Tools & Platforms

Tranquility, Carahsoft Bring TimePilot to Public Sector

Published

2 months ago

July 11, 2025

Tranquility AI and Carahsoft Technology have partnered to provide public sector access to advanced criminal justice technology.

Carahsoft said Thursday it will serve as a public sector distributor and make Tranquility AI’s TimePilot technology available to government agencies through its reseller partners and contract vehicles, including NASA Solutions for Enterprise-Wide Procurement V contracts and its Information Technology Enterprise Solutions-Software 2, National Association of State Procurement Officials ValuePoint and OMNIA Partners contracts.

What Is TimePilot?

TimePilot is an artificial intelligence-powered law enforcement technology platform. The investigative technology platform works to convert large volumes of disparate data, including surveillance footage, cell phone records and social media posts, into actionable insights.

The platform automates the creation of case timelines that correlate with evidence. It features an intelligent assistant that enables interactive engagement with case files. In addition, TimePilot features advanced analysis and report-writing capabilities and a proprietary language model that supports over 120 languages, including slang.

“Investigators and prosecutors have long been burdened by outdated technology and ever-expanding caseloads. Our flagship product, TimePilot, was designed to solve this problem by surfacing critical evidence in seconds and transforming complex data into clear, actionable insights that streamline case resolution,” said Jim Penrose, co-founder and CEO of Tranquility AI.

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Tools & Platforms

San Antonio Spa Unveils First AI-Powered Robot Massager

Published

1 hour ago

September 13, 2025

Name

In the heart of San Antonio, a quiet revolution in wellness technology is unfolding at Float Wellness Spa on Fredericksburg Road. The spa has become the first in the city to introduce the Aescape AI-powered robot massager, a device that promises to blend cutting-edge artificial intelligence with the ancient art of massage therapy. Customers lie face-down on a specialized table, where robotic arms equipped with sensors scan their bodies to deliver personalized treatments, adjusting pressure and techniques in real time based on individual anatomy and preferences.

This innovation arrives amid a broader surge in AI applications within the health and wellness sector, where automation is increasingly tackling labor shortages and consistency issues in human-delivered services. According to a recent feature by Texas Public Radio, the Aescape system at Float Wellness Spa uses advanced algorithms to map muscle tension and provide targeted relief, marking a significant step for Texas in adopting such tech.

Technological Backbone and Operational Mechanics

At its core, the Aescape robot employs a combination of 3D body scanning, machine learning, and haptic feedback to simulate professional massage techniques. Users select from various programs via a touchscreen interface, and the system adapts on the fly, much like a therapist responding to subtle cues. This isn’t mere gimmickry; it’s backed by years of development, with the company raising substantial funds to refine its precision.

In a March 2025 report from Bloomberg, Aescape secured $83 million in funding from investors including Valor Equity Partners and NBA star Kevin Love, underscoring investor confidence in robotic wellness solutions. The technology draws from earlier prototypes showcased at events like CES 2024, where similar AI-driven massage robots demonstrated personalized adaptations to user needs.

Market Expansion and Local Adoption in San Antonio

The rollout in San Antonio follows successful debuts in cities like Los Angeles, as detailed in a December 2024 piece by the Los Angeles Times, which described the experience as precise yet impersonal. At Float Wellness Spa, appointments are now bookable, with sessions priced competitively to attract a mix of tech enthusiasts and those seeking convenient relief from daily stresses.

Posts on X, formerly Twitter, reflect growing public intrigue, with users like tech influencer Mario Nawfal highlighting the robot’s eight axes of motion for deep-tissue work without the awkwardness of human interaction. This sentiment aligns with San Antonio’s burgeoning tech scene, where AI innovations are intersecting with local industries, as noted in recent updates from the San Antonio Express-News.

User Experiences and Industry Implications

Early adopters in San Antonio report a mix of awe and adjustment. One reviewer in a Popular Science article from March 2024 praised the Aescape for its customized convenience, likening it to “the world’s most advanced massage” powered by AI that learns from each session. However, some note the absence of human warmth, a point echoed in an Audacy video report from August 2025, which captured the robot’s debut turning heads in the city.

