obtain both coherent and accurate labeling results. For this challenging task, we propose a novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet). Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation. Technically, multi-scale contexts are captured on the output of a CNN encoder, and then they are successively aggregated in a self-cascaded manner. Meanwhile, for fine-structured objects, ScasNet boosts the labeling accuracy with a coarse-to-fine refinement strategy. It progressively refines the target objects using the low-level features learned by CNN's shallow layers. In addition, to correct the latent fitting residual caused by multi-feature fusion inside ScasNet, a dedicated residual correction scheme is proposed. It greatly improves the effectiveness of ScasNet. Extensive experimental results on three public datasets, including two challenging benchmarks, show that ScasNet achieves the state-of-the-art performance.

IBM (NYSE: IBM) has announced that several new European clients have selected IBM Watson Internet of Things (IoT) technologies, the company said. New contracts signed with Spanish electricity grid operator Red Elà ctrica de Espaà a, Italian elderly care provider Cooperativa Sole, Dutch telecommunications operator Tele2 and Israeli manufacturer of smart air conditioning Electra Group are examples of IBM s commitment to transforming business and improving operations with the power of Artificial Intelligence (AI)-enabled, IBM Cloud-based Internet of Things (IoT) technologies. Red Elà ctrica de Espaà a (http://www.ree.es/en), the sole transmission agent and operator of the national electricity system in Spain has selected IBM Watson IoT technologies as part of its Intelligent Asset Management initiative project. Israel s manufacturer and distributor of consumer goods, is collaborating with IBM to create smart air conditioning solutions, which incorporate Watson IoT technology.

China is planning to upgrade its naval power with unmanned AI submarines that aim to provide an edge over the fleets of their global counterparts. A report by the South China Post on Sunday revealed Beijing's plans to build the automated subs by the early 2020s in response to unmanned weapons being developed in the US. The subs will be able to patrol areas in the South China Sea and Pacific Ocean that are home to disputed military bases. While the expected cost of the submarines has not been disclosed, they're likely to be cheaper than conventional submarines as they do not require life-supporting apparatus for humans. However, without a human crew, they'll also need to be resilient enough to be at sea without onboard repairs possible.

When artificial intelligence is brought up in conversation, the classic idea of a robot versus a human emerges – somewhat of an us-versus-them mentality – but artificial intelligence works at its best when it – machine learning, natural language processing, and robotics – is viewed as a partnership with the human workforce. Enter augmented intelligence, which sits at the nexus between artificial intelligence and humans, and revolves around technology helping people to complete their work more efficiently and allowing them to focus more on high-value "human-only" type activities. Today's utilities are faced with multiple market disruptions including the proliferation of distributed energy sources, evolving regulatory and policy changes, the increased adoption of energy efficiency products and programs, changing consumer behaviors, and an imperative to modernize their technologies and processes. Faced with these disruptions, utility executives can leverage innovative approaches such as augmented intelligence to position themselves for success. Utilities make investments in new equipment by upgrading existing assets, such as transformers and substations, and performing preventative maintenance -- all with the goal of improving reliability of service.

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.

Events If you're wondering how your organisation can cut through the hype and actually benefit from AI and machine learning, you should join us at MCubed in London this October. And by grabbing one of our earlybird tickets before they expire next week, you'll be boosting your bottom line as well as your brain. Our conference sessions range from the fundamentals of machine learning and AI, through the frameworks and tools you need to use, and how they're being applied in business as well as academic settings. We'll be looking at how you can use the technology to make sense of user generated content, tackle engineering problems, or predict currency rates, through to how machine learning is shaping the smart grid, or helping underwater drones map the ocean floor. And we'll be taking you through the logistics of developing and deploying projects in the real world, whether your team comes from a traditional developer or data science background.

The state of the art practice in robotics planning is to script behaviors manually, where each behavior is typically generated using trajectory optimization. However, in order for robots to be able to act robustly and adapt to novel situations, they need to plan these activity sequences autonomously. Since the conditions and effects of these behaviors are tightly coupled through time, state and control variables, many problems require that the tasks of activity planning and trajectory optimization are considered together. There are two key issues underlying effective hybrid activity and trajectory planning: the sufficiently accurate modeling of robot dynamics and the capability of planning over long horizons. Hybrid activity and trajectory planners that employ mixed integer programming within a discrete time formulation are able to accurately model complex dynamics for robot vehicles, but are often restricted to relatively short horizons. On the other hand, current hybrid activity planners that employ continuous time formulations can handle longer horizons but they only allow actions to have continuous effects with constant rate of change, and restrict the allowed state constraints to linear inequalities. This is insufficient for many robotic applications and it greatly limits the expressivity of the problems that these approaches can solve. In this work we present the ScottyActivity planner, that is able to generate practical hybrid activity and motion plans over long horizons by employing recent methods in convex optimization combined with methods for planning with relaxed plan graphs and heuristic forward search. Unlike other continuous time planners, ScottyActivity can solve a broad class of robotic planning problems by supporting convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. In order to support planning over long horizons, ScottyActivity does not resort to time, state or control variable discretization. While straightforward formulations of consistency checks are not convex and do not scale, we present an efficient convex formulation, in the form of a Second Order Cone Program (SOCP), that is very fast to solve. We also introduce several new realistic domains that demonstrate the capabilities and scalability of our approach, and their simplified linear versions, that we use to compare with other state of the art planners. This work demonstrates the power of integrating advanced convex optimization techniques with discrete search methods and paves the way for extensions dealing with non-convex disjoint constraints, such as obstacle avoidance.

Recently, it was proposed to employ approximate quantum adders to implement quantum autoencoders in quantum technologies. Here, we carry out the experimental implementation of this proposal in the Rigetti cloud quantum computer employing up to three qubits. The experimental fidelities are in good agreement with the theoretical prediction, thus proving the feasibility to realize quantum autoencoders via quantum adders in state-of-the-art superconducting quantum technologies. A quantum autoencoder is a quantum device which can reshuffle and compress the quantum information of a subset of a Hilbert space spanned by initial n -qubit states onto n ′ qubit states with n ′ n [1, 2]. This approach may allow one to employ fewer quantum computing resources [3, 4]. On the other hand, recently it was proven that a general quantum adder performing the equal weight superposition of two unknown quantum states is forbidden in general [5].