It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
FILE PHOTO: U.S. Treasury Secretary Scott Bessent speaks to reporters at the White House in Washington, D.C., October 22, 2025. REUTERS/Kevin Lamarque/File Photo
Treasury Secretary Scott Bessent told Fox News host Sean Hannity on Monday night that he agrees with Elon Musk's prediction that artificial intelligence will produce so much abundance that Americans won't need to save for retirement.
The exchange, flagged online by journalist Aaron Rupar, came when Hannity asked Bessent whether he believed Musk's claim that AI-driven wealth would make traditional retirement savings unnecessary.
"I do, but I think I would change the timeline," Bessent said. "I've seen Elon speak for a long time and he is — look, his record speaks for itself in terms, as an inventor, as a venture capitalist, as a great American. He's normally early. He's just so far ahead of the curve. He sees things that no one else sees. But I do think we are building this incredible economy that we can't even imagine."
Bessent added that a quarter of jobs that exist in 2026 didn't exist in 2000.
Musk laid out the underlying vision earlier this year on the Moonshots with Peter Diamandis podcast, calling AI and robotics a "supersonic tsunami" that would bring about a world of zero scarcity.
"Don't worry about squirreling money away for retirement in 10 or 20 years," Musk, the world's richest man, told Diamandis. "It won't matter."
Musk predicted that by 2030, AI will exceed "the intelligence of all humans combined," and that humanoid robots will eventually outnumber people.
Bessent's embrace of that forecast landed at a pivotal moment for millions of American retirees.
The 2026 Social Security Trustees Report, released in June, projects that the retirement program's primary trust fund will be depleted in 2032, triggering an automatic 22 percent benefit cut for current and future beneficiaries unless Congress acts.
The Committee for a Responsible Federal Budget estimated that an immediate 24 percent cut on insolvency would translate to an average monthly reduction of about $500 for retirees, affecting roughly 63 million people.
The trustees also noted the outlook had worsened partly because of the 2025 "One Big Beautiful Bill Act," which reduced tax revenue flowing into the trust fund.
Musk's original comments were widely panned by financial planners. Business Insider surveyed seven personal finance and AI experts about his advice, all of whom urged Americans to keep saving.
“This is just like a free-for-all. There’s really no oversight. There’s really no regulation.” (Erin Brockovich, Brockovich AI Data Center Reporting)
Erin Brockovich is a well-known American consumer advocate who made her name by exposing evidence that Pacific Gas & Electric poisoned groundwater in Hinkley, California (1996) with toxic hexavalent. Now, she’s investigating AI datacenters.
AI date centers are everywhere, in every state throughout the land. They are starting to quasi-own American citizens via appropriation of resources, both human and natural. This is an invasion of the land, the water, the power, the intellectual resources of America.
Erin Brockovich was recently interviewed about her commitment to investigating AI development by Katie Couric, appearing on YouTube.
Erin: “I’ve never seen in 30 years anything like this… This is every town in every state, and all our resources at once happening.”
The common thread in submissions from individuals in communities across the land: “it feels to them like a takeover.” First, there is a lack of transparency. Local city councils, where “the people” should be represented are hijacked via non-disclosure agreements signed by local officials. This is step number one in gaining approval to build where city regulations and city code control the land. The locals are left out of decisions.
Datacenters Consume Natural Resources
Smaller data centers use about 500,000 gallons of water per day. The average family of four uses ~350 gallons of water per day. Therefore, a newly constructed small data center is equivalent to moving 1,428 new families into a neighborhood.
The bigger data centers, which are in vogue, being built right up next to hospitals, schools, and residences willy nilly use anywhere from 1-to-7 million gallons of water per day. On average, equivalent to moving 15,000-20,000 new families into a neighborhood.
“So, if we build 1,200 data centers… that’s the projection for the total and you just take an average of those between 500,000 to seven million, and the average is 5 million gallons per day, and multiple that by 365 days. You’re looking at numbers of 2.2 to 2.7 trillion gallons of water per year… that are on already drought restricted lands.” (Erin)
(There are already 4, 542 active data centers, 1,500 more are in the planning stage.)
A recent study by The Guardian found that 2/3rds of the planned data centers in the U.S. are in drought-stricken areas.
