Tech
GIGABYTE Brings Supercomputer Power to Your Desktop with AI TOP ATOM
GIGABYTE launches AI TOP ATOM on October 15th—a compact personal AI supercomputer powered by NVIDIA Grace Blackwell GB10. Delivers 1 petaFLOP performance for on-premises AI development, supporting models up to 200B parameters on your desktop.
Last Updated on October 15, 2025 by Daily News Staff
Global Launch Set for October 15th as Tech Giant Democratizes AI Development
GIGABYTE is making a bold move to put enterprise-level AI computing power directly into the hands of developers, researchers, and students. The company’s latest innovation, AI TOP ATOM, launches globally on October 15th, promising to transform how we think about on-premises AI development.
Desktop Supercomputing Becomes Reality
What makes AI TOP ATOM remarkable isn’t just its specs—though those are impressive—it’s the promise of bringing supercomputer performance into a compact form factor that fits on your desk. Powered by NVIDIA’s Grace Blackwell GB10 Superchip, this personal AI supercomputer delivers up to 1 petaFLOP of FP4 AI performance. To put that in perspective, we’re talking about the kind of computational muscle that can handle large-scale models with up to 200 billion parameters right in your office.
The system comes equipped with 128GB of unified system memory and supports up to 4TB SSD storage, giving users the resources they need for serious AI workloads without the traditional infrastructure headaches.
Scale When You Need It
Here’s where things get interesting for power users: GIGABYTE designed AI TOP ATOM with scalability in mind. Need to tackle even larger models? Connect two units using the built-in NVIDIA ConnectX-7 NIC, and you can handle models up to 405 billion parameters. It’s like having a modular supercomputer that grows with your ambitions.
Software That Actually Makes Sense
Hardware is only half the story. AI TOP ATOM ships with NVIDIA’s complete AI software stack preinstalled—the full suite of tools, frameworks, and libraries designed specifically for generative AI workloads. But GIGABYTE didn’t stop there. They’ve integrated their exclusive AI TOP Utility, which provides an intuitive interface for the tasks that matter most: model fine-tuning, inference, and deployment across large language models (LLMs), large multimodal models (LMMs), and modern machine learning applications.
This approach addresses one of the biggest pain points in AI development—getting everything configured and working together. With AI TOP ATOM, you’re ready to start prototyping and developing from day one.
Who’s This For?
GIGABYTE is positioning AI TOP ATOM as a solution for anyone serious about AI development, from individual developers and academic researchers to students and educational institutions. The compact chassis means it works in environments where traditional server infrastructure simply isn’t practical—dorm rooms, small offices, research labs with limited space.
The “personal AI supercomputer” concept represents a significant shift in accessibility. What once required cloud computing budgets or dedicated data center space can now happen on-premises, giving developers more control over their data, faster iteration cycles, and potentially lower long-term costs.
The Bigger Picture
As AI development continues to accelerate across industries, tools like AI TOP ATOM signal an important trend: the democratization of high-performance AI computing. When powerful AI development tools become more accessible, innovation happens in unexpected places—and that’s exactly what GIGABYTE seems to be betting on.
AI TOP ATOM launches globally on October 15th. For complete specifications, pricing, and availability in your region, visit the official GIGABYTE website.
Source: GIGABYTE.com
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Automotive
EPA removal of vehicle emissions limits won’t stop the shift to electric vehicles, but will make it harder, slower and more expensive
The EPA’s move to rescind the 2009 “endangerment finding” and roll back vehicle emissions limits won’t stop the shift to electric vehicles—but it will slow adoption, raise costs, and increase climate and public health harms.

Alan Jenn, University of California, Davis
The U.S. government is in full retreat from its efforts to make vehicles more fuel-efficient, which it had been prioritizing, along with state governments, since the 1970s.
The latest move came on Feb. 12, 2026, when President Donald Trump and the Environmental Protection Agency issued a new rule rescinding the landmark “endangerment finding,” and reversing various emissions limits on cars and trucks. The 2009 finding stated that greenhouse gases pose a threat to public health and welfare. If the new rule stands up in court and is not overruled by Congress, it would undo a key part of the long-standing effort to limit greenhouse gas emissions from vehicles.
As a scholar of how vehicle emissions contribute to climate change, I know that the science behind the endangerment finding hasn’t changed. If anything, the evidence has grown that greenhouse gas emissions are warming the planet and threatening people’s health and safety. Heat waves, flooding, sea-level rise and wildfires have only worsened in the decade and a half since the EPA’s ruling.
