Connect with us

AI Insights

WMO supports Artificial Intelligence forecasting pilot in Africa

Published

on


Use of AI in meteorological modelling has recently demonstrated the ability to produce state-of-the-art predictions with relatively small computational power. Thus, there is growing interest in leveraging AI technology to help countries without sophisticated super-computers to “leapfrog” to the latest most advanced prediction systems.

WMO’s Executive Council recently set up a new Joint Advisory Council on Artificial Intelligence to inform WMO activities and to balance opportunities and challenges. 

WMO is therefore excited about a new pilot project in Malawi, with funding from the Climate Risk and Early Warning Systems (CREWS) initiative. It will test the ground in leveraging a state-of-the-art AI-based Weather Prediction (AI-WP) system to improve the accuracy, timeliness, and accessibility of weather predictions in Malawi.

It aims to empower Malawi’s Department of Climate Change and Meteorological Services (DCCMS) to build operational capacity in AI-WP and early warning provision, and to evaluate how an AI-WP system can help in closing critical capacity gaps in Malawi and more generally in Least Developed Countries and Small Island Developing States.

The project builds on a high-resolution data-driven weather forecasting model named Bris developed by MET-Norway and the “forecast-in-a-box” concept developed by the European Centre for Medium-Range Weather Forecasts (ECMWF).

Through this pilot, meteorologists in Malawi will gain hands-on experience running AI-enabled forecasts locally, assessing their operational feasibility, forecast skill, and potential to support timely early warnings for high-impact weather events. 

Malawi – like many African Least Developed countries – is highly vulnerable to climate-related hazards. Its early warning systems face significant gaps due to limited observational infrastructure, constrained human resources, and outdated forecasting systems. 

“We firmly believe this initiative represents a strategic opportunity to strengthen Malawi’s early warning infrastructure, deliver actionable insights, and support long-term capacity development for our forecasting staff,” said Lucy Mtilatila, director for Climate Change and Meteorological Services and permanent representative of Malawi to WMO.

The project – which combines Norway’s AI expertise with Malawi’s local knowledge and data – was presented by Roar Skålin, permanent representative of Norway to WMO at the High-Level Open Consultative Platform on AI during the week of the WMO Executive Council. 

Forecast-in-a-Box

It is being launched in collaboration with the European Centre for Medium Range Weather Forecasing (ECMWF) which is developing early prototypes of Artificial Intelligence/Integrated Forecasting System (AIFS) packaged as a Forecast-in-a-Box as part of the AI-driven solutions of the Digital Twin Engine and the Destination Earth initiative of the European Commission. 

Data-driven models such as ECMWF’s AIFS and MET Norway’s Bris are fundamentally different from traditional numerical weather prediction systems. They are lighter, faster, and more portable, making them well suited to run outside large high-performance computing (HPC) infrastructures. 

This set-up enables to run forecasts closer to where the data is needed, offering key benefits: 

  • Users can tailor the forecasting pipeline to their specific needs.
  • Improved responsiveness and timeliness.
  • Deployment across a range of environments.
  • No deep expertise in system setup or infrastructure is needed.

WMO Integrated Processing and Prediction System 

Despite the huge possibilities, there question marks about the capability of AI to support forecasts and warnings of local high-impact weather and water hazards.  The WMO’s Executive Council therefore requested the development of technical guidelines on the use of AI-based technologies and how incorporate AI into the WMO Integrated Processing and Prediction System (WIPPS).  This is the worldwide network of operational centres of WMO’s Members and is the backbone of all forecasting.  

The planned Joint Advisory Group will be a coordination mechanism among WMO’s Infrastructure and Services Commissions, Research Board and other relevant WMO bodies. It will include experts from the public, private and academic sectors and will steer joint efforts to explore the opportunities and challenges of AI/ML technology.



Source link

AI Insights

Artificial Intelligence Cracks One of Archaeology’s Biggest Puzzles in History That Defied Experts for Decades

Published

on


In a discovery that’s turning heads across the archaeological world, researchers have used artificial intelligence to uncover 303 previously unknown Nazca geoglyphs in the Peruvian desert, nearly doubling the number of documented ancient figures etched into the arid landscape.

