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Tencent Music Entertainment Group to Report Third Quarter 2026 Financial Results on November 17, 2026
Tencent Music Entertainment Group ("TME," or the "Company") (NYSE: TME and HKEX: 1698), the leading all-in-one music and audio entertainment platform in China, today announced that it will report its unaudited financial results for the third quarter of 2026 before the U.S. market opens on Tuesday, November 17, 2026.
CT manufacturing suppliers face costly cybersecurity rules to keep defense business
Connecticut defense suppliers like Windsor’s NE-XT Technologies are still investing in cybersecurity despite the Pentagon’s pause on CMMC audits.
Oregon Medicaid program erroneously paid up to $4.1M due to system errors, audit says
The overpayments are a small fraction of what the state spends on its Medicaid program, but they could put federal funding at risk, state auditors warned.
The 90-day business reset every real estate agent should do before 2027
A 90-day reset helps agents audit lead sources, rebuild databases, and tighten systems, then make 3 decisions for 2027.
Tesla Opens Cybercab Fleet To Businesses As NHTSA Audits Paperwork
Tesla is courting businesses to buy Cybercab fleets as NHTSA audits how the steering-wheel-free robotaxi was certified for U.S. roads.
Want a chance at being the next winner of 'American Idol'? Check here for Louisiana auditions.
Want a chance at being the next winner of 'American Idol'? Check here for Louisiana auditions.
10 stars who were discovered in unexpected ways
Not every star is discovered in an audition room. EW rounds up 10 famous actors, like Janet Leigh and Charlize Theron, who got their big break when they least expected it.
Reports show MD owed millions by providers, local health agencies
The Office of Inspector General for Health conducted almost 1,400 audits and investigations over the last four years that found more than $42 million in improper or unrecovered payments — they only recently were made public.
Daldrop Opens for Business in Southern Oswego County
$10 Million Facility in the L. Michael Treadwell Oswego County Industrial Park marks the largest expansion outside of Germany in the company’s 73-year history By Stefan Yablonski [caption id=attachment_11076 align=alignright width=450] Daldrop SBB CEO Jens Daldrop addresses attendees at the 2025 groundbreaking for the project. Daldrop also chairs the advisory board of Germany-based Daldrop Group.[/caption] A new era of cleanroom technology is underway in Central New York. The Daldrop Group’s advanced manufacturing facility and U.S. headquarters is physically built within the L. Michael Treadwell Oswego County Industrial Park. This industrial park is located in the town of Schroeppel. It marks the largest expansion outside of Germany in the company’s 73-year history. Daldrop SBB is based in Neckartailfingen, Germany. It is a global supplier of cleanroom technology, primarily to the pharmaceutical industry. It designs and builds advanced cleanroom environments for the pharmaceutical, biotechnology and medical technology industries. It nvested $10 million in the new project. “We broke ground in 2025; we were under construction all winter. We just opened this spring, early spring,” said Benjamin Morrow, operations manager and control. The company consolidated sites in Salina and DeWitt into the industrial park in Schroeppel, Morrow added. “The next move here is to maximize every square inch of this facility and then we are already looking at the expansion. So, it is more capital investment on equipment — definitely higher-end automation in additional equipment so we can produce more components and then to support the ever-growing base in North America,” Morrow continued. “We are the headquarters for service, maintenance and spare parts. So as Daldrop has more and more installations, they need more and more service, maintenance and spare parts. So that will come out of this facility as well — it already is.” “As a global leader in GMP (global manufacturing support) pharmaceutical cleanrooms for decades, I am very proud to bring manufacturing jobs to Upstate New York,” said Mike Sullivan, president of Daldrop US operations. Key to choosing Southern Oswego County [caption id=attachment_11077 align=alignright width=450] Daldrop SBB at the L. Michael Treadwell Oswego County Industrial Park when the company announced the start of construction.