Regulations over the years have cut emissions from power generation, leaving transportation as the largest source of greenhouse gas emissions in the U.S.
The scientific community agrees that vehicle emissions are harmful and should be regulated. The public also agrees, and has indicated strong preferences for cars that pollute less, including both more efficient gas-burning vehicles and electric-powered ones. Consumers have also been drawn to electric vehicles thanks to other benefits such as performance, operation cost and innovative technologies.
That is why I believe the EPA’s move will not stop the public and commercial transition to electric vehicles, but it will make that shift harder, slower and more expensive for everyone.
Putting carmakers in a bind
The most recent EPA rule about vehicle emissions was finalized in 2024. It set emissions limits that can realistically only be met by a large-scale shift to electric vehicles.
Over the past decade and a half, automakers have been building up their capability to produce electric vehicles to meet these fleet requirements, and a combination of regulations such as California’s zero-emission-vehicle requirements have worked together to ensure customers can get their hands on EVs. The zero-emission-vehicle rules require automakers to produce EVs for the California market, which in turn make it easier for the companies to meet their efficiency and emissions targets from the federal government. These collectively pressure automakers to provide a steady supply of electric vehicles to consumers.
The new EPA move would undo the 2024 EPA vehicle-emissions rule and other federal regulations that also limit emissions from vehicles, such as the heavy-duty vehicle emissions rule.
The possibility of a regulatory reversal puts automakers into a state of uncertainty. Legal challenges to the EPA’s shift are all but guaranteed, and the court process could take years.
For companies making decade-long investment decisions, regulatory stability matters more than short-term politics. Disrupting that stability undermines business planning, erodes investor confidence and sends conflicting signals to consumers and suppliers alike.

A slower roll
The Trump administration has taken other steps to make electric vehicles less attractive to carmakers and consumers.
The White House has already suspended key provisions of the Inflation Reduction Act that provided tax credits for purchasing EVs and halted a US$5 billion investment in a nationwide network of charging stations. And Congress has retracted the federal waiver that allowed California to set its own, stricter emissions limits. In combination, these policies make it hard to buy and drive electric vehicles: Fewer, or no, financial incentives for consumers make the purchases more expensive, and fewer charging stations make travel planning more challenging.
Overturning the EPA’s 2009 endangerment finding would remove the legal basis for regulating climate pollution from vehicles altogether.
But U.S. consumer interest in electric vehicles has been growing, and automakers have already made massive investments to produce electric vehicles and their associated components in the U.S. – such as Hyundai’s EV factory in Georgia and Volkswagen’s Battery Engineering Lab in Tennessee.
Global markets, especially in Europe and China, are also moving decisively toward electrifying large proportions of the vehicles on the road. This move is helped in no small part due to aggressive regulation by their respective governments. The results speak for themselves: Sales of EVs in both the European Union and China have been growing rapidly.
But the pace of change matters. A slower rollout of clean vehicles means more cumulative emissions, more climate damage and more harm to public health.
The EPA’s move seeks to slow the shift to electric vehicles, removing incentives and raising costs – even though the market has shown that cleaner vehicles are viable, the public has shown interest, and the science has never been clearer. But even such a major policy change can’t stop the momentum of those trends.
This is an updated version of an article originally published Aug. 5, 2025.
Alan Jenn, Associate Professor of Civil and Environmental Engineering, University of California, Davis
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Consumer Corner
How agentic AI is changing the way contractors assess storm damage
Hours after a storm, teams of professionals need to move quickly to assess the damage. That work used to take days of site visits and time-consuming analyses. Now, a roofing contractor can pull up a color-coded map of every roof over 15 years old within two miles with agentic tools.

How agentic AI is changing the way contractors assess storm damage
(Tiffany Miller) Hours after a storm, teams of professionals need to move quickly to assess the damage. That work used to take days of site visits and time-consuming analyses.
Now, a roofing contractor can pull up a color-coded map of every roof over 15 years old within two miles with agentic tools. An insurance claims manager can see which homes have the worst damage and need to be inspected first. A government assessor can map which neighborhoods were hit hardest. As hurricane season opens June 1, Eagleview Horizon, an agentic AI engine, shows how predictive AI is giving these professionals a head start.
Why the trades are under pressure
Billion-dollar weather and climate disasters in the U.S. have nearly tripled since the 2000s, now averaging 19 per year, according to NOAA. The trades are paying for it. Insurance carriers are required to handle more claims. Contractors race to keep up with demand. And government planners assess wider damage zones due to population increases. For insurers, that pressure is already showing. AM Best reported that the homeowners insurance segment had its toughest first quarter in five years, driven by January wildfires in California and tornado outbreaks across the Midwest, Southern and Plains states. That hit a property and casualty insurance market where U.S. premiums crossed $1.05 trillion in 2024, according to S&P Global Market Intelligence.