Datacenters disrupt the regular flow of water. For example, residents sent reports to Erin “of water bills going from $40/month to $250/month… and we’re seeing the exact same thing with electrical.”
According to Katie Couric, Sam Altman, CEO of Open AI, dismisses concerns about AI water use, “fake, completely untrue, totally insane, and having no connection to reality.”
In response, Erin wondered if Altman lives in the areas and experiences what residents report to her. He is implying that the reports by residents are false (16,000 total submissions to Erin by individuals involving various AI issues). “It is insulting.”
Erin asked AI itself in a query to the faceless genius a question of placement of data centers assuming the entire network were to start all over again from scratch, redesigned. AI responded that it would place new data centers based upon one simple rule: “Zero conflict with human resources.” Hmm.
“The people” who submit to Erin from all 50 states are saying the opposite of Sam Altman. And she’s going with the people as a more credible source. After all, they live in the immediate area of data centers. He does not.
Data Center Noise – Wow!
The “hum,” the “buzz” can extend a couple of miles away from a data center. The endless humming is a 24/7 annoyance that’s driving some people nuts. Residents are informing Erin of dizziness, nausea, headachy experiences. This is a common issue tbat’s reported from every one of the facility areas in the country.
Locals Fighting Back
Three hundred (300) municipalities have established moratoriums on data center development, some for one year, some for two years, some six months, some are flat-out bans.
But according to Erin, as usual, “many of them won’t be able to withstand the pressure by huge corporations that have got all the money in the world.”
Yet, public protests can exercise powerful influence. For example, in Utah it was clearly via public opposition that the governor had to scale back the size of the facility coming in.
And New York is the first state to ban development of new data centers as a moratorium for one year and direct state regulators to create standards focused on environmental impacts, energy demand, water usage and other factors: “As datacenter development threatens to hike up utility bills, deplete our natural resources, and create uncertainty for New Yorkers, it’s my responsibility to take action and lead,’ Hochul, a Democrat, said in a statement.” (New York Becomes First State to Impose One-Year Pause on New AI Datacenters, The Guardian, July 14, 2026)
According to Katie, a recent Gallup poll shows 7 out of 10 Americans oppose data centers being built near them, making it one of the few contemporary issues that has broad bipartisan agreement, republicans and democrats alike do not favor AI datacenters in their locales.
Meanwhile, the power source for data centers is passed off to consumers. Infrastructure is required. Erin has received submissions from people stating their utility bills normally $75 went up to $425. A recent submission by a resident out of Texas, which has the most data centers at 417, said their city notified residents to expect a 75% electrical bill increase.
Stated by Katie: According to The New York Times, PJM, the nation’s largest electrical grid operator’s results of an electricity auction will add $6.3 billion costs to consumers within the next 3 years, an increase driven by the power demands of data centers.
AI Ownership
Katie Couric’s interview of Erin Brockovich brought to light the impact AI has on the country’s natural resources but did not discuss nontangible resources like music or speech or art. These human resources, similar to natural resources, are public resources. Who owns these resources?
Bernie Sanders has an interesting take on ownership of resources: “The foundation of AI is based on our collective human intelligence. Our books, songs, artwork, journalism, computer code, scientific research, videos, conversations, images, and ideas span generations. The reality is that Big Tech oligarchs have fed this knowledge into their AI models without permission, without acknowledgement, and without compensation. The creative work of many millions of people – writers, artists, musicians, journalists, teachers, scientists and ordinary people- has been stolen by the wealthiest people in the world. In the coming weeks I will be introducing the American AI Sovereign Wealth Fund Act. This legislation would give the public a direct ownership stake in the largest AI companies in our country. Through a one-time 50% tax, not on the profit of the largest AI companies in the US but something that is far more valuable than that: The Stock. Under this bill the federal government would have the power through its voting shares and an equal representation on each company’s board to block decisions that hurt the public and to push for policies that help them. When a public resource generates wealth, the public should share in that wealth.”
The AI Race With China Is a Lie Told by Big Tech to Justify the Data Center Invasion
The truth is, we don’t need hyperscale data centers to “beat China.” We aren’t racing China. We’re killing ourselves so Silicon Valley can race itself.
