Future beyond Chatbots: Huawei focuses on AI's Impact and Weather

HUAWEI CLOUD Releases Pangu 3.0

Published: Jul. 10, 2023 at 2:32 AM EDT

Note specifics on weather forecasting

Shaping the Future beyond Chatbots: Huawei focuses on AI's Impact on Industry Transformation

DONGGUAN, China, July 10, 2023 /PRNewswire/ -- Huawei is bringing forth new industry opportunities and technological advancements driven by the AI wave. During his keynote speech at Huawei's Developer Conference, Zhang Ping'an, Huawei Executive Director and CEO of HUAWEI CLOUD, unveiled the Pangu Model 3.0 and Ascend AI cloud services. These innovations aim to empower industry customers and partners and unlock the potential of artificial intelligence for transformative growth across a range of sectors.

Pangu Models 3.0 is a system of pre-trained models that can be quickly adapted to meet scenario-specific needs and address complex challenges across multiple industries. By leveraging large-scale data sets and machine learning algorithms, Pangu 3.0 is set to revolutionize the industrial application of AI in diverse areas such as weather forecasting, drug development, fault identification in trains, and the mining industry.

Mr. Zhang said: "Huawei Cloud Pangu models will empower everyone from every industry with an intelligent assistant, making them more productive and efficient. We will uphold our mission of "AI for Industries", and use Pangu models to reshape all industries with AI."  ,,, ' 

Digital Twin for Intense Weather

Interesting and clever approach, if there is enough detail in the model being used.  How much is required for the purpose? 

ACM TECHNEWS

Digital Twin for Intense Weather Gives Scientists 'Control Loop'

By ZDNet, February 10, 2023

The work allows for a digital twin of the real world, allowing scientists to make predictions and see the effects of interventions in a kind of control loop.

Computer maker Cerebras used its AI computer on a non-AI problem: simulating "buoyancy-driven Navier-Stokes flows" that capture dynamics of many systems in nature and the built environment.

Credit Cerebras/DoE NETL 2023

Scientists at artificial intelligence computing developer Cerebras and the U.S. Department of Energy's National Energy Technology Laboratory (NETL) said they can model extreme weather by accelerating field equations.

Said Cerebras' Andrew Feldman, "This is a real-time simulation of the behavior of fluids with different volumes in a dynamic environment."

He explained this digital twin of real-world conditions basically enables a "control loop" for manipulating reality.

Cerebras' CS-2 supercomputer can model the Rayleigh-Bénard convection process caused by fluids being heated from the bottom and cooled from the top, while the Cerebras-NETL Wafer Scale Engine field equation application programming interface describes scientific equations.

The partners said, "The simulation is expected to run several hundred times faster than what is possible on traditional distributed computers, as has been previously demonstrated with similar workloads."

From ZDNet

View Full Article  

DeepMind & Google’s ML-Based GraphCast

Did a quick updated look,  hard to get a useful error track of forevast.

DeepMind & Google’s ML-Based GraphCast Outperforms the World’s Best Medium-Range Weather Forecasting System  in Medium.com

Medium-range weather forecasts play a crucial role in agriculture, construction, travel and other industries. They also bring practical value to people’s daily lives, enabling us to plan outings and keeping us safe from extreme weather events. Traditional numerical weather prediction (NWP)-based forecasting models that run simulations on computing clusters however do not scale efficiently with today’s increasing weather data availability, and their accuracy relies on manual input from experts, which is time-consuming and cost inefficient.

In the new paper GraphCast: Learning Skillful Medium-Range Global Weather Forecasting, a research team from DeepMind and Google presents GraphCast, a machine-learning (ML)-based weather simulator that scales well with data and can generate a 10-day forecast in under 60 seconds. GraphCast outperforms the world’s most accurate deterministic operational medium-range weather forecasting system and all existing ML-based benchmarks. ... '

New Supply Chain Mindset and Examples

 Have just reviewed a number of storm and weather related supply chain examples.  

The New Supply Chain Mindset: From Just-in-Time to Just-In Case

EVER GIVEN SUEZ CANAL STUCK SUPPLY CHAIN DISRUPTION OLD.jpg

November 15, 2022

Valerie Tardif, SCB Contributor

Many of those in supply chain management learned early on the principles of just-in-time (JIT) manufacturing. Inspired by the work of Taiichi Ohno at Toyota Motor Co., the JIT revolution tightly coupled production and demand, resulting in little or no work-in-process inventory. Subsequently, it went on to change how supply chains ran. JIT fostered demand-driven, agile and lean processes, and contributed to breaking down functional silos inside an organization.

The adoption of JIT principles resulted in more than four decades of economic prosperity for global manufacturing and distribution. Yet much of that success relied on enabling factors that were existing at the time. To anticipate customer demand, companies relied on past sales data. They also assumed that customers would act rationally, with trade-offs that could be easily evaluated, and would respond logically to incentives.

