Image by Annie Spratt, from Unsplash
Australia Drops Misinformation Bill Amid Senate Opposition
- Written by Kiara Fabbri Former Tech News Writer
- Fact-Checked by Sarah Frazier Former Content Manager
Australia’s Misinformation Bill was abandoned after Senate opposition and criticism over free speech. Michelle Rowland accused the Coalition of prioritizing partisanship above any attempt to navigate the public interest.
In a Rush? Here are the Quick Facts!
- The Bill aimed to tackle harmful online misinformation and ensure digital platform accountability.
- Critics claimed the Bill failed to adequately protect freedom of expression.
- The government plans alternative measures, including AI regulation and deep fake legislation.
The Australian government has abandoned its proposed Communications Legislation Amendment (Combatting Misinformation and Disinformation) Bill 2024, citing insufficient support in the Senate, as reported on a statement by communications minister Michelle Rowland.
The legislation aimed to address the dangers posed by harmful online misinformation and disinformation.
The Bill sought to hold digital platforms accountable by introducing enforceable measures to minimize harmful content, enhance transparency, and empower users. It included mechanisms to tackle the spread of false information through algorithms, bots, fake accounts, and deep fakes, while protecting freedom of speech.
In her statement, Rowland says that despite support from crossbench MPs and constructive collaboration on refining the Bill, the government could not secure Senate approval.
An initial version of the legislation was revised to gain broader support, but the second draft also failed to secure parliamentary approval or address critics’ concerns, as reported by The Guardian .
The Coalition maintained its commitment to opposing the bill, while members of the Senate crossbench recently indicated they would either vote against it or were not persuaded enough to support it, said The Guardian.
In October, the Australian Human Rights Commission stated that “although there have been improvements to the bill, freedom of expression is not sufficiently protected,” as reported by The Guardian.
In her statement, Rowland accuses critics of the bill to have prioritized political partisanship over public interest, despite the Coalition’s earlier commitment to legislate similar safeguards while in government.
In response to the setback, the government has proposed alternative measures. These include legislation against non-consensual deep fakes, a truth-in-political-advertising framework for elections, and reforms to regulate artificial intelligence.
The opposition spokesperson, David Coleman, criticized the bill, stating it “betrayed our democracy” and represented “censorship laws in Australia,” as reported on ABC News .
“This bill would have had the effect of suppressing the free speech of everyday Australians, as platforms would have censored online content to avoid the threat of big fines,” Coleman said in a statement, as reported by ABC News.
Image by Mike Benna, from Unsplash
AI Powers Urban Forest Models
- Written by Kiara Fabbri Former Tech News Writer
- Fact-Checked by Sarah Frazier Former Content Manager
Tree-D Fusion, an AI system, creates 3D models of urban trees, predicting growth, environmental impacts, and improving urban forestry management.
In a Rush? Here are the Quick Facts!
- Tree-D Fusion creates 3D models of urban trees from single images.
- The system uses AI and procedural modeling to simulate tree growth accurately.
- Models help predict tree growth, environmental impacts, and urban forestry challenges.
Researchers from MIT, Google, and Purdue University have introduced “ Tree-D Fusion ,” an AI-driven system that creates detailed 3D models of urban trees using single images. The results were delineated in a recent paper .
By combining artificial intelligence with decades of forestry science, the system models trees’ structures and growth patterns to provide insights into urban forestry planning.
The project includes a large-scale database of 600,000 simulation-ready tree models across North America, intended for applications such as predicting tree growth and its impact on urban environments, as reported in an MIT press release .
The paper explains that the system relies on a hybrid approach to model trees. Deep learning algorithms first create a structural envelope representing a tree’s overall shape. Then, traditional procedural models refine this envelope by simulating realistic branch and leaf patterns based on the tree’s genus.
This combination enables Tree-D Fusion to predict how trees may grow under different environmental conditions, including variations in temperature and groundwater availability.
Unlike previous models, it captures typically hidden features, such as the backside of trees not visible in street-view images, and accounts for the dynamic nature of trees as they interact with their environment, says the MIT press release.
“We’re bridging decades of forestry science with modern AI capabilities,” explained Sara Beery, assistant professor at MIT and principal investigator at MIT CSAIL, as reported by MIT.
“This allows us to not just identify trees in cities but to predict how they’ll grow and impact their surroundings over time. We’re using AI to make existing forestry knowledge applicable across broader urban settings.”
MIT explains that Tree-D Fusion represents an advancement over earlier urban forest monitoring efforts, which often relied on neighborhood-level observations or struggled with scaling.
The system uses image data from tools like Google Street View and integrates it into predictive models capable of estimating future growth and identifying potential risks, such as branches interfering with power lines.
Despite its advancements, the system faces challenges, particularly with overlapping or “entangled” trees where branches from neighboring trees grow into each other.
“What makes this work exciting is how it pushes us to rethink fundamental assumptions in computer vision,” said Beery as reported by MIT. Trees’ dynamic and constantly changing forms require new approaches, unlike static objects like buildings.
The researchers are already exploring how Tree-D Fusion can be scaled globally, with potential applications for urban forestry and biodiversity monitoring.
“Our goal is to use AI-driven insights to support natural ecosystems, promote sustainability, and improve urban planning,” said Jae Joong Lee, a Purdue University PhD student who developed the Tree-D Fusion algorithm, as reported by MIT.