[/caption] Sullivan said that the selection of the Schroeppel industrial park was mainly due to the help of Operation Oswego County and two people in the organization. “Prior to being purchased by Daldrop International from the Stuttgart region of Germany, Vince Bongio was a founding partner in SBB Inc. as well as our head of engineering. We based out of Syracuse due to Vince’s skills in HVAC and the manufacturing capabilities found in Syracuse,” said Sullivan. Bongio moved to Syracuse from California in the 1970s to work in Carrier’s Industrial Division of Large-Scale Petrochemical Refrigeration. Next, he advanced to General Electric’s TV Tube Plant where he got into cleanrooms ,leading to his role of head of engineering for Clestra Cleanrooms where he met Sullivan, their New England distributor. “Vince Bongio is a phenomenal engineer. Formerly of Carrier, he grew up in California and went to Cal Poly,” Sullivan explained. “Carrier hired him right out of college and he moved to Syracuse. He is the reason we are in Syracuse today. He is retiring this year. We are very proud and his legacy is having brought Daldrop to Syracuse.” In 2000, Sullivan, Bazinet, Bongio Inc (SBB) was formed with engineering and manufacturing, based in Syracuse, and sales and construction based in Massachusetts as it is today. “SBB Inc. started in Syracuse. I had a design–build contracting firm out of Massachusetts that was primarily doing all the new technologies coming out of MIT,” Sullivan said. “I started a manufacturing side. A big thing for cleanrooms is the HVAC. Most cleanroom suppliers are architecturally focused people. The strength in Syracuse is HVAC. So, SBB developed with me running sales and construction out of Massachusetts and manufacturing out of Syracuse.” “Daldrop wanted to have a relationship in the States and of those who approached us, they were the only ones who really understood HVAC. So, we were the right fit for them. We did a joint venture focused out of Massachusetts from 2012 to 2018 leveraging their technologies,” Sullivan explained. “The commitment was if we brought their product to the States, we would do that if they built it here. “The pieces that had to be in place to make the investment, they wanted to acquire us, we were great with that. If you looked at business recommendations the general guidance was to build in a low labor area — something like North Carolina — but Syracuse had a strong background in HVAC and we chose Syracuse and Oswego County to be our headquarters in the States. So, it is built off of history, technology and the existing supply chain — bringing jobs back to the area.” During the initial discussions with Daldrop about coming to the U.S., SBB agreed to sell under the conditions Daldrop would manufacture in the U.S. There are many areas that offer incentives and economic packages, but the strength of HVAC expertise and skilled manufacturing was a big part of the discussions for site selection. Coming home [caption id=attachment_11078 align=alignright width=451] Construction moves forward in March at the L. Michael Treadwell Oswego County Industrial Park.[/caption] The second key person to building in Oswego County is Ben Morrow, Sullivan added. “Ben has been working with Daldrop SBB for more than 12 years and has advanced to COO of manufacturing and engineering. Ben grew up in Phoenix, so building a factory for the Germany company in Phoenix is a strong success for both his community and the appreciation of Ben’s skills as a team leader,” Sullivan said. “I went to high school in Phoenix. So, this is like full circle for me. It’s a small world,” Morrow said. “I hadn’t been back to Phoenix since graduation. This is one of the properties that John Trimble of C&S, he is the former president of C&S, he just retired, suggested. He and I were very good personal friends. John was from Mexico and I was from Phoenix. We knew each other from high school. And the architect of this building, Ed Smart of smartDESIGN Architecture in Batavia — I went to high school with him. So, this was a project that’s full circle a coming home kind of thing. The three of us knew each other — kind of like it was meant to be. Very serendipitous.” Schroeppel was one option for Daldrop to consider; the original intent was just to get a warehouse to store some product, he added. The facility is Daldrop’s largest outside of their native country. Total payroll at the facility will be approximately $2.4 million once fully staffed. “Here is 32,000 square feet and we plan to expand and put in an additional 10,000 square feet,” Morrow said. “We have 17.5 acres. We can put at least three more facilities here, just on this property. The master plan submitted for the current building is the first of possible three buildings on the campus.” “If we can find the right people and advance the production according to the plan, we should be able to grow for the next decade. One of the first jobs we filled was our new HR manager, with a primary focus posting and filling new positions,” Sullivan said. Stop the exodus, start hiring [caption id=attachment_11080 align=alignright width=452] Principals at the local Daldrop SBB headquarters, from left: Ben Morrow, Mike Sullivan and Jens Daldrop.