For industry insiders, this represents a pivot toward scalable wellness tech. With labor costs rising and therapist shortages persistent, robots like Aescape could redefine spa economics, potentially expanding to chains like Equinox. Yet, challenges remain, including regulatory hurdles for AI in healthcare-adjacent fields and ensuring data privacy for body scans.

Future Prospects and Competitive Dynamics

Looking ahead, Aescape’s expansion signals broader trends in robotic automation. A Yahoo Finance piece from August 2025 introduced a competing system, RoboSculptor, which also leverages AI for massage, hinting at an emerging market rivalry. In San Antonio, this could spur further innovation, with local startups like those covered in Nucamp’s tech news roundup exploring AI tools in customer service and beyond.

As AI integrates deeper into personal care, ethical questions arise—will robots supplant human jobs, or augment them? For now, Float Wellness Spa’s offering provides a tangible glimpse into this future, blending Silicon Valley ingenuity with Texas hospitality. Industry watchers will be keen to monitor adoption rates, as success here could accelerate nationwide rollout, transforming how we unwind in an increasingly automated world.

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Tools & Platforms

AI drone swarms revolutionize wildfire detection and air quality monitoring

Published

1 hour ago

September 13, 2025

The Editors

From the outside, wildfire smoke may look like a drifting gray cloud. But for scientists, these plumes are dynamic, complex, and potentially dangerous. They can stretch for hundreds of miles, impacting air quality, visibility, and public health. Until now, capturing accurate data on how these smoke particles move and behave has been one of the most difficult tasks in atmospheric science.

Researchers at the University of Minnesota Twin Cities have developed a groundbreaking way to observe and analyze wildfire smoke: a swarm of AI-powered aerial robots that can detect, track, and build 3D models of smoke plumes.

Unlike traditional drones, these small flying machines work as a team. They recognize smoke, fly directly into it, and take high-resolution images from multiple angles. Their mission is to help us better understand how smoke travels—an understanding that could reshape how we predict air pollution and respond to environmental hazards.

This new study, published in the peer-reviewed journal Science of the Total Environment, opens doors to more accurate fire behavior models and better air quality predictions, not just for wildfires, but also for prescribed burns, volcanic eruptions, sandstorms, and other particle-driven events.

A graphical abstract of the study. (CREDIT: Science of The Total Environment)

A Growing Crisis Meets High-Tech Tools

Between 2012 and 2021, about 50,000 prescribed burns were carried out in the United States—intentional fires set under controlled conditions to improve forest health and reduce wildfire risk. But even controlled burns carry risk. According to a 2024 report by the Associated Press, 43 of these burns spiraled out of control and became wildfires.

These numbers, while small in percentage, matter deeply. That’s because smoke particles, especially the small ones, can stay in the air for days and travel far from their source. “A key step is understanding the composition of smoke particles and how they disperse,” explained Jiarong Hong, professor of mechanical engineering at the University of Minnesota and senior author of the study. “Smaller particles can travel farther and stay suspended longer, impacting regions far from the original fire.”

Understanding how these plumes evolve over time is essential for early hazard detection, public health responses, and emergency planning. Yet, traditional tools for studying smoke—like satellites, remote sensing, and Lidar—fall short. These tools lack the detail and flexibility needed to capture fast-changing flows of smoke, especially in rough terrain or remote regions.

The Science Behind the Swarm

The team’s drone system includes one manager drone and four worker drones. These drones aren’t just fancy flying cameras—they’re mini laboratories in the sky.

Each drone carries a 12-megapixel camera mounted on a three-axis gimbal for capturing smoke in motion. They are powered by long-lasting 6000 mAh batteries and guided by advanced flight controllers and NVIDIA Jetson processors. These processors allow the drones to recognize smoke in real time, adjust their paths, and capture the best angles for imaging.

