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New South Wales

Australia's AI Action Plan – where does it take us? - Ethical AI Advisory


The one glaring gap in the Commonwealth government's AI strategy and action plan is a process to develop a coordinated governance framework around the development, use and procurement of AI services within commonwealth government agencies. This is where the NSW Government has taken a clear lead, setting out a mandatory customer service circular which all NSW Government agencies need to adhere to. There is practical guidance on adhering to principles, assessing risk, managing data, sourcing AI solutions, meeting legal obligations and more.

TfNSW kicks off new campaign to hire 'hundreds' of IT professionals


Transport for NSW (TfNSW) has announced plans to hire "hundreds" of local IT professionals across the state, with the government entity wanting work performed across "bots, apps, AI solutions, autonomous 3D mapping drones, and cybersecurity", as well as to help it transform the state's camera network. "We really encourage anyone with an interest in this field to throw their hat in the ring. There has been a 500% increase in training budgets for IT alone and at least 40% of IT jobs don't require a degree. This is about finding people from all walks of life that are eager to learn in the seat," TfNSW secretary Rob Sharp said. "This is a really exciting time to be working with Transport for NSW. At the moment we are just scratching the surface in how we are pioneering technology to deliver smart, innovative solutions that enable our people to make NSW a better place to live, work, and visit. Sharp said those with "a passion for technology" should consider working for the New South Wales government agency as it is "ready to help you develop the skills you need for a long and rewarding career in IT". According to TfNSW group chief information officer Richard Host, hiring has commenced on "hundreds" of new roles based in Sydney and regional NSW. "These roles will create opportunities and career pathways to break down barriers for people considering a career in IT," he added. "We are looking for people with passion for solving problems, working with people, and for technology.

Free Machine Learning Tutorial - New in Big Data: Apache HiveMall - Machine Learning with SQL


Elena works in the field of Natural Language Processing. She graduated with a degree from Saint-Petersburg State University in Russia first and then acquired PhD from Macquarie University in Sydney, Australia, where she works currently. Now she applies theoretical concepts developed in the field of Natural Language Processing to solve business problems of different big and small enterprises. As an early adopter of BigData tools and concepts she finds existing BigData frameworks to be attractive means of working with data. She started using such tools and advising other people to adopt BigData concepts way before Hadoop, Spark and other related technologies became "must to know" tools for many IT professionals.

NSW government dedicates AU$28m to assist with bushfire technology research


A total of AU$28 million over four years will be directed into research and development of new technologies and industries to help New South Wales tackle future bushfires. NSW Treasurer Dominic Perrottet said the funding would be evenly split into AU$7 million chunks under the NSW Bushfire Response R&D Mission. The New South Wales government made the announcement ahead of its 2021-22 Budget, which is set to be handed down next week. "The 2019-20 bushfires claimed lives, destroyed thousands of homes, and cost NSW billions, this investment will go towards reducing the impact of bushfires and responding in the most effective way possible," he said. "This focus on new technology to enhance planning, preparation, and response will save jobs when a disaster strikes and boost jobs in new industries."

NSW Police Introduce New Video Analysis Tools With Ethics At Their Core - Which-50


This week, the New South Wales Police announced the introduction of upgrades to their Insights policing platform. This new technology is designed to provide further services to frontline officers through faster access to critical information in the course of their roles in identifying persons and criminal activity across the state. Powered by Microsoft Azure cognitive technologies, the machine learning and deep learning capabilities were fully deployed in February 2021, with the goal of reducing police labour hours on manual data processing tasks, such as reviewing video feeds. Examples of how the AI systems will be used include one case were NSW Police collected 14,000 pieces of CCTV footage as part of a murder and assault investigation which would previously have taken detectives months to analyse. Microsoft claims the AI/ML infused Insights platform ingested this huge volume of information in five hours and prepared it for analysis by NSW Police Force investigators, a process which would otherwise have taken many weeks to months.

NSW Police using artificial intelligence to analyse CCTV footage


The New South Wales Police Force is in the process of bringing its back-end into the 21st century, turning to Microsoft and its Azure cloud platform for help. According to Microsoft, the force is retiring, re-architecting, or replacing over 200 legacy systems with cloud-based systems. Part of this transformation is changing the way the force analyses CCTV footage. Labelled as the "AI/ML-infused Insights policing platform", the system essentially speeds up the processing of data. In one example, NSW Police collected 14,000 pieces of CCTV as part of a murder and assault investigation and analysed it in a manner faster than it previously could.