[/caption] “I have been coming to Syracuse for 30 years. One of the things I had a hard time watching was the exodus of factories. One of the toughest things is to watch it with my partners’ kids who had to move away for jobs. It is important for us to give opportunity where people’s kids don’t have to leave the area to get work,” Sullivan said. “My responsibility is to find people who have skill sets that want to work and then I have to build opportunity for that work to happen. “After years of watching companies and Vince’s three sons all move out of the areas to get jobs, it is important to us to provide opportunities for the Syracuse region and help grow our company. We have been hiring for our new factory and will continue for the next few years.” “We are hiring and will continue to hire. Currently we have about 19 on staff right now,” Morrow said. “We expect to grow that by about 10 to 15 in the next 12 to 18 months or so. We are at 19 currently — one in the wings. It’s been great being able to hire locally. All these folks that we’ve been hiring, they all live within a few miles from here.” “We hired someone this past week (end of August). She grew up in the area, raised a family and is working down in Florida. She just took a job with us so she can move back and be with her family. We will be moving her back and she should be starting in October. We are very happy to see this happen,” Sullivan said. “I feel strongly about having a spot for the kids to not have to move out of the area. It is better to have people moving into the area.” Currently, Daldrop SBB has openings for finance manager, COO of construction and delivery, as well as mechanical, electrical designers and Revit modelers, construction managers, commissioning agents and many other skills that are needed. Along with Daldrop’s reputation in life science cleanrooms, there is a strong history in semiconductor, electronics, glass, photonics, aerospace and other markets. This may be helpful with any new technologies building in the market such as the Micron expansion and the support network, Sullivan said. “We acquired this property and closed on it two months before any announcement from Micron. It is serendipitous that they’re right down the street from us,” Morrow added. “Everybody started thinking ‘you guys are supporting Micron.’ We were here first. That is not a goal of ours. We may or may not … they are talking to us; it is an opportunity.” The long run [caption id=attachment_11079 align=alignright width=450] Benjamin Morrow, operations manager and control at Daldlrop, speaks during a recent ribbon-cutting event in Schroeppel.[/caption] “Long term, we will expand the Phoenix facility,” Sullivan said. “We built our facility with a campus approach. Sustainability is big. We designed our rooms to be more sustainable. Our building has no gas lines for heat. We put geothermal wells in the ground. We put heat pumps in for heating and cooling out of those wells and we put a large layer of solar panels up on the roof. We worked with National Grid for metering. Our building is a high efficiency model. We upgraded all the instillation and the glass filtering. So, it is a very green building. We will have a very minimal carbon footprint. It also adheres to LEED (Leadership in Energy and Environmental Design) construction standards.” “We are a bunch of HVAC engineers. We did a large scale geothermal and spent the money up front because we knew it made sense in the business sense in the environmental sense and sustainability,” Morrow said. “We have insulated metal panels on walls and roof; we are an envelope from the exterior.” Daldrop, as a global player, has a strong culture of sustainability that is found in all its products including the new facility. The building has been designed with (sixteen) 400-foot deep geothermal wells, high efficiency construction methods, approximately 70 kilowatt solar photovoltaic panels, electric vehicle chargers and 26% recycled content. “Daldrop thinks of long-term investing,” Sullivan said. “On the roof, photovoltaic panels supplement our power. Over half the energy that I use is coming from the sun. We have chargers up front for employee electric vehicles. This place is built to last. This slab that we are walking on, normal buildings are four inches, some go to six inches, we are nine inches all the way through — except where the heavy equipment is — that is 10 to 12 inches thick, solid concrete. It allows us to reconfigure for whatever type of equipment we want to bring in,” Morrow said. “We are authorized by Underwriters Laboratory to self-certify so we don’t