Digital innovations to create smarter cities and technology to use and protect customer identities will be at the forefront of the 2021 Digital.NSW Showcase on Tuesday and Wednesday. NSW Government Chief Information and Digital Officer Greg Wells said the Digital.NSW Showcase would highlight the latest innovations used by NSW Government agencies including the Department of Customer Service (DCS). "The NSW Government is a world leader in delivering customer-centric digital outcomes, whether it is providing clear guidance on the safe use of Artificial Intelligence (AI) with the AI Strategy or investing $2.1 billion into digital transformation projects as part of the Digital Restart Fund," Mr Wells said. "During COVID-19 we were the first state to introduce a digital COVID-Safe check-in system which made it easier for people to safely visit venues and access essential services while supporting the efforts of NSW Health contract tracers. "We were also one of the first states to successfully integrate the digital COVID-19 vaccination certificate into the Service NSW app and to link it to the check-in system, allowing customers to provide vaccination status quickly and efficiently." The latest version of the of the Beyond Digital Strategy will also be released in conjunction with the showcase. Beyond Digital sets the strategic direction and objectives for NSW to continue its work in becoming a customer-centric, digitally enabled government. DCS's latest initiative, Digital Identity and Credentials, will also be in focus at the showcase, with the team behind the critical project demonstrating how customers will soon have more choice and convenience in the way they safely and securely prove their identity and share their information. Executive Director of Information, Communications and Technology and Digital Sourcing Mark Lenzner said the Digital.NSW Showcase would focus on four core themes including digital, data, ICT infrastructure and Smart Places, each exploring a different way technology can deliver more efficient and accessible services for customers "Digital products such as DCS's integration of the digital vaccination certificate, Dine & Discover vouchers and Digital Licensing play a critical role across NSW government by making sure customers can easily access and use important services, even when faced with a pandemic," Mr Lenzner said. "Presentations under the Smart Places stream will explore how the NSW Government can create a better quality of life for people through technology.

In modeling multivariate time series for either forecast or policy analysis, it would be beneficial to have figured out the cause-effect relations within the data. Regression analysis, however, is generally for correlation relation, and very few researches have focused on variance analysis for causality discovery. We first set up an equilibrium for the cause-effect relations using a fictitious vector autoregressive model. In the equilibrium, long-run relations are identified from noise, and spurious ones are negligibly close to zero. The solution, called causality distribution, measures the relative strength causing the movement of all series or specific affected ones. If a group of exogenous data affects the others but not vice versa, then, in theory, the causality distribution for other variables is necessarily zero. The hypothesis test of zero causality is the rule to decide a variable is endogenous or not. Our new approach has high accuracy in identifying the true cause-effect relations among the data in the simulation studies. We also apply the approach to estimating the causal factors' contribution to climate change.

Onboard autonomy technologies such as planning and scheduling, identification of scientific targets, and content-based data summarization, will lead to exciting new space science missions. However, the challenge of operating missions with such onboard autonomous capabilities has not been studied to a level of detail sufficient for consideration in mission concepts. These autonomy capabilities will require changes to current operations processes, practices, and tools. We have developed a case study to assess the changes needed to enable operators and scientists to operate an autonomous spacecraft by facilitating a common model between the ground personnel and the onboard algorithms. We assess the new operations tools and workflows necessary to enable operators and scientists to convey their desired intent to the spacecraft, and to be able to reconstruct and explain the decisions made onboard and the state of the spacecraft. Mock-ups of these tools were used in a user study to understand the effectiveness of the processes and tools in enabling a shared framework of understanding, and in the ability of the operators and scientists to effectively achieve mission science objectives.

The North Carolina Agriculture and Technical State University (NC A&T) in collaboration with Georgia Tech Research Institute (GTRI) has developed methodologies for creating simulation-based technology tools that are capable of inferring the perceptions and behavioral states of autonomous systems. These methodologies have the potential to provide the Test and Evaluation (T&E) community at the Department of Defense (DoD) with a greater insight into the internal processes of these systems. The methodologies use only external observations and do not require complete knowledge of the internal processing of and/or any modifications to the system under test. This paper presents an example of one such simulation-based technology tool, named as the Data-Driven Intelligent Prediction Tool (DIPT). DIPT was developed for testing a multi-platform Unmanned Aerial Vehicle (UAV) system capable of conducting collaborative search missions. DIPT's Graphical User Interface (GUI) enables the testers to view the aircraft's current operating state, predicts its current target-detection status, and provides reasoning for exhibiting a particular behavior along with an explanation of assigning a particular task to it.