Rural Michigan residents rally against the $7 billion Stargate data center planned on southeast Michigan farm land in Saline, Michigan on December 1, 2025. (Photo by Jim West/UCG/Universal Images Group via Getty Images)
As Big Tech races to build water-guzzling, energy-hungry data centers for its artificial intelligence, talk of an “AI race” between the United States and China has permeated public discourse. Pundits, politicians, and the media have all joined tech corporations in selling this narrative. And it’s giving license to Big Tech and their political handmaidens to ruin our communities, exploit our every action (both online and via AI-powered surveillance), and steal the wealth of human knowledge for private gain.
But the idea of an AI race between China and the US isn’t grounded in reality. The researchers, companies, and governments behind Chinese and US AI development are pursuing completely different goals.
The discourse in the US assumes that achieving artificial general intelligence (AGI)—computers that mimic human consciousness—would be so momentous and earth-shattering that clearly this must be the goal of anyone pursuing AI development. But that’s not the main goal of Chinese AI development. And a competition in which the competitors are running toward different finish lines isn’t a race.
If we allow the myth of an AI race with China to give Big Tech free rein, we face a more polluted, less equal world. A Race Doesn’t Have Two Different Finish Lines
Yes, China is developing LLMs, although largely in an open-source way as opposed to the for-profit competition in the US. Recent news stories report that China is “catching” the US in LLM development. Indeed, the latest Chinese model outperforms leading US models. But this isn’t evidence of an LLM-AGI race. Instead, it shows that without making AGI its main focus, China is capable of developing its own models almost as quickly as US companies.
If every environmental review, every question raised by a community, every issue around water usage and electricity prices can be dismissed or lessened as “helping China,” then meaningful political debate can be silenced.
More to the point, LLM development in China is incidental to the country’s real goal for AI. Its focus remains on products embedded with AI and robots. Or, as AI policy researcher Liang Zheng says, in China, “The first priority is to use it to benefit ordinary people” (debatable, but indicates the kind of AI they are pursuing).
In the US, the first priority is to exploit people so that the tech oligarchs can profit. It’s chatbots all the way down.
This isn’t to argue that China is doing it “right” and the US is doing it “wrong.” Either approach will lead to a future in which citizens become increasingly disempowered. In which work becomes more scarce and less lucrative for most people. And in which a handful of billionaires grow wealthier and more powerful.
But the arguments being hauled out to support the destructive growth of hyperscale data centers are based on a fallacy. There is no need to “beat China.” China and the US are racing on separate tracks, in different races, with different finish lines.
These two separate approaches also explain the mind-boggling scale of the data center invasion we currently face. The massive hyperscale data centers—recent proposals would demand up to 5 gigawatts, enough electricity to power roughly 3.75 million US households—are only “required” because the US is racing toward AGI. Meanwhile, the embodied AI dominating in China does not require the same amount of computational power.
The truth is, we don’t need hyperscale data centers to “beat China.” We aren’t racing China. We’re killing ourselves so Silicon Valley can race itself. How Big Tech Is Weaponizing the “AI Race” to Build Data Centers
Yet, the “AI race” story is a convenient lie for Big Tech and its political protectors. It is a neat political argument designed to insulate the industry from criticism and regulation.
If every environmental review, every question raised by a community, every issue around water usage and electricity prices can be dismissed or lessened as “helping China,” then meaningful political debate can be silenced. Real regulation—if even possible—can be avoided. Fear becomes a substitute for policy.
We understand why O’Leary and the other Tech Broligarchs don’t understand the grassroots opposition to data centers building across the country. It’s hard to spot the grassroots from the window of a private jet.
At the same time, data center developers and their minions in Washington have tried to weaponize false claims about foreign ties to the anti-data center movement. Kevin O’Leary of Shark Tank fame has explicitly said that our movement is being funded by China. He has no evidence because evidence of a falsehood can’t exist. In fact, Fox News has been forced to retract its coverage of his claims.