need to have an inspector come out and recertify. We get audited often, but we certify our own electronics. The control system in this building is rather high-end. Our thermostat in every room is zoned, independent. We analyze the data; incorporate it with our solar panels.” “There is a lot of competition for people to work. It’s not just about how much salary they are going to make — it’s about the work environment, the ability for stability. We want to make sure our employees have the best environment to work in,” Morrow added. Similarities “One of the reasons I believe why Daldrop chose this area, Phoenix in particular, is because it is a village, a river-based village. Neckartailfingen in Germany where Daldrop is based is also a river-based village, manufacturing community. So, there is a lot of similarities — that smalltown feel,” Morrow said. “We have a lot of connections through multiple employees that lead back to Carrier, also. Everything came together, the timing everything.” “We are thankful for all the economic development support. Everyone has been helping make this happen. So, keep it up, it is working, that is all I got to say, Sullivan said. “If you look at historical industrial manufacturing, this region has skills that have been un-utilized. The northern industrial region has a lot of skills that if we can tap before they go away, we can leverage them. That is what we are trying to do. You’d be surprised at the supply chain left behind from Carrier — that supply chain is still there.” “Operation Oswego County has been fantastic,” Morrow added. “I can’t say enough good things about that group.” What Is Cleanroom Technology Cleanroom technology is a system of specialized engineering, filtration and operational controls used to maintain an extremely low concentration of airborne particles, microbes and chemical vapors. How It Works • Air Filtration: Uses HEPA or ULPA filters to remove 99.97% of tiny particles down to 0.3 microns. • Airflow Control: Directs air in a steady, unidirectional (laminar) or filtered turbulent pattern to push dust away from work areas. • Environmental Regulation: Maintains precise control over temperature, humidity and positive air pressure to block outside air leaks. • Specialized Attire: Requires personnel to wear full-body garments (“bunny suits”), gloves and masks to stop human shedding
Bodies
your look inside the human body starts here. Bodies – the science within brings real human specimens, carefully and respectfully preserved, to Sacramento memorial auditorium, giving visitors …
State paid health policy nonprofit $108 million before reviewing work plans, audit finds
Rep. criticizes failures in $435 million deal: ‘Major weaknesses exist in the department’s oversight practices’
North Country Youth Chorus Opens Season Under New Director
ST. JOHNSBURY, Vt. — The North Country Youth Chorus will begin its 2026-27 season on Monday, Oct. 19, open to all children in grades four through eight. There's no audition
LA County Public Health fell short in response to Chiquita Canyon landfill issues, audit says
An underground reaction at the Chiquita Canyon landfill has produced chemicals and bad odors for years.
Knowtex launches frontier healthcare AI lab
Artificial intelligence company Knowtex launched a frontier AI lab for healthcare, aimed at developing and rigorously evaluating clinical AI tools for accuracy, auditability and clinician trust.
Gemini's Personal Intelligence is coming to cars
It's probably time to audit your Gemini settings.
Chappell Roan brings out Christina Aguilera at L.A. benefit concert
The pop star brought her old hits — and a surprise appearance by Christina Aguilera — to the first night of her Super Graphic Spectacular fundraiser at the Shrine Auditorium.
New wave of healthcare fraud enforcement
In past years, federal and state authorities typically initiated healthcare fraud investigations based on whistleblower complaints and audits. While that still occurs, the model is changing. Federal enforcement agencies are increasingly using sophisticated data analytics, artificial intelligence, and coordinated investigative efforts to identify potential fraud. The Department of Justice (DOJ),…
Board of criticized North Dakota business financing program plans meeting to address audit
Officials overseeing a state-backed business financing program that drew critical audit findings and a sharp response from lawmakers plan to meet in coming weeks to address the situation.
Alabama Department of Public Health improperly reimbursed $28M in federal vaccine program, audit finds
$27.94 million of the ADPH’s reimbursements over a five-year period had inadequate documentation or were unrelated to contract purposes.
Can AI solve our mental health access problem?
