Artificial intelligence (AI) technologies are forecast to add US$15 trillion to the global economy by 2030. According to the findings of our Index and as might be expected, the governments of countries in the Global North are better placed to take advantage of these gains than those in the Global South. There is a risk, therefore, that countries in the Global South could be left behind by the so-called fourth industrial revolution. Not only will they not reap the potential benefits of AI, but there is also the danger that unequal implementation widens global inequalities. AI has the power to transform the way that governments around the world deliver public services. In turn, this could greatly improve citizens' experiences of government. Governments are already implementing AI in their operations and service delivery, to improve efficiency, save time and money, and deliver better quality public services. In 2017, Oxford Insights created the world's first Government AI Readiness Index, to answer the question: how well placed are national governments to take advantage of the benefits of AI in their operations and delivery of public services? The results sought to capture the current capacity of governments to exploit the innovative potential of AI. The 2019 Government AI Readiness Index, produced with the support of the International Development Research Centre (IDRC), sees a development of our methodology, and an expansion of scope to cover all UN countries (from our previous group of OECD members). It scores the governments of 194 countries and territories according to their preparedness to use AI in the delivery of public services. The overall score is comprised of 11 input metrics, grouped under four high-level clusters: governance; infrastructure and data; skills and education; and government and public services. The data is derived from a variety of resources, ranging from our own desk research into AI strategies, to databases such as the number of registered AI startups on Crunchbase, to indices such as the UN eGovernment Development Index. We divided the countries by region, principally following UN groupings, with the chief exception of the Western European and Others Group, which we separated to allow more in-depth analysis of higher scoring governments.

CANBERRA – An Australian government minister on Wednesday expressed concern for three Australians arrested in Iran on suspicion of spying and separated their plight from a tense standoff in the Middle East over the weekend attack on Saudi Arabian oil facilities. Trade Minister Simon Birmingham was responding after Iran on Tuesday acknowledged for the first time that it is holding three Australian citizens, including two British dual nationals, on suspicion of espionage. "The government continues to seek information and clarity around these matters," Birmingham told Australian Broadcasting Corp. "We are concerned for the welfare of these individuals and work to make sure their treatment is as fair as possible." Iran confirmed the arrests of Melbourne University Middle East expert Kylie Moore-Gilbert in October and travel blogging couple Mark Firkin and Jolie King in July as fallout continues from Saturday's fiery missile and drone attack on the heart of Saudi Arabia's oil industry. Secretary of State Mike Pompeo was headed to Jiddah in Saudi Arabia on Tuesday to discuss possible responses to what U.S. officials believe was an attack coming from Iranian soil.

A lot of real-world applications could be modeled as the Mixed-Integer Non-Linear Programming (MINLP) problems, and some prominent examples include portfolio optimization, resource allocation, image classification, as well as path planning. Actually, most of the models for these applications are non-convex and always involve some conflicting objectives. Hence, the Multi-Objective Evolutionary Algorithm (MOEA), which does not require the gradient information and is efficient at dealing with the multi-objective optimization problems, is adopted frequently for these problems. In this work, we discuss the coding scheme for MOEA in MINLP, and the major discussion focuses on the constrained portfolio optimization problem, which is a classic financial problem and could be naturally modeled as MINLP. As a result, the challenge, faced by a direct coding scheme for MOEA in MINLP, is pointed out that the searching in multiple search spaces is very complicated. Thus, a Compressed Coding Scheme (CCS), which converts an MINLP problem into a continuous problem, is proposed to address this challenge. The analyses and experiments on 20 portfolio benchmark instances, of which the number of available assets ranging from 31 to 2235, consistently indicate that CCS is not only efficient but also robust for dealing with the constrained multi-objective portfolio optimization.

"I don't want you to be hopeful. I want you to panic. I want you to feel the fear I feel every day. And then I want you to act on changing the climate"– Greta Thunberg Greta Thunberg is a 16-year-old Swedish schoolgirl, who is famously called as a climate change warrior. She has started an international youth movement against climate change and has been nominated as a candidate for the Nobel Peace Prize 2019 for climate activism. According to a recent report by the Intergovernmental Panel (IPCC), climate change is seen as the top global threat by many countries.

Whether we know it or not, artificial intelligence (AI) is already steeped into everyday life. It's present in the way social media feeds are organised; the way predictive searches show up on Google; and how music services such as Spotify make song suggestions. The technology is also helping transform the way enterprises do business. Commonwealth Bank of Australia, for instance, has applied AI to analyse 200 billion data points to free up more time so its customer service officers can focus on doing exactly what their title suggests: servicing customers. As a result, the bank has seen a 400% uplift in customer engagement.

Telstra's independent venture capital arm has shown its intention to expand into the artificial intelligence data market following a $US100m (145m AUD) capital raising for San Francisco company Trifacta. Trifacta employs machine-learning technology to deduce a greater depth of insights from the increasing level of data migrating to cloud-based storage. Australia's largest venture capital fund, Telstra Ventures Fund No 2, led the investment, joined in the round by the likes of Energy Impact Partners, NTT Docomo, BMW Ventures and ABN AMRO. Telstra Venture joins a long and credible list of existing investors from Accel Partners, Greylock Partners, Ignition Partners and Google. "The share register for Trifacta is very impressive. It is great to have so many experienced and impressive co-investors in this deal. That is a really massive plus for us," Mr Koertge said.

A robust model for time series forecasting is highly important in many domains, including but not limited to financial forecast, air temperature and electricity consumption. To improve forecasting performance, traditional approaches usually require additional feature sets. However, adding more feature sets from different sources of data is not always feasible due to its accessibility limitation. In this paper, we propose a novel self-boosted mechanism in which the original time series is decomposed into multiple time series. These time series played the role of additional features in which the closely related time series group is used to feed into multi-task learning model, and the loosely related group is fed into multi-view learning part to utilize its complementary information. We use three real-world datasets to validate our model and show the superiority of our proposed method over existing state-of-the-art baseline methods.

Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in DNNs. We propose a backward elimination procedure called SurvNet, which is based on a new measure of variable importance that applies to a wide variety of networks. More importantly, SurvNet is able to estimate and control the false discovery rate of selected variables, while no existing methods provide such a quality control. Further, SurvNet adaptively determines how many variables to eliminate at each step in order to maximize the selection efficiency. To study its validity, SurvNet is applied to image data and gene expression data, as well as various simulation datasets.

In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and person-alisation of Interactive Narratives (INs). INs are partially observable for the players and their goals are dynamic as a result. We used Receding Horizon IRL (RHIRL) to learn players' goals in the form of reward functions, and derive policies to imitate their behaviour. Our preliminary results suggest that RHIRL is able to learn action sequences to complete a game, and provided insights towards generating behaviour more similar to specific players.

Vector is going to start using artificial intelligence to predict where storms will cause power outages. The Auckland lines company has partnered with IBM to pilot the new system, a first for the country, next month. It uses satellite imagery and artificial intelligence to show areas where trees might be encroaching on power lines. It can then suggest the locations most at risk of outages. Vector already uses artificial intelligence to manage electricity demand and network data across its network, since it partnered with Israeli technology company mPrest in 2017.