We understand why O’Leary and the other Tech Broligarchs don’t understand the grassroots opposition to data centers building across the country. It’s hard to spot the grassroots from the window of a private jet. But the opposition is real and organic, and no amount of disinformation and pushing the “AI race against China” scare tactic will derail the movement. Big Tech Is Selling Us a Dystopian Future
The myth of an AI race with China threatens to propel us into Big Tech’s vision of the future—one that’s more unequal than ever. Yes, tech leaders suggest their algorithms will cure cancer, but their real goal is and has been to increase their power and wealth at the expense of the rest of us.
Even as American Tech Broligarchs have distanced themselves from earlier prophecies of widespread job loss, their vision of the future will see the vast majority of us out of meaningful work. We’ll be subject to living off whatever meager handouts are created in an attempt to mollify us.
Even if there were an AI race, is it worth running, let alone winning, if the prize is a dystopian future of mass misery with a thin layer of super wealthy tech oligarchs at the top?
At the same time, our movement will be traced whether or not we use the electronic gadgets they sell us. Already, surveillance devices linked to AI can recognize our faces, record our license plates, and report our movements. Companies and governments can buy this data in order to track us.
Meanwhile, “surveillance pricing” allows companies to change prices in an instant so that they can exploit our needs for their profit. Deepfake videos have already added to the rapidly decaying trust in a commonly shared world and a basic set of facts necessary for a functioning democracy. And this is but a scratch of the surface.
Even if there were an AI race, is it worth running, let alone winning, if the prize is a dystopian future of mass misery with a thin layer of super wealthy tech oligarchs at the top? Big Tech’s Dystopian Vision Isn’t Inevitable
But we should be clear. This dystopian vision is not the inevitable outcome of unstoppable technological “progress” as the Tech Broligarchs would have us believe. Each and every decision being made to advance AI is a political decision. And, for now, we still have the ability to determine our political future.
Across the country, there is a growing resistance to the nightmare being shoved down our throats. Communities are coming together to fight the spread of destructive hyperscale data centers. Already in 2026, more than $130 billion-worth of proposed data center projects have been defeated and canceled.
That’s the real “AI Race.” Not China versus the US, but us versus Big Tech.
Communities are taking control of their futures by placing moratoriums on new data centers. New York enacted a one-year pause on new centers, and there is growing support for a nationwide pause in Congress.
We aren’t destined to live in Elon Musk’s fever dream. We have the power to stop him and his fellow Broligarchs. When we organize, we win. That’s the real “AI Race.” Not China versus the US, but us versus Big Tech. That’s not only a race worth running—it’s one we have to win.
As artificial intelligence becomes central to scientific discovery, researchers face a growing but often overlooked risk: the AI models, datasets, and automated systems they depend on can be compromised in ways that conventional cybersecurity tools are not designed to detect.
A new project called VERITAS (VERified Infrastructure for Trustworthy AI in Science), led by principal investigator Anita Nikolich, research scientist and director of research and technology innovation at the University of Illinois School of Information Sciences, will address this gap by establishing AI Assurance as a core function of scientific research infrastructure. Funded through a three-year, $896,000 grant from the National Science Foundation's Cybersecurity Innovation for Cyberinfrastructure program, VERITAS brings together experts in adversarial AI, research cyberinfrastructure, data science, and workforce development. The project aims to develop practical methods for documenting, reviewing, and stress-testing AI systems before they are used in high-impact scientific workflows.
A blind spot in how science secures AI
Traditional cybersecurity focuses on preventing unauthorized access, catching malware, and stopping data theft. AI-enabled research introduces additional risks that may not trigger conventional security alerts.
A poisoned dataset, for example, may appear statistically normal while causing a model to produce unreliable results. A backdoored model downloaded from a public repository may contain no recognizable malware and may operate normally until a particular input activates its hidden behavior. An autonomous AI agent may have excessive permissions that allow it to alter data, invoke laboratory tools, or manipulate a research workflow.
In each case, the infrastructure may appear secure while the scientific result is compromised.
"We cannot simply bolt traditional cybersecurity onto AI-driven science," said Nikolich. "When a poisoned dataset or backdoored model produces an answer that looks plausible but is subtly wrong, no firewall or virus scanner is likely to catch it. The researchers doing our most important scientific work deserve assurance that the AI systems they rely on are documented, tested, and behaving as intended."