Last week I asked you what worries you most about AI in health. It was close, but your top answer was “AI giving bad health advice.” Last week’s poll results: You’re in good company. This concern was top of mind at a conference I joined at Stanford last week, where researchers, clinicians, and people who build AI systems spent a day debating trust and safety in health AI. Stepping back, I’m also hearing your emerging concerns about AI’s impact on our well-being generally. I see this as a matter of public health, and we’ll take it in pieces as we learn together. Today we’ll focus on AI chatbots used for therapy for mental health. Early this month, Governor Newsom vetoed Senate Bill 903, California’s most ambitious attempt to regulate AI use for mental health. It’s at the center of active debate, and raises the questions patients, family members, citizens, and voters will face as AI moves deeper into health care. Let’s work through it. What is “AI-based therapy”? It’s really two things: purpose-built mental health apps designed to deliver structured support, and general chatbots like ChatGPT, Claude, and Gemini that people increasingly use for emotional support—even if they weren’t built for it. Spoiler alert: I don’t know whether the shift toward AI therapy is good or bad. The evidence isn’t there yet. But I do know it’s already happening, and I have some ideas about how we can roll it out with safety in mind. The therapy dilemma, in a nutshell Mental health challenges are common, and strain health and well-being across populations. Therapy (interactive dialogue with a trained, empathetic practitioner) helps many people heal. The demand for licensed therapists far exceeds the supply in California, and AI-based therapy can narrow that gap. AI-based therapy remains novel, unproven, and not well tested. Right off the bat, we can see we’re dealing with trade-offs here. Since the main driver of demand for therapy from a robot is lack of access to therapy from a human, we’ll start there. It’s too hard to find and afford a human therapist California is short roughly 55,000 licensed, non-prescribing therapists and counselors, about 40% of what we need. Psychiatrists, who rely on talk therapy as a complement to prescribed medications, are even more scarce. Eight counties have none, and density ranges from 1.7 per 100,000 residents in San Benito County, to 68.1 in Marin County. My wife is a psychiatrist in Marin, and even here access is tight. If it’s hard in the best-supplied county, imagine rural Sierra County, or the San Joaquin Valley. In 2024, more than half of Californians who tried to make a mental health appointment struggled to find a provider who took their insurance, compared with 14% for physical health care. When Senate staff posed as “secret shopper” patients and called 120 mental health providers in Medicare Advantage networks, they landed an appointment less than once for every five tries. Insurers pay behavioral health clinicians about 22% less than medical and surgical clinicians for comparable office visits. On top of that, billing denials and prior authorizations hit hardest at the solo and small practices common in mental health. Psychiatrists opt out of Medicare at the highest rate of any specialty. In many places, a cash practice for therapy fills quickly with patients paying $250 or more an hour, with no insurance paperwork at all. Families who can pay cash find providers more easily. Those who rely on insurance, including Medi-Cal enrollees, older adults, rural residents, and non-English speakers, compete for what’s left. So we can’t pretend the human system works, and we can’t blame people for turning to AI for support when they’re in distress. AI is filling the gap It’s estimated that roughly a quarter of AI users use it for mental health support. If you have internet, it’s available 24/7, it’s cheap, and it speaks your language. Uninsured adults are about twice as likely as insured adults to do it (30% vs. 14%). It feels anonymous in ways that may enable people to ask things they’re unsure or embarrassed about. The largely hidden, personal use makes it hard to study whether it’s actually helping people with their mental health issues, but researchers are trying. Does AI therapy work? A 2026 analysis of nearly 40 randomized trials found chatbots showed a real but small reduction in depression symptoms. But most of these trials were compared to an alternative of no therapy, not a human therapist. So the benefit of AI therapy is probably real, but modest, and mostly untested against the alternative of human contact. We need more studies. Serious concerns OpenAI’s own estimate is that more than a million ChatGPT users each week show signs of suicidal planning or intent. These are high stakes encounters, and AI doesn’t always get it right. That’s partly because a chatbot can sound like a therapist, but it has no license, no duty of care, and it’s not clear who to hold accountable when it gets things wrong. AI is also designed to help users in whatever task they’re seeking to accomplish. People may feel like they’re getting therapy, but they’re mainly getting a convincing conversation. It’s easy for an AI companion to wander into gray areas, like playing along with “creative writing” that may be about the user’s own suicidal intent. Courts are driving safety conversations A wave of lawsuits have been filed on behalf of families claiming that chatbots pushed people they loved toward self-harm, including teens who died by suicide. The first settlements came this year. OpenAI is headquartered in San Francisco, and dozens of cases alleging that ChatGPT drove users to suicide or self-harm are now before a San Francisco judge. Character.ai has filed for bankruptcy after several states brought lawsuits accusing the chatbot of pushing dangerous content to kids. But the technology seems to be improving. Last month, an