New York City could soon reduce the chances of AI bias in the job market. The Associated Press notes the city's council has passed a bill barring AI hiring systems that don't pass yearly audits checking for race- or gender-based discrimination. Developers would also need greater transparency (including disclosures of automated systems), and provide alternatives like human reviews. Fines would reach up to $1,500 per incident. The bill was passed November 10th.

Federal Trade Commission chair Lina Khan is hiring three artificial intelligence researchers from New York University to advise on emerging technology issues. Khan announced Friday that NYU's Meredith Whittaker, Amba Kak, and Sarah Myers West are joining an AI strategy group within the FTC's Office of Policy Planning. They join Olivier Sylvain, a law professor from Fordham University, who is serving as Khan's senior adviser on technology. The new hires likely signal an increased regulatory emphasis on AI technologies like facial recognition, which is also an area of expertise for Alvaro Bedoya, who's been nominated as an FTC commissioner.

One of the best ways to understand whales is to listen to them. A new artificial intelligence (AI) program named INSTINCT is helping scientists study whales by learning their calls. The Alaska Fisheries Science Center Marine Mammal Laboratory developed Infrastructure for Noise and Soundscape Tolerant Investigation of Nonspecific Call Types, or INSTINCT. It was developed to detect and identify whale calls from underwater acoustic recordings. Automating this analysis means data critical for whale conservation gets to managers years--sometimes decades--faster.

Despite the hope that artificial intelligence (AI) might revolutionise the future of work, the general consensus so far seems to be that it has negative implications on the workforce. The TUC warned in March that workers could be "hired and fired by algorithm", while a recent Harvard Business School study revealed that the majority (88 per cent) of employers believe qualified applicants are filtered out by the screening software. Campaigners and policy makers are also questioning the impact that AI is having on the quality of work – whether it be algorithms that decide how much work app-based couriers receive, or automated performance monitoring pushing warehouse staff to forego toilet breaks in order to meet packing targets. The issue was summarised by a report from the Institute for the Future of Work, which said: "We find that it is not the replacement of humans by machines but the treatment of humans as machines" that defined the current era of work. Last week, a group of MPs decried the "growing body of evidence" pointing towards a "significant negative impact on the conditions and quality of work across the country" caused by the use of algorithms in the workplace.

Despite widespread popularity, today's microwave ovens are limited in their cooking capabilities, given that they heat food blindly, resulting in a nonuniform and unpredictable heating distribution. We present software-defined cooking (SDC), a low-cost closed-loop microwave oven system that aims to heat food in a software-defined thermal trajectory. SDC achieves this through a novel high-resolution heat sensing and actuation system that uses microwave-safe components to augment existing microwaves. SDC first senses the thermal gradient by using arrays of neon lamps that are charged by the electromagnetic (EM) field a microwave produces. SDC then modifies the EM-field strength to desired levels by accurately moving food on a programmable turntable toward sensed hot and cold spots. To create a more skewed arbitrary thermal pattern, SDC further introduces two types of programmable accessories: A microwave shield and a susceptor. We design and implement one experimental test bed by modifying a commercial off-the-shelf microwave oven. Our evaluation shows that SDC can programmatically create temperature deltas at a resolution of 21 C with a spatial resolution of 3 cm without the programmable accessories, and 183 C with them. We further demonstrate how an SDC-enabled microwave can be enlisted to perform unexpected cooking tasks: Cooking meat and fat in bacon discriminatively and heating milk uniformly. Since the introduction of microwaves to the consumer market in the 1970s, they have seen widespread adoption and are today the third most popular domestic food heating method (after baking and grilling).13 Indeed, the original patents for the microwave by Raytheon Inc. in the late 1940s envisioned a universal food cooking instrument for all kinds of food ranging from meat to fish.1 While microwaves have revolutionized the kitchen since their inception, today's consumer microwaves are mainly used as blunt heating appliances (e.g., reheating pizzas) rather than precise cooking instruments (e.g., cooking steak).

Liu Leqi is a Ph.D. student in the Machine Learning Department at Carnegie Mellon University, Pittsburgh, PA, USA. Her research interests include AI and human-centered problems in machine learning. Dylan Hadfield-Menell is an assistant professor of artificial intelligence and decision-making at the Massachusetts Institute of Technology, Cambridge, MA, USA. His recent work focuses on the risks of (over-) optimizing proxy metrics in AI systems. Zachary C. Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University, Pittsburgh, PA, USA, and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research spans core machine learning methods, applications to clinical medicine and NLP, and the impact of automation on social systems. He can be found on Twitter (@zacharylipton), GitHub (@zackchase), or his lab's website (acmilab.org).