Australia’s fire season looms and researchers are looking to move beyond ‘detecting fires with people in towers and binoculars’.
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SYDNEY—In October, a sensor mounted on an 8.5-metre pole in the Australian countryside detected small particles in the air near a timber plantation and sent out an alert.
When plantation staff arrived, they found a small man-made fire that was already under control, but the incident offered a glimpse of how authorities hope to use new technology to battle bushfires that have grown increasingly intense in recent years.
As a new fire season begins in Australia—a recent bushfire ravaged about half of Fraser Island in the country’s east—researchers are looking to rely more on technology to find blazes quickly and better predict their path.
A monthslong government inquiry into the devastating 2019-2020 fire season in Australia, which killed more than two dozen people and burned an area bigger than Washington state, concluded that authorities need to be better prepared and recommended that officials work with the private sector to develop new technology.
“We’re still detecting fires with people in towers and binoculars. We’ve got to move beyond that,” said Leigh Kelson, program director at FireTech Connect in Australia, a government-funded effort to help startups develop new firefighting technologies. Firefighters also rely on the equivalent of 911 calls to find new blazes.
The ideas researchers are exploring include fitting drones with lasers that can map dry areas at higher fire risk and whether satellites can detect extreme fire behaviour.
Last fire season, authorities were surprised by how quickly the blazes spread and how long they burned, which they later attributed to a prolonged drought that had left the land parched with plenty of dry vegetation to fuel the flames. Last year was Australia’s hottest year on record, according to the government’s Bureau of Meteorology, and fires are projected to become more intense and frequent because of climate change.
Parts of Australia have received more rainfall in recent months, and the current fire season isn’t expected to be as severe. Still, firefighters have struggled to control some of the blazes. The fire on Fraser Island, the largest sand island in the world and a Unesco World Heritage site, prompted officials to tell some residents to evacuate from homes in the path of the flames. On Sunday, firefighters said rainfall had finally helped to contain that blaze, which started in mid-October.
Other regions, including the western U.S. and even in frosty Siberia, have experienced particularly intense fire seasons recently, stretching global firefighting resources and making early detection of fires more crucial. Fires left unchecked can create their own weather systems, raining embers down on nearby communities and pushing dangerous smoke into big cities and even to neighbouring countries.
The sensor that picked up the October fire near the timber plantation is one of more than 40 in a network that covers an area nearly double the size of New York City in Australia’s Victoria state. The solar-powered, cylinder-shaped sensors are packed with instruments including optical and thermal cameras, flame detectors, particle counters for air quality, and ground-vibration readers. The data are publicly available online in real-time.
Attentis Pty. Ltd., a Melbourne, Australia-based company, finished installing the network in early 2019 as a pilot project to demonstrate the technology. They didn’t get much use detecting fires last season because the blazes were too far away.
Cameron McKenna, managing director at the company, said he is in talks to install similar sensor networks to improve fire detection capabilities in other parts of the country. “We use multiple methods of detection as opposed to a single method,” he said.
In Canberra, Australia’s capital, researchers are working with a local firefighting agency to install video cameras on four fire towers. One tower will also have a thermal camera installed.
A computer program will scan images from the thermal camera to detect fires, and a person will monitor the other video feeds. Researchers plan to develop a computer program to automatically scan the video feeds too, said Marta Yebra, director of the Australian National University Bushfire Initiative. The idea is to determine whether the cameras detect new fires quicker than the old-fashioned way of having a person in the fire tower, she said.
Rohan Scott, who leads the rural fire service in the Canberra area, said people in towers on average spot fires within seven minutes of ignition. But the towers are only staffed on high-risk days.
“If we can get detection 24/7 but at the same speed as a human-manned fire tower, then I think that would be a good outcome,” he said. “Anything quicker than that would be a definite bonus.”
Other ideas involve using drones fitted with lasers to create detailed maps so authorities know which areas have more dry vegetation and present a higher fire risk. One recent effort involved researchers poring over satellite imagery in an attempt to build a model for detecting extreme fire behaviour from orbit. They found that changes in smoke colours could help predict fire behaviour, according to the Minderoo Foundation, a nonprofit that sponsored the research.
One company, Fireball.International Pty. Ltd., says its machine-learning method can detect smoke from ground-based cameras and fires from heat signatures on thermal-satellite images. Its system is already being used by a big power company in California, and Fireball plans to begin pilot programs in Australia this fire season, executives said.
A prototype of the system detected smoke from fires within 15 minutes of ignition, while averaging less than one false positive a day per camera, according to an analysis published in a peer-reviewed journal in January. It said there is room for improvement.
“People are beginning to realise, ‘Gosh, we really can detect a fire in the first five minutes,’” said Tim Ball, an academic and co-founder of the company who is also a former firefighter.
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AI doesn’t rebel—people design, deploy and profit from it. The real danger lies in allowing tech companies to escape accountability while shaping regulations that protect their dominance.