When launched, the drones work together to fly around a smoke plume, snapping high-resolution images from multiple directions. These images are then grouped by time intervals and fed into a computer model using something called a Neural Radiance Field (NeRF). This advanced AI model helps turn 2D images into a realistic, detailed 3D reconstruction of the smoke plume.

Illustration of the drone swarm system that uses multi-view imaging for 3D smoke plume characterization. (CREDIT: Science of The Total Environment)

This step is key. With the 3D model, researchers can analyze the shape, direction, and flow of the smoke over time. It gives them crucial data like volume, angle of movement, and dispersion speed—all critical for improving fire and smoke simulation tools.

Other cutting-edge AI techniques were considered, including Dynamic NeRF (D-NeRF) and RoDynRF, which are good at modeling motion. But these systems struggle with featureless subjects like smoke and require long training times. The drone swarm approach avoids those problems by directly capturing the data in the field.

“This approach allows for high-resolution data collection across large areas—at a lower cost than satellite-based tools,” said Nikil Nrishnakumar, the study’s first author and a graduate researcher at the Minnesota Robotics Institute.

From Research to Real-World Impact

The drone swarm has already been tested in field deployments and has shown promising results. With this system, the team can generate multiple 3D reconstructions over time, creating a time-lapse view of how a smoke plume changes in real-time. It’s like watching the plume evolve in 3D—a powerful tool for scientists and emergency responders.

Drone hardware configuration showing the quadcopter with camera mounted on a 3-axis gimbal and GPS with RTK (left), and the NVIDIA Jetson Orin Nano (right). (CREDIT: Science of The Total Environment)

But the benefits of this technology reach far beyond wildfire science.

“Early identification is key,” Hong emphasized. “The sooner you can see the fire, the faster you can respond.”

The drones could be used in other dangerous scenarios as well, including volcanic eruptions, dust storms, and even urban pollution events. Because the system is modular and cost-effective, it can be scaled up or down based on the size of the area being studied. This flexibility makes it a strong candidate for use by government agencies, environmental researchers, and emergency crews.

The next steps for the team involve making the system more autonomous and scalable. They’re now integrating fixed-wing drones with Vertical Takeoff and Landing (VTOL) capability. These new drones can fly longer distances—over an hour at a time—and don’t need a runway to take off. That opens the door to monitoring vast forests and hard-to-reach locations.

In addition, the team plans to explore Digital Inline Holography to improve particle characterization. This method could provide even deeper insights into what types of particles are present in a smoke plume and how they interact with the environment.

“We’re not just building tools,” Nrishnakumar said. “We’re laying the groundwork for smarter, faster, and safer responses to environmental hazards.”

Flowcharts detailing the steps involved in (a) stabilizing the manager drone, (b) collecting data with the worker drone swarm, and (c) processing captured data for 3D plume reconstruction and characterization. (CREDIT: Science of The Total Environment)

A New Era of Smoke Science

Many modern simulation tools like FIRETEC and QUIC-Fire already exist to model how fires spread and how smoke particles behave. These systems use complex inputs—everything from fuel type and moisture to wind speed and topography. But even the best models have one major limitation: they need real-world data to validate their predictions.

That’s why the drone swarm matters so much. It provides the missing piece—real, time-sensitive, high-resolution data that can make these simulations more accurate and useful.

Until now, simulation models have struggled to work in areas without detailed 3D maps of vegetation and terrain. They also haven’t been able to compare their predictions with real-world smoke movement, especially in complex or fast-changing environments. The drone swarm changes that by creating accurate 3D ground truth models that can be used for comparison and refinement.

As the climate warms and wildfire risks rise, these tools may become vital to protecting both ecosystems and human health. With more than 40% of the U.S. population living in areas prone to wildfire smoke, this research couldn’t come at a better time.

This project was supported by the National Science Foundation’s Major Research Instrumentation program and conducted with the help of the St. Anthony Falls Laboratory. Along with Hong and Nrishnakumar, the research team included Shashank Sharma and Srijan Kumar Pal, also from the Minnesota Robotics Institute.

