RBA governor Michele Bullock speaks at the CEDA event on Tuesday. RBA governor Michele Bullock speaks at the CEDA event on Tuesday. RBA governor says AI is'great white hope' but its adoption is so far adding to inflation, rather than economic growth The Reserve Bank governor said AI could be a bubble and there was no evidence it is making the economy more efficient, as the Albanese government claims the technology will solve Australia's economic malaise. Ahead of an expected interest rate rise next week Michele Bullock also said the slump in house prices was deeper than others in Australia's recent history. Resurgent oil prices and inflation are weighing on markets but US tech stocks surged on Monday after Facebook owner Meta debuted its new Muse AI agent and saw its shares rise 11%.

Governance institutions must respond to societal risks, including those posed by generative AI. This study empirically examines how public trust in institutions and AI technologies, along with perceived risks, shape preferences for AI regulation. Using the nationally representative 2023 Artificial Intelligence, Morality, and Sentience (AIMS) survey, we assess trust in government, AI companies, and AI technologies, as well as public support for regulatory measures such as slowing AI development or outright bans on advanced AI. Our findings reveal broad public support for AI regulation, with risk perception playing a significant role in shaping policy preferences. Individuals with higher trust in government favor regulation, while those with greater trust in AI companies and AI technologies are less inclined to support restrictions. Trust in government and perceived risks significantly predict preferences for both soft (e.g., slowing development) and strong (e.g., banning AI systems) regulatory interventions. These results highlight the importance of public opinion in AI governance. As AI capabilities advance, effective regulation will require balancing public concerns about risks with trust in institutions. This study provides a foundational empirical baseline for policymakers navigating AI governance and underscores the need for further research into public trust, risk perception, and regulatory strategies in the evolving AI landscape.

An existing model of opinion dynamics on an adaptive social network is extended to introduce update policy heterogeneity, representing the fact that individual differences between social animals can affect their tendency to form, and be influenced by, their social bonds with other animals. As in the original model, the opinions and social connections of a population of model agents change due to three social processes: conformity, homophily and neophily. Here, however, we explore the case in which each node's susceptibility to these three processes is parameterised by node-specific values drawn independently at random from some distribution. This introduction of heterogeneity increases both the degree of extremism and connectedness in the final population (relative to comparable homogeneous networks) and leads to significant assortativity with respect to node update policy parameters as well as node opinions. Each node's update policy parameters also predict properties of the community that they will belong to in the final network configuration. These results suggest that update policy heterogeneity in social populations may have a significant impact on the formation of extremist communities in real-world populations.

The use of technology to improve human life and activity has long been implemented. Nowadays, we see technological innovations beyond what our predecessors could have ever imagined. People use to travel by foot or riding an animal of some sort. Then comes the invention of carriages with (again) animals to pull it. Many years later, we now have cars–which is basically an automated carriage if you think about it–, trains, ships, planes, and all other sorts of vehicle I haven't mentioned. This is only on transportation technology.

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

CMOs had better get accustomed to implementing artificial intelligence (AI) or machine learning into their marketing toolkits. According to the CMO Survey (PDF) released earlier this year, CMOs said their marketing team's AI implementations were likely to almost triple over the next three years. Currently, CMOs averaged a 1.93 when asked to rate their AI implementations into marketing toolkits (1 Not at all; 7 very likely). But in the next three years they see those implementations at a 3.48. CMOs will have to cope with AI's impact on content generation, technology advances, regulations, misuses, challenges, effective use cases, and so on, according to Scott Liewehr, CEO of Digital Clarity Group.

It may not be common knowledge, but the automotive industry is in deep discussions to find out how aeronautic technology can benefit the next generation of road vehicles. More specifically, satellite imaging firms are using their expertise to assist the creation of high-definition (HD) maps, which can optimise autonomous vehicle (AV) navigation, ride-share operations and last-mile delivery services. Maxar is based in Westminster, Colorado. From here, it runs a global business you've never heard of, but will almost certainly have used. Its constellation of satellites circles the earth once every 90 minutes--that's 16 revolutions per day--on what is called a sun-synchronous orbit.

Handing off product packaging to an automated system is a relief, but returning from a meeting to find a treadmill on the fritz and product sitting still -- all while customers call in, clamoring for their shipments -- is not. That's the kind of problem with which businesses are wrestling as they find ways to pass off their repetitive tasks or "chores" to machines, allowing the humans to focus instead on the more nuanced responsibilities. When implementing time-saving machines, what's a business to do to ensure equipment failure doesn't turn automation into only frustration? Intelligence of Things Tracker charts the latest news on how companies are increasingly turning to IoT to predict when and how their equipment is going to break -- and to let them swoop in to the rescue before that expensive oil drill or package sorting machine stutters to a halt, snarling operations and revenue in the blink of an eye. From smart homes and smart cars to intelligent industrial machines that can have their ailments remotely diagnosed, IoT is a rapidly growing field.

Where is the next big marketing disruption coming from? I caught up with a host of digital experts at the CMO Digital Insight Summit to find out. Tucked away on Scotland's wild and rugged west coast, Turnberry has a special place in golfing folklore. Famous for the legendary'Duel in the Sun' between Tom Watson and Jack Nicklaus at the 1977 Open Championship – an epic display of both sport and sportsmanship – the resort has seen its fair share of intense competition over the years. And in many ways, that made it the perfect setting for the recent CMO Digital Insight Summit.