According to Nikolich, rather than requiring scientists to become cybersecurity experts or expecting cybersecurity teams to become machine-learning specialists, VERITAS will integrate AI Assurance into the research infrastructure scientists already use. The project has three connected components:
Model and data documentation. VERITAS will pilot standardized model cards and dataset datasheets for large scientific computing allocations. Similar to nutrition labels on packaged food, these documents describe where a model or dataset came from, how it was created or modified, its intended use, its known limitations, and the assumptions researchers should understand before reusing it. The goal is to improve transparency, reproducibility, and the ability to trace problems through complex AI workflows.
Operational AI security services. VERITAS will pilot a new AI Assurance Engineer role at the National Center for Supercomputing Applications (NCSA). The engineer will review selected technically novel AI projects before deployment, scan model files for unsafe or malicious behavior, examine software for vulnerabilities, and assess the risks around uses of autonomous agents.
Model and data integrity challenges. Through the National Data Platform (NDP) Education Hub, VERITAS will create hands-on challenges that train students to detect poisoned data, inspect potentially compromised models, evaluate agent permissions, and identify weaknesses in scientific AI workflows.
Finding vulnerabilities before they become scientific failures
AI red-teaming—deliberately attacking an AI system to find its weaknesses before an adversary does—is now a well-established field. It has rarely been brought into scientific research, where a manipulated model produces a false result that can pass for legitimate science. VERITAS is among the first efforts to adapt the practice to scientific cyberinfrastructure.
"AI systems can fail in ways that are difficult to distinguish from legitimate scientific results," said Nikolich. "Proactive red teaming allows us to identify those weaknesses before a vulnerable model or agent becomes embedded in a research pipeline. The objective is to help research teams make their systems more trustworthy and resilient."
Building the AI Assurance workforce
VERITAS will also help prepare students for careers at the intersection of machine learning, cybersecurity, and scientific computing. Participants in the project's challenges will work with realistic scientific models, datasets, and infrastructure using NDP while learning about responsible disclosure practices.
By embedding documentation, security review, adversarial assessment, and workforce development into existing scientific cyberinfrastructure, VERITAS seeks to create a model for AI Assurance that can be adopted by supercomputing centers, research institutions, and national-scale AI infrastructure providers.
"AI is now part of the scientific workflow," Nikolich said. "We need to protect its integrity just as seriously as we protect the networks and computing systems around it."
AI model advances scientific discovery with soil carbon research
ITHACA, N.Y. – A new computer model from Cornell University researchers is one of the first artificial intelligence tools to advance scientific discovery in agriculture and biogeochemistry and is 50 times more efficient than its predecessors.
In a paper published in the journal Geoscientific Model Development, the researchers demonstrated the AI on processes behind the important issue of soil organic carbon, as the Earth’s soils hold roughly three-quarters of the world’s terrestrial carbon and more carbon than the atmosphere and all the world’s plants combined.
Scientists have been exploring ways to use AI for research purposes, but most common AI tools, such as ChatGPT, mainly repurpose existing information. Researchers have also used AI to extract patterns from data. But the new model, called the Biogeochemistry-Informed Neural Network (BINN) goes a step further by predicting biological processes that are not yet well understood and suggesting factors that control them.
“BINN is very easy to use and can be democratized among the scientific community in various disciplines,” said Yiqi Luo, the senior author of the study. “This is one of the first tools of this type that can promote scientific research with AI.”
Soil scientists know the mechanisms by which soils acquire organic carbon – plants extract and sequester carbon from carbon dioxide to grow, and when those plants die, organic matter from stems, leaves and roots decompose into smaller and smaller bits to become part of the earth. But what is not well known are the speed of these processes and how many such processes are required to break down the litter.
“We use AI and data to tell us quantitatively how fast and how many of these kinds of processes are required,” said Haodi Xu, a doctoral student in Luo’s lab, and co-first author of the study.
When compared to previous models, BINN computed 50 times faster. The accuracy of predictions of quantities of soil organic carbon was found to be very similar to the previous models. But previous models contained spatial biases, meaning that when making predictions across the contiguous U.S., it might favor the data from one area versus another. The researchers found less spatial bias with BINN.