The findings, detailed in a peer-reviewed study published in PNAS, mark a major leap forward in the study of the enigmatic Nazca culture and suggest a far more complex ceremonial and social use of these sprawling ground drawings than previously thought.

The project, a collaboration between Yamagata University in Japan and IBM Research, relied on deep learning to scan over 629 square kilometers of high-resolution aerial and drone imagery. The AI system, trained on a relatively small dataset of known geoglyphs, was able to detect faint, shallow, and weathered relief-type figures—many as small as 9 meters across—that have eluded human researchers for decades.

“This technology has allowed us to condense nearly a century of archaeological progress into just six months,” said Professor Masato Sakai, lead archaeologist at Yamagata’s Institute of Nazca.

The Overlooked Geoglyphs That Reshaped Archaeological Thinking

Unlike the more famous line-type Nazca geoglyphs—large stylized animals like monkeys, hummingbirds, and whales that stretch up to 90 meters and were first studied from the air in the early 20th century—the newly discovered figures belong mostly to the lesser-known relief-type category.

These smaller figures, meticulously outlined by removing surface stones to expose the lighter earth beneath, depict a range of human-related motifs: humanoids, decapitated heads, and domesticated animals like camelids. In fact, over 80% of the new finds depict human-modified subjects, in stark contrast to the wildlife-centric themes of the larger geoglyphs.

Nazca Lines, Peru, South America
Nazca Lines, Peru, South America. Credit: Wikimedia Commons

Crucially, these relief-type geoglyphs are often located within 43 meters of ancient foot trails, suggesting they were designed to be viewed by individuals or small groups traveling across the Nazca Pampa—not by aerial observers or large congregations. This supports earlier hypotheses proposed by German mathematician and Nazca researcher Maria Reiche, who posited that many geoglyphs were tied to ritual processions.

By contrast, the massive line-type figures tend to cluster around linear and trapezoidal paths, believed to be part of community-wide ceremonial networks. These findings lend weight to the idea that Nazca geoglyphs served a dual-purpose landscape: intimate, localized rituals and broader, communal pilgrimage activity.

AI’s Role in Rewriting Ancient Narratives

The AI’s success in detecting such difficult-to-spot figures came down to clever engineering and a bit of patience. Because of the limited training data—just over 400 known geoglyphs at the time—researchers fine-tuned a model pre-trained on conventional photographs, enhancing it with custom algorithms that scanned the imagery in 5-meter grids. A geoglyph probability map was then generated, helping archaeologists prioritize field surveys.

Ai Nazca LinesAi Nazca Lines
The Nazca Lines in the Peruvian desert showing a geoglyph representing a hummingbird. Credit: ALAMY

The team manually examined over 47,000 AI-flagged image boxes, spending more than 2,600 labor hours on screening and field verification. The payoff was significant: 303 new figurative geoglyphs confirmed between September 2022 and February 2023, alongside 42 new geometric figures and dozens of new groupings not previously documented.

This approach also revealed that many geoglyphs cluster in narrative scenes—for example, humanoids interacting with animals or symbolic decapitation motifs—further supporting the idea that the Nazca used these trails and figures to transmit cultural memory and ritual significance through motion and space.

“AI doesn’t replace the archaeologist,” said Dr. Alexandra Karamitrou, an AI researcher at the University of Southampton not involved in the study. “But it radically expands what’s possible, especially in places as vast and harsh as the Peruvian desert.”

Cultural Heritage Under Threat and a Race Against Time

This technological advance comes at a pivotal moment. The Nazca geoglyphs, designated a UNESCO World Heritage Site, face growing threats from climate change, unauthorized vehicle incursions, and flash flooding—phenomena becoming more frequent in the desert due to shifting weather patterns.

The Nazca LinesThe Nazca Lines
Credit: University of Yamagata

Preserving these fragile expressions of ancient Andean culture is now as much about data as it is about dirt. The AI-assisted survey not only improves the mapping of known figures but also highlights potential hot spots for future discoveries, many of which lie just beneath the surface of satellite scans.