Intelicare awarded $100K grant from NSSN to improve machine learning for assisted living – Software


InteliCare has been awarded a $100,000 grant from the New South Wales Smart Sensing Network ("NSSN") to develop its machine learning (ML) capability in conjunction with the University of Sydney (USyd) and Macquarie University (MU). The company is negotiating an agreement with USyd, MU and the NSSN to use these funds to help fund a one-year joint project delivered by the universities' Computer Science Departments. The goal is to build ML algorithms that can predict and prevent chronic disease and mental health deterioration that can lead to a loss of independence and an increased risk of injury. In addition to the NSSN funds, InteliCare will provide a co-contribution of $152,898 in cash and the universities will provide $161,021 of in-kind support. Ongoing development beyond the initial project will require the company to budget from working capital.

NSW to undergo trial of smart kerbsides


The New South Wales government has announced it will trial smart kerbsides as part of an initiative to make it easier for people to find parking spots. The trial, to be conducted in Liverpool, will run for 12 months and falls under the government's Smart Places Acceleration strategy that launched in August last year. Speaking at the state's first Parking Summit, Minister for Digital and Minister for Customer Service Victor Dominello and Minister for Transport and Roads Andrew Constance said the smart kerbside pilot would be able to show where street parking is available in real time. Dominello said the trial is aimed at lessening time spent on looking for parking and depending on the trial's success, data collected may be used to introduce other measures. "In busy city suburbs like Liverpool, kerbside parking is valuable real estate. We need to ensure we use it to its optimal level. We also need to guarantee drivers and small business are getting an equal share because -- it is so important to ensure our suburban economies get back up and running," he said.