MINNEAPOLIS / ST. PAUL (07/27/2026) - University of Minnesota Twin Cities researchers have developed a first-of-its-kind AI system that allows underwater companion robots to monitor a diver’s health in real-time, simply by "watching" their exhaled bubbles.
Published in The International Journal of Robotics Research, the paper marks the first time robotic vision has been used to estimate a diver’s Human Respiration Rate (HRR).
Scuba diving, particularly in extreme environments, is inherently risky and places humans under intense physical stress — ranging from exhaustion to life-threatening respiratory distress. By tracking the frequency and volume of bubbles exhaled from a diver’s regulator, camera-equipped robots can now detect signs of stress, hyperventilation or exhaustion in real-time.
This non-contact approach solves a long-standing challenge where traditional medical sensors and wearables often fail underwater because thick wetsuits or drysuits block the contact needed for accurate readings. Wireless data transmission through water is also severely limited.
“Our goal was to give divers a dedicated robotic safety partner to provide a second set of ‘eyes’ capable of reading physiological stress underwater,” said Junaed Sattar, Associate Professor in the Department of Computer Science and Engineering and senior author on the paper. “This work is a first step towards assessing not just one but a group of divers in the robot's field of view.”
To train the AI model, the research team developed a "fuzzy labeling" system. Because underwater footage can be murky, they manually categorized thousands of images while using synchronized audio cues made up of the distinct sound of regulator exhalations to teach the robot exactly what a breath looks like.
“While monitoring breathing is a standard vital sign on land, doing so underwater presents immense technical challenges,” said Demetrious Kutzke, a Ph.D. student in the Robotics & Vision Laboratory at the University of Minnesota and the study’s lead author.
To meet these challenges, the team compiled an extensive dataset of audio and visual recordings from various environments to ensure the AUV could operate in different water temperatures and levels of clarity. Data collection spanned locations from Lake Superior in Duluth, Minn. and Square Lake in Stillwater, Minn., to the Caribbean Sea off the coast of Barbados.
At the core of the field trials was a communication system called HREyes, where the robot could notify its human dive partner of their status, categorizing their breathing as "below-normal" (<14 breaths/min), "normal" (14–20 breaths/min) or "above-normal" (>20 breaths/min). By converting these visual observations into breaths-per-minute, the robot can determine if a diver is under duress.
Looking ahead, the team plans to pair breathing-rate data with the analysis of diver movement. Merging these metrics will provide a comprehensive “wellness profile" to ensure maximum safety during deep-sea explorations.
In addition to Sattar and Kutzke, the research team included Vennela Dupati, undergraduate student in the Department of Computer Science and Engineering and the Department of Electrical and Computer Engineering.
This research was supported in part by the Science, Mathematics, and Research for Transformation (SMART) Scholarship from the U.S. Department of Defense and the National Science Foundation.
Read the full paper entitled, “Robotic estimation of single scuba diver respiration rate for safety in underwater human-robot collaboration,” on the Sage Journal’s website.
Martina Buchna, a ranger at the Bavarian Forest National Park, and developer Dr Stefan Kahl are convinced of the strengths of the BirdNET Live bird-song app. The ranger demonstrates an audio logger that can be left hanging in the forest for several days to record sounds. Training data for the app was collected using this device.
Credit: Gregor Wolf / Bavarian Forest National Park
### Joint news release from Julius-Maximilians-Universität Würzburg (JMU), University of Technology Chemnitz, and the Bavarian Forest National Park Authority ###
Previous apps for identifying animal calls regularly reached their limits in practice: without a stable internet connection, real-time identification in the forest or high mountains was impossible.
The new BirdNET Live app, a further development of the BirdNET app, solves this problem with a technological breakthrough: the underlying AI models have been optimised, streamlined and accelerated to such an extent that they can now run directly on the smartphone.
Around 11,000 bird species are currently known worldwide; the app can currently recognise 8,927 of them. It is also capable of identifying the calls of 268 mammals, 254 insects and 340 amphibians, in real time and completely offline. In total, BirdNET Live can identify almost 10,000 animal species by their sounds – more than any other app, as lead developer Dr Stefan Kahl from the Professorship of Media Informatics at the University of Technology Chemnitz explains: the next-best offline app can only identify around 2,300 species.