independent evaluation found that newer AI models “almost never endorse suicide.” That’s progress, but our societal appetite for even one preventable death is very low. So how do we move forward? Finding a middle ground Earlier this month the governor vetoed Senate Bill 903, a bill designed to place guardrails on the use of AI for therapy. The bill would have prohibited any AI-based platform from being advertised as psychotherapy, and would have required a licensed professional to review and approve each encounter where AI was used to support therapeutic decisions. Newsom called it overly broad, since even routine screening questions would have needed clinician sign-off. Knowing we’re already short on mental health providers, requiring human review of AI encounters may not relieve our access problems. Still, the answer isn’t no oversight. Here’s one feasible short term solution. Rather than reviewing each encounter, a licensed clinician can design the guardrails: what counts as a crisis, what triggers an immediate handoff to a human, and what gets sampled for audit each month. The AI runs inside that structure. Conversations get checked, failures get reported, and a real clinician owns the framework. Medicine already has models to learn from. The FDA process for rolling out newly approved medications is an example. After a product reaches patients, we keep watching for adverse events. Patient-facing AI needs the same “digital pharmacovigilance”: systematic tracking of harms, near misses, and the types of groups that get hurt and why. It’s also critical that users know that they can reach a human if they’re in crisis. Most chatbots now include messages like these: Bottom line The rush to use unproven AI tools for mental health support is mostly a workaround, a sign of a health system people can’t get into. We need better access to proven care from licensed clinicians. That takes reimbursement that makes accepting insurance viable, and a bigger workforce. But, built and tested well, AI could be part of the answer. It could be the 2 a.m. check-in when no one else is awake, or a way for patients to practice skills their therapist taught them. It could be the first step to get someone the right level of care, and take paperwork off clinicians’ plates so they spend more time with patients. None of this is simple. The SB 903 veto doesn’t end the debate, and the legislature will be back. In the meantime, those of us using these tools need to stay informed, ask hard questions, and hold the companies building them accountable. I’ll try to do my part, and we’ll keep learning together. Love,Matt P.S. If you or someone you know is struggling with mental health challenges, emotional distress, substance use problems, or just needs to talk, call or text 988- you’ll reach a real person— or chat at 988lifeline.org 24/7. Dr. Matt Willis is the author of Your Local Epidemiologist in California. A California native, he’s served as a primary care physician, CDC epidemiologist, and public health officer for Marin County, where he guided the pandemic response. He lives in Marin with his family and their dogs, Teddy and Ramona. Thank you Cynthia He, MD, PhD, and Parnika Saxena, MD, for their guidance in preparing this article. Cynthia works in an outpatient mental health clinic, treating patients who have Medi-Cal or, sometimes, no insurance. Parnika works in an inpatient psychiatric unit, treating patients with public or private insurance or self-pay ability. All opinions are their own and do not represent their employers.
Audit lays out safety recommendations for Business Loop 70
An audit found road safety hazards for pedestrians, drivers and cyclists in need of improvements on Business Loop 70.
Finalists named for Nistler College of Business & Public Administration deanship
Open forums will be held Oct. 5, 7, 12 and 14 in Nistler Hall’s Barry Auditorium.
Sebastian Stan recounts failed “American Idol ”audition: ‘They line you up like they’re gonna assassinate you’
Needless to say, he did not secure a Golden Ticket.
Audit into public health contract process sparks outrage from Michigan House Oversight members
Audit into public health contract process sparks outrage from Michigan House Oversight members
‘Clear political stunt’: Beshear rejects Ball’s nominees for executive ethics commission
A day after submitting nominations for an appointment to the Executive Branch Ethics Commission, Republican Auditor Allison Ball says Democratic Gov. Andy Beshear rejected her list. A spokesperson for the governor said the list was “a clear political stunt.”
'The Wiz' Eases on Down the Road to Eisenhower Auditorium
The hit Broadway musical “The Wiz” will bring its reimagining of “The Wizard of Oz” to Penn State next week, featuring performers from “American Idol” and
Triune Entertainment Holds Auditions for Exciting World Premiere
Triune Entertainment Holds Auditions for Exciting World Premiere
Paramount Business Jets gains two industry safety certificates
Paramount Business Jets has attained certified broker status from both the ARGUS and Wyvern safety audit programs
New commission works to untangle challenges at health licensing boards
After state audits reported inadequate response times by some health licensing boards to complaints about licensees, and other boards behind on healthcare facility inspections, lawmakers set up a new commission to address the issues causing those oversight issues.
Report: LA County fell short in addressing Chiquita Canyon health impacts
Los Angeles County showed “shortcomings” from the beginning, the State Auditor said.
California falls short on Medi-Cal fraud, state report says - The Business Journal California auditor flags Medi-Cal fraud oversight concerns
California's state auditor raises concerns about Medi-Cal fraud oversight, citing millions in questionable claims and thousands of high-risk providers not under investigation.