With roughly 1,000 AI-flagged candidate sites still awaiting verification and many trails only partially mapped, researchers expect hundreds more figures may remain undiscovered. If so, we’re only beginning to grasp the cultural sophistication of a civilization that, over 1,500 years ago, etched stories into stone—not for us, but for the gods, the landscape, and each other.



Source link

Continue Reading

AI Insights

Poll: Do you think artificial intelligence is going to put your job / career at risk?

Published

on


Artificial Intelligence is everywhere, and we seemingly can’t escape.

I’ve never (and will never) use AI to write articles on Windows Central, beyond perhaps using Copilot to quickly check the specs on a product I’m reviewing — but even that often requires additional review, due to the hallucinations AI seems prone to. It seems like we might be increasingly in the minority, though.



Source link

Continue Reading

AI Insights

Vikings vs. Falcons props, picks, SportsLine Machine Learning Model AI predictions: Robinson over 65.5 yards

Published

on


Week 2 of Sunday Night Football will see the Minnesota Vikings (1-0) hosting the Atlanta Falcons (0-1). J.J. McCarthy and Michael Penix Jr. will be popular in NFL props, as the two will face off for the first time since squaring off in the 2023 CFP National Title Game. The cast of characters around them has changed since McCarthy and Michigan prevailed over Washington, as the likes of Bijan Robinson, Justin Jefferson and Aaron Jones now flank the quarterbacks. There are several NFL player props one could target for these star players, or you may find value in going after under-the-radar options.

Tyler Allgeier had 10 carries in Week 1, which were just two fewer than Robinson, with the latter being more involved in the passing game with six receptions. If Allgeier has a similar type of volume going forward, then the over for his rushing yards NFL prop may be one to consider. A strong run game would certainly help out a young quarterback like Penix, so both Allgeier and Robinson have intriguing Sunday Night Football props. Before betting any Falcons vs. Vikings props for Sunday Night Football, you need to see the Vikings vs. Falcons prop predictions powered by SportsLine’s Machine Learning Model AI.

Built using cutting-edge artificial intelligence and machine learning techniques by SportsLine’s Data Science team, AI Predictions and AI Ratings are generated for each player prop. 

For Falcons vs. Vikings NFL betting on Sunday Night Football, the Machine Learning Model has evaluated the NFL player prop odds and provided Vikings vs. Falcons prop picks. You can only see the Machine Learning Model player prop predictions for Atlanta vs. Minnesota here.

Top NFL player prop bets for Falcons vs. Vikings

After analyzing the Vikings vs. Falcons props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model says Falcons RB Bijan Robinson goes Over 65.5 rushing yards (-114 at FanDuel). Robinson ran for 92 yards and a touchdown in Week 14 of last season versus Minnesota, despite the Vikings having the league’s No. 2 run defense a year ago. After replacing their entire starting defensive line in the offseason, it doesn’t appear the Vikings are as stout on the ground. They allowed 119 rushing yards in Week 1, which is more than they gave up in all but four games a year ago.

Robinson is coming off a season with 1,454 rushing yards, which ranked third in the NFL. He averaged 85.6 yards per game, and not only has he eclipsed 65.5 yards in six of his last seven games, but he’s had at least 90 yards on the ground in those six games. Over Minnesota’s last eight games, including the postseason, six different running backs have gone over 65.5 rushing yards, as the SportsLine Machine Learning Model projects Robinson to have 81.8 yards in a 4.5-star prop pick. See more NFL props here, and new users can also target the FanDuel promo code, which offers new users $300 in bonus bets if their first $5 bet wins:

How to make NFL player prop bets for Minnesota vs. Atlanta

In addition, the SportsLine Machine Learning Model says another star sails past his total and has five additional NFL props that are rated four stars or better. You need to see the Machine Learning Model analysis before making any Falcons vs. Vikings prop bets for Sunday Night Football.

Which Vikings vs. Falcons prop bets should you target for Sunday Night Football? Visit SportsLine now to see the top Falcons vs. Vikings props, all from the SportsLine Machine Learning Model.





Source link

Continue Reading

Trending