For the majority of the 100,000-plus contractors in the $25 billion U.S. roofing industry, the work hasn’t changed much. They still spend hours canvassing neighborhoods street by street. Adjusters drive door-to-door. Government planners wait for damage reports to come in. As storm seasons grow more intense and claims volume rises, that model is starting to crack.
A shift across multiple industries
AI can find information faster, but newer systems can also turn a question into a workflow.
For early adopters, that is changing more than storm response. Commercial roofers can use these tools to pinpoint structural changes and complete annual inspections more safely. Infrastructure managers can track changes over time to flag maintenance needs. Property managers can identify which assets in large portfolios are approaching maintenance risk before they fail.
What may matter most is who can do this work. Until recently, sophisticated property analysis required trained specialists and could take weeks. Now any contractor, adjuster, assessor or planner can begin with a plain question and get a clearer view of where to act first.
What this means for storm season
Trades will keep facing issues caused by more storms, and time will tell whether new tools can spread fast enough to meet the overwhelming demands of the industry.
Photo courtesy of Shutterstock

SOURCE:
Eagleview
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Tech
Drones paired with AI could help search‑and‑rescue teams find missing persons faster
AI-powered drones equipped with thermal and infrared imaging are transforming search-and-rescue operations, enabling teams to locate missing persons faster and assess their condition—including signs of injury, consciousness, or life-threatening temperature changes—in real time.
Last Updated on May 16, 2026 by Daily News Staff
Adeel Khalid, Kennesaw State University
A combination of infrared imaging, thermal imaging and color cameras on an uncrewed drone, along with an AI system to interpret the data, can help emergency responders and search-and-rescue teams locate, identify and track people who have gone missing in the wilderness. The experimental system helps responders pinpoint where a missing person is and determine whether they are hurt or even alive.
People who get lost or hurt while exploring nature can become stranded for days. Rescue teams often use drones to look for the person or signs of their whereabouts. The small drone my colleagues and I built at my lab at Kennesaw State University flies autonomously using a grid search pattern. It sends live video and images to a ground station operated by the rescue team.
When the AI system finds a person, it analyzes images to determine whether the individual is upright or lying on the ground. It segments parts of the person’s body, identifying the person’s head and the body’s position. It then zeroes in on the forehead. It extracts forehead temperature readings, pixel by pixel, from the imaging data to estimate forehead temperature. We have two papers detailing these findings accepted for the American Institute of Aeronautics and Astronautics Aviation Forum 2026 conference.
Our AI model then assesses whether the person is conscious or unconscious and identifies abnormal temperatures that could indicate heat stress, hypothermia or other physical complications, or death – all vital information for a search-and-rescue team.
In field trials we have conducted, the system has provided consistent temperature readings of the heads of volunteers from our research team who have walked out into a variety of environments, under different conditions.
Why it matters
It is critical to get accurate and timely information on the whereabouts of a missing person. The likelihood that the person will survive decreases steeply as time passes.
An AI-enhanced drone can make search-and-rescue operations significantly more efficient than sending teams of people out into the environment to search on foot, especially in poor weather conditions or under thick foliage. Rescuers who know whether a person is conscious or unconscious can also better gear up for what they need to do to retrieve the person and administer aid. Our technology could save lives.
What other research is being done
Search-and-rescue personnel use various kinds of drones, but the machines often lack the ability to positively identify humans, especially under thick foliage, in bad weather or when the person is lying down or unconscious. The AI-based technology we have developed overcomes those challenges.
Better sensors that are very lightweight, that can function at night or in rain, and can see more clearly through thick foliage could further improve our drone and drones used by others. Researchers are devising AI-powered sound recognition for detecting screams for help, advanced thermal imaging for better nighttime vision and autonomous drones that could act as first responders.
Also under development are drones that can carry heavy payloads, such as flotation devices, fly for up to 14 hours or perform real-time mapping of the ground below.
What’s next
One of our next steps is to have multiple drones fly together and autonomously coordinate search-and-rescue operations among themselves. This will allow the technology to cover a much larger area, perhaps hundreds of square miles.
We are also designing a large drone that can carry up to 110 pounds (50 kilograms) of payload and stay aloft for an hour.
The Research Brief is a short take on interesting academic work.
Adeel Khalid, Professor of Industrial & Systems Engineering, Kennesaw State University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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