These Angels Are Flunking Their Audition For Next Season
The Los Angeles Angels have been conducting tryouts for next year, and most of the results haven't been pretty.
Former MS Rep. Nick Bain shares plan to tackle state auditor backlog
After losing his state House reelection bid by two dozen votes, Nick Bain is touting that loss in his run to fill the state auditor's seat next year.
John Stamos Shares Beach Boys' Mike Love Is Facing Health Issues
Full House's John Stamos revealed that Beach Boys singer Mike Love is facing "serious health challenges" ahead of their performance at the Ryman Auditorium in Nashville.
Supreme Court revives DHS citizenship data tool for voter audits in win for Trump
In a major win for President Donald Trump’s efforts to find non-citizen voters, the Supreme Court on Friday said states can use a controversial citizenship data collection tool for reviewing voter rolls.
It's a Date: Youthclusive Posts Tryouts for 'The Prom'
Young Santa Barbara County performers, musicians, directors, designers, technicians, and crew members are invited to audition with Youthclusive Sept.
Grammy-winning singer Victoria Monét to make hometown Sacramento tour stop
The award-winning R&B and soul singer will bring her tour to Memorial Auditorium this winter.
Minors account for half of complaints against 'academy-style' entertainment agencies
A total of 366 dispute resolution requests involving 'academy-type' entertainment agencies, those that train their clients, provide auditions or even guarantee certain opportunities for a cost, were filed between 2023 and June this year.
Bruce Hornsby talks hits, new album ahead of Iowa City show
Piano virtuoso Bruce Hornsby reflects on his hits, new album “Indigo Park,” and a freewheeling career ahead of his Sept. 30 concert with the Noisemakers at Hancher Auditorium in Iowa City.
Amarillo NAACP hosting mental health forum on Saturday
The Amarillo NAACP is conducting a Mental Health Forum, focused on people of color, in the Amarillo Area Foundation auditorium at the corner of 10th and Polk.
Blessing Health System to host hiring event for entry-level care roles Sept. 30
QUINCY — Blessing Health System will host its next Walk-In Wednesday hiring event from 4 to 5 p.m. on Sept. 30 in the Blessing Hospital Auditorium, targeting job seekers interested […]
Butler County mental health board eliminates finance department after audits find accounting discrepancies
As the Butler County Mental Health and Addiction Recovery Services Board seeks voter approval of a 1-mill, 10-year renewal levy, the board has voted to eliminate its entire finance department following the discovery of accounting discrepancies dating back more than three years.
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
Voluntary Business Update on Prosus Food Delivery
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Want a chance at being the next winner of 'American Idol'? Check here for Louisiana auditions.
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Paul Becht
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Viewpoint: Unlocking Europe’s wildfire data
Europe is already investing in the science, technology and infrastructure needed to understand, prevent and fight wildfires. Copernicus provides satellite-based Earth observation and environmental information, while the European Forest Fire Information System provides information on fire danger and forest fires across Europe. Weather and climate systems add forecasts of heat, drought, wind and humidity; historical fire databases show where fires have occurred; and researchers generate increasingly detailed knowledge about fuels, fire behaviour, forecasting, modelling and risk management. Meanwhile, new European research projects are adding digital twins and other tools to this growing ecosystem.However, these data do not necessarily sit together, nor are they necessarily described, structured or governed in ways that allow them to be used together. Much of the information is public, but some is commercially held. Some is sensitive. Some is subject to legal, security or operational restrictions. Even when data can be legally accessed, different systems may use different formats, terminology and rules.Europe therefore faces an unusual paradox: we have vast amounts of relevant information to respond to the wildfire challenge, but not the means to bring it together. For example, a wildfire researcher or emergency service might need to ask whether a particular combination of vegetation, drought, wind and topography has historically been associated with rapid fire spread. The relevant information may exist in several institutions, using different systems and subject to different conditions of access.The challenge is to conduct an authorised analysis that interacts with these sources, combines the results and respects the rules imposed by each data custodian.An important part of the answer can be found in the Fair principles, the idea that data should be findable, accessible, interoperable and reusable. Developed more than a decade ago, these principles have become a central reference point for modern data stewardship, making it easier for people and machines to find data, understand what it means and determine whether it can be reused.Readability by machines is essential, since AI systems will be key to meeting the data challenge. Bring analysis to the dataThe proposition is not to give an AI system unrestricted access to sensitive databases. Quite the opposite. The goal is to make it possible for approved algorithms to go to the data, rather than requiring sensitive data or large