Internet of Things Inc (CVE:ITT) (OTCMKTS:INOTF) has revealed that its Weather Telematics Inc subsidiary is collaborating with IBI Group (TSE:IBG) to develop the Winter Ice and Snow Decision Support System for Operations Management (WISDOM) platform.

WISDOM is a resource management and decision support tool that uses artificial intelligence to assist in real-time responses to winter snow/ice road clearing operations. 

In a statement Thursday, Internet of Things said Weather Telematics and IBI have partnered with two Ontario counties to pilot the platform in the winter of 2020-21. They were brought together by the Ontario Centres of Excellence. 

Winter road maintenance accounts for a large portion of Canadian cities’ operating expenses, the company noted.  In 2018 alone, governments across North America spent upwards of $2 billion on winter operations.

With rapid population growth and more frequent extreme weather events, the costs of snow clearing are projected to increase further each year, prompting governments to analyze means to improve winter maintenance efficiency.

“We are extremely excited to be collaborating with IBI Group on the WISDOM platform,” Internet of Things CEO Michael Lende said in a statement. 

“Surprisingly, most governments rely on basic solutions to manage their winter maintenance and cleaning processes. WISDOM has the potential of dramatically improving the ability to manage government resources, significantly lower costs and improve driver safety.”

Derek Sims, IBI’s global director of intelligence said the platform “holds great promise in its ability to help governments better manage their winter snow/ice road clearing operations, and to lower their use of salt products without negatively impacting vehicle and pedestrian safety.”

The WISDOM platform consists of three interactive components:

• Real-time traffic and AI-predicted road conditions information which is used to route snowplows to designated areas efficiently, while predicted road conditions allow for more informed resource deployment in response to upcoming weather events
• Automation for the dynamic routing of appropriate vehicles to assist dispatchers
• Deployment of sensors on select vehicles to obtain ground-truth road condition data that feeds into the previous two components, bettering prediction accuracy and adjusting responses

Together, these components create a feedback loop that achieves precise dispatching of winter operation units and reduces excessive use of salt and de-icing materials, Internet of Things said.

The post Internet of Things says its Weather Telematics unit developing AI platform to assist in winter road clearing operations appeared first on Artificial Intelligence.

Most people have complained at least once about the quality of their local weather forecast. Unfortunately, predicting future weather is extremely difficult. The fact that meteorologists can do it as accurately as they do is incredible.

However, Google is working on a way to make predicting weather forecasts even better. It shared new research this week detailing how artificial intelligence (AI) can help create “nearly instantaneous” weather forecasts. Although the idea doesn’t have any real-world implications yet, researchers are hopeful that it will provide better predictions sometime soon.

Lightning Fast

Most people don’t know that today’s systems of creating forecasts can take hours. They must crunch massive amounts of data to provide meteorologists with complex readouts about future patterns and possibilities. When U.S. federal agencies run simulations to create forecasts, the calculations often include up to 100 terabytes of data and require the use of an expensive supercomputer.

Google software engineer Jason Hickey says, “If it takes 6 hours to compute a forecast, that allows only 3-4 runs per day and results in forecasts based on 6+ hour old data, which limits our knowledge of what is happening right now.”

Google’s AI forecast tool allows researchers to generate accurate rainfall predictions six hours ahead of when the precipitation occurs. Best of all, this takes the AI just “minutes” to complete.

The Big Tech firm says that quick forecasts are “an essential tool needed for effective adaptation to climate change, particularly for extreme weather.”

It also says that these rapid predictions will be important tools for “crisis management, and the reduction of losses to life and property.”

Considering the growing number of unpredictable weather events worldwide, having forecasts that don’t take hours to generate would certainly be useful. It would benefit those in the path of hurricanes, tornadoes, and even wildfires by being able to identify wind patterns.

NOAA’s Ark

Google trained its weather forecasting AI using radar data collected by the National Oceanic and Atmospheric Administration (NOAA) between 2017 and 2019. This simple data served as a proxy for making predictions about rainfall rather than analyzing complex physics patterns.

The company claims that its system is just as accurate or more accurate than three other methods of forecast prediction. However, when it tried to determine what was happening more than six hours ahead of time, it was outperformed.

So, it appears that AI weathermen aren’t going to solve every problem. While AI can likely be used as a way to provide quick, reliable short-term forecasts, NOAA’s more advanced models won’t be going anywhere just yet. Their 10-day forecasts are crucial for people planning events in advance and predicting dangerous weather situations.

Fortunately, AI weather prediction is a growing space. Other companies, including IBM and Monsanto, are also researching the concept. Meanwhile, Google will continue working with its AI forecasting system to push the limits of what is possible.

Although its uses may still be a few years off, Google has high hopes for the technology. It says, “Immediate forecasting is a key tool for real-time planning, facilitating decisions and improving lives.”

The post New Google AI provides ‘nearly instantaneous’ weather forecasts appeared first on Artificial Intelligence.

One of the most difficult challenges for artificial intelligence systems is predicting the weather – with an almost infinite number of variables to assess and model. But Google says its AI is making progress in the accuracy of its “nowcasts.”

Nowcasting is forecasting the weather in the very short term, over the next one or two hours. While a quick glance out of the window might give you some idea, knowing exactly when rain is about to start or stop (for example) can be incredibly useful for event planning, farming, disaster prevention and many other purposes.

Google has applied deep learning techniques, modeled on the neural networks of the human brain, to produce improved nowcasts for small areas (one sq km or 0.39 sq mi) over the course of an hour.

The new system, presented in a paper that has not yet been peer reviewed, is a significant step forward from existing prediction models, Google says. It can make accurate predictions in minutes rather than hours, which is much more useful when you want to know what the weather is going to do in the short-term.

That speed increase is achieved by ditching the heavy lifting of computationally intensive physics models, and replacing it with neural networks that are trained on historical data.

“A significant advantage of machine learning is that inference is computationally cheap given an already-trained model, allowing forecasts that are nearly instantaneous and in the native high resolution of the input data,” writes Jason Hickey, a Senior Software Engineer at Google Research, in a blog post.

Google’s AI nowcasts proved more accurate than three existing prediction techniques they were compared with, though the roles were reversed for forecasts more than six hours ahead – for now, this type of artificial intelligence processing is best suited for making short-term predictions in weather patterns.

AI also excels at chewing through – and making sense of – large amounts of data. The National Oceanic and Atmospheric Administration (NOAA) now collects nearly 100 terabytes of data per day from radar, satellites, weather stations and other sources.

One of the tricks Google’s system pulls is treating nowcasting as a computer vision problem, rather than trying to model real-world physics. Radar images are collated and analyzed using convolutional neural networks or CNNs – the same layered networks used to recognize objects in images and to interpret natural speech.

These CNNs are particularly good at achieving fast results with a high level of accuracy, as long as they’ve had enough training data, and the early signs are they could be very useful for nowcasting as well.

On a broader scale, knowing how weather conditions are shifting in real time is going to be crucial in assessing how climate change continues to impact the planet, and it seems AI is up to the challenge.

“As weather patterns are altered by climate change, and as the frequency of extreme weather events increases, it becomes more important to provide actionable predictions at high spatial and temporal resolutions,” write Google’s researchers in their paper.

The post Google’s latest AI innovations improve “nowcast” weather predictions appeared first on Artificial Intelligence.