River basins on the edge of change


![Figure][1] Most of Victoria and New South Wales in Australia experienced the Millennium Drought. River levels dropped, and reservoirs were at a fraction of their capacity. In 2010, the Lake Eildon reservoir in Victoria was at 29% capacity. PHOTO: ASHLEY COOPER/CONSTRUCTION PHOTOGRAPHY/AVALON/GETTY IMAGES Ecological systems can switch into qualitatively different states after small perturbations ([ 1 ][2]). Climate change and anthropic activities are dominant drivers of such ecological shifts, which affect the ability of these systems to recover from future disturbance ([ 2 ][3]). Such finite resilience in complex and dynamic natural systems has been predicted and documented ([ 3 ][4]). However, whether abrupt transitions have occurred or will occur in the water cycle is an unsolved problem ([ 4 ][5]). The possibility for river basins to achieve thresholds at which tiny perturbations alter their state and lead to chronic water scarcity or excessive water bears substantial implications for the sustainable use of water resources in extreme climate conditions ([ 5 ][6]). On page 745 of this issue, Peterson et al. ([ 6 ][7]) demonstrate that river systems exhibit a finite resilience to perturbations and that climate may indeed drive river basins between alternative states. To assess multistability in hydrological systems, Peterson et al. statistically investigated annual and seasonal precipitation and runoff records of more than 160 river basins in Victoria, Australia, before, during, and after the Millennium Drought (2001–2009), the worst drought ever recorded for southeast Australia ([ 7 ][8]). River basins are networks of channels and ridges that convey water to a common outlet. The studied river basins had neither major reservoirs nor water extractions, and runoff changes were not correlated to remotely sensed changes of Earth's surface cover. Seven years after the meteorological drought ended (that is, when dry weather ceased to dominate), more than a third of the river basins had not returned to previous runoff conditions, and most of them showed no signs of recovering soon. Conversely, basins that did shift back to a normal runoff state had shown warning signs in the 3 years before recovery. Unlike hydrological models that assume an infinite resilience of river systems, these findings imply that river basins do not always recover from droughts and that returning to predrought runoff conditions is not just a matter of time. Rather, the onset of positive feedbacks in hydrological systems, such as increased transpiration in nonrecovered basins, may amplify the impacts of climate change and, alarmingly, reduce the probability of switching back to a normal runoff state. Among basins switching back to predrought conditions, runoff recovery was not simply explained by increased soil moisture or groundwater recharge, as estimated by exploring basin wetness during shifts between multiple states. Hydrological recovery may instead have been driven by complex interactions of vegetation and soil hydraulics, which demands further research. Although climate shifts are predicted to alter water processes over widespread regions ([ 8 ][9]), the existence of multiple equilibrium points in the water cycle has focused on small subcontinental scales. Positive feedbacks between precipitation and soil moisture have suggested the emergence of preferential states in soil moisture dynamics and the persistence of droughts in the state of Illinois in the United States ([ 9 ][10]). Bistability was also demonstrated in coupled salt-vegetation dynamics at the basin scale, where gradual changes of salt concentration in irrigation water led to abrupt and irreversible changes in land productivity ([ 10 ][11]). Bistability was also identified in urban settings, where more frequent floods, droughts, population growth, and competition for resources may challenge the resilience of water supply systems and lead worldwide cities toward poverty ([ 11 ][12]). The conclusion of Peterson et al. —that river basins may irreversibly enter a persistent water scarcity state after severe meteorological droughts—challenges the comforting assumption that water systems naturally tend to absorb disturbances. This emphasizes the necessity to change the way global water processes are conceptualized. Assessing the existence of multiple equilibrium points in a natural water system and accounting for feedback mechanisms will likely help improve understanding of the hydrological response of river basins in drying regions and the development of appropriate water management adaptation strategies to climate change. However, the intrinsic heterogeneity of water systems and the stochastic nature of meteorological forcing (such as temperature, precipitation, and wind) raise questions about the possibility that all river basins have multiple equilibrium points ([ 12 ][13]). Further research is needed to identify the critical tipping points at which river basins switch to alternative states as well as the early warning signals of change in their resilience. This requires long-term monitoring of river basins that not only experience climate disturbances up and beyond critical thresholds but also do not undergo notable land-use change. Unfortunately, screening dynamic water processes is an actual challenge, and substantial limitations in current monitoring systems hamper systematic basin-scale hydrological investigations. Observations through standard equipment are still inadequate to fully grasp natural processes ([ 13 ][14]). They offer limited spatial coverage and generally involve high maintenance costs, which hinder implementation in many parts of the world, such as remote environments and developing countries ([ 14 ][15]). Although classical hydrological observations have been consistently decreasing worldwide since the 1980s, the recent use of innovative and unintended technology (such as low-cost electronics and participatory sensing) is providing opportunities for sensing the water cycle at even higher spatiotemporal resolutions ([ 15 ][16]). Dense and accurate monitoring of the hydrological response of and within river basins coupled with advanced data interpretation are necessary steps toward disentangling the complex interactions between basin morphological and functional attributes and hydroclimatic drivers in a changing world. 1. [↵][17]1. M. Scheffer, 2. S. Carpenter, 3. J. A. Foley, 4. C. Folke, 5. B. Walker , Nature 413, 591 (2001). [OpenUrl][18][CrossRef][19][PubMed][20][Web of Science][21] 2. [↵][22]1. B. Walker, 2. C. S. Holling, 3. S. R. Carpenter, 4. A. Kinzig , Ecol. Soc. 9, (2004). 3. [↵][23]1. R. M. May , Nature 269, 471 (1977). [OpenUrl][24][CrossRef][25][Web of Science][26] 4. [↵][27]1. G. Blöschl et al ., Hydrol. Sci. J. 64, 1141 (2019). [OpenUrl][28] 5. [↵][29]1. T. R. Ault , Science 368, 256 (2020). [OpenUrl][30][Abstract/FREE Full Text][31] 6. [↵][32]1. T. J. Peterson, 2. M. Saft, 3. M. C. Peel, 4. A. John , Science 372, 745 (2021). [OpenUrl][33][Abstract/FREE Full Text][34] 7. [↵][35]1. A. I. J. M. van Dijk et al ., Water Resour. Res. 49, 1040 (2013). [OpenUrl][36] 8. [↵][37]1. J. S. Caplan et al ., Sci. Adv. 5, eaau6635 (2019). [OpenUrl][38][FREE Full Text][39] 9. [↵][40]1. P. D'Odorico, 2. A. Porporato , Proc. Natl. Acad. Sci. U.S.A. 101, 8848 (2004). [OpenUrl][41][Abstract/FREE Full Text][42] 10. [↵][43]1. C. W. Runyan, 2. P. D'Odorico , Water Resour. Res. 46, W11561 (2010). [OpenUrl][44] 11. [↵][45]1. E. H. Krueger et al ., Earths Future 7, 1167 (2019). [OpenUrl][46] 12. [↵][47]1. T. J. Peterson, 2. A. W. Western , Water Resour. Res. 50, 2993 (2014). [OpenUrl][48] 13. [↵][49]1. A. K. Mishra, 2. P. Coulibaly , Rev. Geophys. 47, RG2001 (2009). [OpenUrl][50] 14. [↵][51]1. N. van de Giesen, 2. R. Hut, 3. J. Selker , Wiley Interdiscip. Rev. Water 1, 341 (2014). [OpenUrl][52] 15. [↵][53]1. F. Tauro et al ., Hydrol. Sci. J. 63, 169 (2018). 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How Transport for NSW is tapping machine learning


At the peak of the Covid-19 pandemic in 2020, Australian transport agency Transport for New South Wales (NSW) had to restore public confidence in the state's transportation network and curb the spread of the disease. One of the ways it did that was to analyse the travel history recorded by Opal transit cards – with an individual's permission – and inform the commuter if the regular buses and train services that they had been taking were Covid-safe. Chris Bennetts, executive director for digital product delivery at Transport for NSW, said those insights were derived using a machine learning model that predicts how full a bus or train carriage was going to be at a given time. Based on the predictions, commuters would be advised if they could continue using their regular services or switch to a different service or mode of transport. "That was interesting for us because it was our first foray into personalisation to offer more choices for customers," said Bennetts.