datasets to be copied to wherever the analysis happens.Imagine an analysis asking a series of questions of different data stations. At one station it might examine weather and climate data; at another, historical fire records; at another, experimental measurements of fire behaviour. The algorithm does not need to download everything. It can operate within the environment permitted by each data custodian and return only the results that the rules allow to leave.This is sometimes described as data visiting, and related approaches already exist in areas such as federated analysis and federated learning.Sensitive data should therefore not be treated as a free-for-all for machines. This requires appropriate governance at multiple levels. Access needs to be authenticated and authorised. The purpose of an analysis should be defined. The code or model being run should be controlled. Data custodians should be able to specify what can be queried and what results can leave their environment. Activities should be auditable and human oversight should remain where the consequences require it.In other words, the objective is not to trust AI with the data. It is to design systems in which AI never has more access than it needs.As open as possible, as closed as necessaryFair data should also not be confused with open data. Some wildfire data should be openly available. Other information may legitimately need to remain restricted because it concerns critical infrastructure, security, commercial interests, privacy or operational capabilities. The principle should be: as open as possible, as closed as necessary.The important point is that restricted data should not automatically become unusable data. If machines can discover what information exists, understand its meaning, establish where it came from and determine the conditions under which it can be used, authorised analyses can potentially be carried out without the underlying data ever leaving the environment in which it is protected.Restricted data can still be Fair and machine-actionable.This is also an important question of data sovereignty. European institutions should not have to surrender control of their data to a handful of technology platforms in order to benefit from AI. Nor does Europe need to start again by building one giant repository for every piece of wildfire information. The better approach is to connect what already exists.This is the principle behind emerging approaches such as Tryangle, developed through the Leiden Initiative for Fair and Equitable Science. Rather than creating another centralised data warehouse, the approach seeks to provide an interoperable layer through which approved algorithms can visit distributed data sources while respecting the rules of their custodians.In this way, a data station can remain where it is. Its custodians can retain control. Existing databases and applications do not have to be abandoned. What changes is their ability to communicate with other systems through common, machine-readable descriptions, access rules and interoperability mechanisms.But approaches like this need data that meets the Fair principles. Only then can an interoperable ecosystem safely begin to turn fragmented information into collective intelligence.Data infrastructure is wildfire infrastructureThe implications of this approach are entirely practical. Firefighters could gain better intelligence about local and evolving risks. Land managers could test prevention strategies using more comprehensive information about fuels, weather and past fires. Researchers could compare evidence across ecosystems without repeatedly moving huge datasets between institutions. Smaller regions and resource-constrained teams could access analytical capabilities without having to build large data infrastructures themselves.Europe is already investing heavily in satellites, databases, research programmes, forecasting systems and scientific expertise. The question is whether we will connect those investments well enough to turn them into actionable intelligence.That requires policymakers and funders to treat data infrastructure as seriously as physical infrastructure. Data generated or supported through European public funding should be Fair, verifiable and machine actionable by design, wherever legally and ethically possible.Related articlesData Corner: Europe’s burning need for wildfire mitigation researchClimate monitoring must remain a global enterprise, experts sayIn practice, that means funding not only the production of data, but also the infrastructure that makes data usable across systems: metadata, provenance, interoperability, access mechanisms and governance. Where data must remain restricted, we should ensure that it can still be securely discovered, verified and used for authorised analysis.Initiatives such as the European Open Science Cloud offer an existing framework through which these principles can be put into practice. What is needed now is to accelerate implementation and make them a standard part of how Europe funds and builds research and innovation systems.We do not need another generation of disconnected databases. The data is already there. The opportunity is to make it work together, securely, responsibly and quickly enough to help prevent the next fire, rather than simply document the last one.Jean-Claude Burgelman is professor emeritus at the Free University of Brussels (VUB) and editor-in-chief at Frontiers Policy Lab. For more than 20 years he held leading roles in research and innovation policy at the European Commission.
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