4 min
A wave of corporate warnings and technical disclosures has flooded the media, with headlines worrying over “swarms” of rogue artificial-intelligence agents launching “unprecedented” cyberattacks, outsmarting their makers, and inching toward a terrifying autonomy. The most revealing part of this narrative isn’t what the software did. It’s who is telling the story—and why. When corporate leaders publicly insist that the systems they financed, engineered and deployed are suddenly beyond their power to contain, skepticism isn’t only healthy; it is essential.
For years, Silicon Valley has drawn scrutiny from civil society and global regulators over tangible harms such as youth mental health deterioration and systematic privacy violations. Today, industry figures seem to be trying to change that public image. Loudly blowing the whistle on their own systems—just as two of the leading companies were preparing for massive initial public offerings—lets AI executives position themselves as a new generation of leaders who have come to terms with their societal responsibilities. They seem to want us to believe that they no longer want to “move fast and break things” but will instead stand as vigilant guardians between humanity and a technological apocalypse.
There is one glaring problem: Software doesn’t rebel. A mathematical model possesses neither intent, malice nor the will to defy its creators, let alone extinguish our species. AI is a human artifact, engineered for profit.
When an agentic model in an evaluation sandbox connects to an unauthorized server or executes an exploit, it hasn’t staged a coup. It has tried to meet the human-defined objectives set out before it through a path its designers failed to constrain. It’s the digital equivalent of the King Midas myth, in which the king’s ill-defined wish turns even his food and drink into gold.
That powerful experimental models were able to discover novel vulnerabilities and breach external systems isn’t a sign of a dangerous superintelligence but of human error or negligence. There is no sentient actor lurking in the weights to be reasoned with, feared or pacified. There are only human software engineers, product managers and corporate boards deciding which guardrails are worth the latency cost and which permissions can be skipped in the race to market.
Policymakers and voters need to resist AI exceptionalism. In any other discipline—from civil engineering to pharmaceuticals—courts and regulators treat a system failure as evidence of bad product design and inadequate safety testing. If an aircraft crashes, we focus on finding the engineering defect, correcting it, and enforcing established liability standards for the damage created.
By leaning on an anthropomorphic narrative, Silicon Valley attempts to repackage its specific human choices that led to experimental, powerful models behaving unexpectedly during tests as an existential peril. Elevating the issue to a cosmic scale leaves the public paralyzed and takes ordinary product accountability off the table.
In the cutthroat race for venture capital and market dominance, building guardrails slows down deployment. Grandstanding about uncontrollable power costs nothing and generates billions of dollars in free publicity, justifying stock prices, all while cultivating an aura of technological capability not only to build the frontier but also ultimately to rein it in.
Governments need to recognize regulatory capture when it stares them in the face. Tech leaders’ strategy looks transparent: Alarm Washington and Brussels into creating a regime in which only trillion-dollar incumbents with fully staffed compliance and safety departments can legally operate. By sitting at the policymakers’ tables before anyone else, these companies can help draft rules digging an impassable moat protecting them from open-source developers and upstart competitors, domestic or international. The real danger is in further concentrating the tech industry into the hands of only a few companies with deep pockets.
Beijing and Washington have brushed off those tech leaders’ calls, albeit for very different reasons. Chinese state media dismissed them as part of the “Cold War playbook” and intended to preserve U.S. dominance. Xi Jinping argued for exactly the opposite at the Brics Summit on Sept. 12, calling on Brics countries to “strengthen cooperation in the field of AI, encourage open source, openness, collaboration and sharing, and break new grounds and scale new heights.” President Trump, steeped in a doctrine of unfettered capitalism and technological supremacy, called fears that AI could destroy humanity a “hoax.” Vice President JD Vance warned that AI companies “begging the government to regulate them” looked like a “Trojan Horse.”
Striving to pursue its “European way” on AI and assert regulatory leadership, Europe, by contrast, welcomed the call. European Union President Ursula von der Leyen made this clear at the State of the EU speech last Wednesday and announced that the EU will invite “the main frontier labs for a discussion on how we can support ongoing industry efforts to pace the frontier.”
Europe has been here before. In an effort to lead global regulation and react to fears borne from ChatGPT, Europe rushed its landmark AI Act into law in 2024. Already the world’s most restrictive rulebook, the framework quickly proved too broad and complex to enforce. Stalled by implementation delays and concerns about European competitiveness, the EU postponed the law’s full rollout, leaving regulations uncertain.
AI should be regulated—risks exist and should be taken seriously. But governments need to act based on available evidence and verified facts, not corporate PR panic, the views of industry insiders, or the desire for quick political wins. The greatest danger facing society isn’t that software will awaken and overthrow its human masters. It is that we will allow the creators of the software to abdicate human responsibility for the systems they choose to build and help them pull up the ladder to market access behind them.