As an open-source project, BirdNET Live offers maximum usability. The app’s user interface is currently available in seven languages; the names of the identified animal species are translated into 25 languages.
A tool for experts and the general public
Whilst conventional identification apps are primarily intended for occasional use in one’s own garden, BirdNET Live is aimed first and foremost at nature conservation professionals, researchers, students and ambitious citizen scientists. Amateur ornithologists can, of course, also use it.
The app features specialised modes for structured surveys, as are standard in scientific fieldwork. It automatically records animal calls with precise location and time data and stores them locally on the smartphone.
The recordings can subsequently be flexibly reviewed, exported or shared with other experts for validation. At the same time, the app serves as a learning tool: users can listen back to their own acoustic recordings directly and thus continuously improve their species identification skills.
Deutsche Bundesstiftung Umwelt funded the development team
BirdNET Live is the result of close interdisciplinary collaboration as part of the RangerSound project, which was funded by the Deutsche Bundesstiftung Umwelt (DBU).
To identify the shortcomings of existing bioacoustic systems in everyday practical use, conservation biologists from the University of Würzburg, the rangers at the Bavarian Forest National Park, hardware experts from the company Oekofor (Freiburg / Breisgau) and AI specialists from University of Technology Chemnitz worked hand in hand.
The result is a practical and robust app designed specifically for field conditions. When used with an external microphone, for example, the smartphone can be carried safely and waterproofed in a rucksack whilst the app continuously records data in the background.
BirdNET Live is an enormous technological progress
“At first glance, running the AI locally on a smartphone seems like a small, logical step forward. However, behind this lies enormous technological progress in optimising our AI models,” says Dr Stefan Kahl from University of Technology Chemnitz, an AI expert and lead developer of BirdNET.
The app demonstrates just how important interdisciplinary collaboration is: “It was only when practitioners, conservation biologists and we, as AI developers, sat down together that we were able to identify the real shortcomings in the field and create a tailor-made, practical solution. This is a model that can be applied to many areas where AI is finding its way into our everyday lives.”
Professor Jörg Müller: “An app for students too”
“We will now be using BirdNET Live as standard in our university teaching. This will give our students entirely new opportunities to collect standardised data for their dissertations or during field courses,” says Professor Jörg Müller, Chair of Conservation Biology and Forest Ecology at the University of Würzburg.
The app’s potential extends far beyond local forests: “We expect the app to serve us exceptionally well in our research work in the extremely species-rich lowland rainforest in Ecuador, where there is no mobile phone coverage whatsoever.”
Dr Simon Verdon: “Ideal for rangers”
“Local analysis carried out directly on site is exactly what we’ve been hoping for in the day-to-day work of ranger teams in protected areas,” says Dr Simon Verdon from the University of Würzburg, a member of the RangerSound project: “They can simply leave the app running during their patrols and immediately get a clear picture of the local species composition without having to rely on mobile network coverage.”
Further facts about BirdNET
• De facto standard: BirdNET is an AI system for the automatic recognition of bird calls and the world’s leading technology for AI-supported bioacoustic monitoring.
• Successful citizen science project: With more than two million active users worldwide and over six million app downloads, more than 250 million verified nature observations have already been collected.
• Important for science: The data collected from citizen science projects feeds directly into global research projects. For example, BirdNET data collected via the BirdWeather network provided the basis for a study published in the renowned journal Nature on the impact of light pollution on birds’ singing behaviour.
• Partners: BirdNET is a joint project between University of Technology Chemnitz and the Cornell K. Lisa Yang Center for Conservation Bioacoustics (USA).
The ‘Schlaumeise’ portal helps you get started
For aspiring citizen scientists, schools, teachers and learners, the schlaumeise.org portal offers supporting materials, tutorials and project ideas covering the topics of bioacoustics, species conservation and artificial intelligence.
Further information on the app and how to download it:
• Official project website: birdnet.tu-chemnitz.de/live-app
Here, chaffinches, robins and blue tits are singing: with the BirdNET Live app, even non-experts can identify bird songs in real time.
In transect mode, the BirdNET Live bird song app runs for several hours. This is useful for applications in national parks, ecology and nature conservation, as it allows bird songs to be assigned to specific points along a route that has been walked.