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Tools & Platforms

Open-Source AI Rivaling OpenAI and DeepSeek

Published

2 hours ago

September 13, 2025

Name

In a bold move to assert its presence in the global artificial intelligence arena, the United Arab Emirates has unveiled K2 Think, an open-source AI model designed to challenge heavyweights like China’s DeepSeek and OpenAI’s offerings. Developed by the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in collaboration with the tech firm G42, this model emerges from Abu Dhabi’s Institute of Foundation Models. With just 2.5 billion parameters, K2 Think punches above its weight, delivering reasoning capabilities that rival much larger systems, according to benchmarks cited in recent reports.

The launch, announced earlier this month, underscores the UAE’s strategic pivot away from oil dependency toward tech innovation. Researchers at MBZUAI claim K2 Think achieves competitive scores in key areas such as mathematical reasoning and code generation, often matching or exceeding models like DeepSeek’s V3.1, which has been hailed for its efficiency on Chinese hardware. This development comes amid intensifying competition, where nations vie for AI supremacy through accessible, cost-effective tools.

A Compact Powerhouse in AI Reasoning

What sets K2 Think apart is its emphasis on efficiency. Unlike resource-intensive models from U.S. giants, this Emirati creation runs on modest hardware, making it ideal for deployment in resource-constrained environments. As detailed in a CNBC article published on September 9, the model was trained using a novel approach that optimizes for speed and sustainability, potentially reducing energy costs by up to 70% compared to peers.

Industry experts note that K2 Think’s open-source nature democratizes access, allowing developers worldwide to fine-tune it for specific applications. This contrasts with proprietary systems like OpenAI’s o1-mini, which, while advanced, remain locked behind paywalls. Posts on X, formerly Twitter, from tech influencers have buzzed with excitement, highlighting how the UAE’s entry could accelerate innovation in regions underserved by Western tech.

Strategic Implications for Global AI Dynamics

The UAE’s foray into open-source AI isn’t isolated; it’s part of a broader ecosystem bolstered by investments from Microsoft-backed G42. A report from The National on September 9 emphasizes that K2 Think signals the country’s readiness to compete in a field dominated by the U.S. and China. DeepSeek, for instance, recently announced plans for an AI agent by year’s end, as per a Bloomberg piece dated September 4, intensifying the race.

For industry insiders, the real intrigue lies in K2 Think’s potential to foster AI sovereignty. By releasing the model under an open license, the UAE invites collaboration, potentially sparking a wave of localized adaptations. This mirrors China’s strategy with DeepSeek, which optimized for domestic chips and undercut costs, as noted in a Fortune analysis from August 21.

Challenges and Future Prospects

Yet, challenges remain. Critics point out that while K2 Think excels in reasoning tasks, it may lag in creative or multimodal capabilities compared to larger models. A Slashdot discussion from September 13 highlights community debates on its scalability, with some users questioning long-term support.

Looking ahead, the UAE’s investment in AI education and infrastructure, including MBZUAI’s programs, positions it for sustained growth. As Euronews reported on September 10, this model could redefine low-cost AI, encouraging a multipolar tech world where emerging players like the UAE challenge established powers.

Economic Diversification Through Tech Innovation

Economically, K2 Think aligns with the UAE’s Vision 2031, aiming to build a knowledge-based economy. Partnerships with global firms ensure technology transfer, while open-sourcing mitigates risks of over-reliance on foreign AI. X posts from AI enthusiasts, such as those praising DeepSeek’s cost efficiencies, underscore a sentiment that the UAE’s model could similarly disrupt markets.

In essence, K2 Think represents more than a technical achievement; it’s a geopolitical statement. As nations like China advance with models like DeepSeek’s upcoming agent, per recent Bloomberg insights, the UAE’s agile approach may inspire others to follow suit, fostering a more inclusive AI future.

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