To reduce inventory, companies depended on predictable transportation, with fast loading and unloading at ports around the world, and high availability of road, rail, barge and final-mile transportation. They were able to synchronize supply and demand by relying on abundant raw materials and components flowing through integrated supply chains, dedicated suppliers and manufacturing capacity.

Many of those assumptions no longer hold. COVID-19 has changed sales patterns and created holes in historical data. The new generation of consumers — digitally native, socially and eco-conscious — is changing demand patterns, making them more unpredictable than ever before.

This new world brings fresh opportunities to rethink how we plan and run supply chains. Succeeding under these conditions begins with three simple changes.

Adopt a probabilistic mindset. Probabilistic modeling is a technique that factors possible events or actions and their probabilities. Probabilistic planning involves predicting future outcomes and making plans that lead to desired cost and benefits. The most common example is a probabilistic demand forecast that outlines expected demand, while including a confidence interval or a probability model around it.

In fact, many parameters in planning should be modeled probabilistically. Lead times in master data are often inflated to represent worst-case figures. Planning leaders should demand that their systems model all the variability around lead times to factor the impact of shipments arriving early or late. Think beyond a future described by “one number,” and take in the wide range of potential outcomes.  ... ' 

Next Generation Weather Reporting/Prediction

 We now all have increasingly good phone based weather systems.  Adding better sensors.

Next-Generation Weather Reporting: Versatile, Flexible, Economical Sensors, By Osaka Metropolitan University (Japan)

May 20, 2022

The versatile, flexible sensor sheet measures the electrical resistance generated when raindrops hit its surface at different wind speeds, and provides sensor data, which is analyzed through reservoir computing.A team of scientists from Japan's Osaka Metropolitan University (OMU) and University of Tokyo have developed a multitasking weather sensor that measures rain volumes and wind speeds.

 The lightweight sensor sheet incorporates machine learning reservoir computing to analyze the output data, and can rapidly deliver localized weather data.

The sensor quantifies rain volume by measuring the electrical resistance produced by raindrop impacts, and derives wind speed measurements from water droplet behavior. The sensors detect resistance changes triggered by shifting rain and wind conditions, then record them as time-series data; the researchers fed this data to the machine, which yielded rain volume and wind speed data.

"The findings open up a promising economical approach to weather reporting, contributing to disaster preparedness and greater community safety," said OMU's Kuniharu Takei.

From Osaka Metropolitan University (Japan)

View Full Article    

IBM Tracks Virus 'Weather'

A nicely done Covid-19 tracking app is part of the IBM 'Weather Channel' App.   Shows location and trends continually updated, based on your location.  Nicely shown on the bottom of the App with a red button to click.  With other virus news and video.  I see the IBM CEO talks about it below.  I am following on my smartphone.

What are the best ways to make this influence behavior?    Some sort of simple behavior-effect prediction?

Later I noted that the warning included: (Some locations do not currently provide all data).  So we have the classic problem of incomplete and even faulty data.

IBM CEO: Covid-19 tracking app can help modify behavior
Ginni Rometty, CEO of IBM, explains what the company is doing to help during the coronavirus crisis. It launched a tool on its Weather Channel app that tracks the outbreak. .... 

Read in CNN Business: https://apple.news/A_YGufs01RRiCOpb8GNl2eg\

Ground Penetrating Radar for Driving?

This is unexpected.   But it has been known that weather is a problem with classic methods, now will ground penetrating radar solve this problem?

MIT’s Ground-Penetrating Radar Looks Down for Perfect Self-Driving    by Bill Howard

Ground-penetrating radar may soon be the sensor that makes your car autonomous in all weather conditions. It turns out that when you scan the 10 feet below the roadway surface, you get a unique identifier that is accurate to an inch or two. Mapping cars would scan the roadways once, then your self-driving car with its own ground-penetrating radar would rescan as you drive, matching its real-time scan to the master map. That would keep your car centered, even if pavement markings are covered by snow or ice, according to WaveSense, an MIT spinoff that already has already tested military applications.

Ground-penetrating radar can’t be the only sensor in a self-driving car. An autonomous car still needs surface radar, possibly lidar, and cameras to track other vehicles, pedestrians, animals, blocked lanes, and cars stopped or crashed in travel lanes. But it has the potential to be the breakthrough that allows bad-weather autonomous driving.  .... " 

Predicting Lightning: Where/When

Skeptical.   If we are talking about predicting the likelihood of lighting in an area an hour before it hits, thats done already in weather predictive maps.    Suggests here that the accuracy is within a 30 km radius.   Specifics of goal are slightly incomplete,  but perhaps that is not being done that accurately yet.  "When it may strike", versus when/where?   AI aspect may well using machine learning in an area that might attract lightning such as tall trees or buildings

Using AI and machine learning to forecast lightning  By Techcrunchx 

As one of the most irregular phenomena in nature, lightning is very disturbing. Scientists have lately made an AI system that forecasts lightning up to 30 minutes before it strikes.

Lightning regularly kills animals and people, initiates fires, destroys power lines and keeps aircraft stranded. Till now, it has been almost out of the question to predict lightning, with no simple technology for predicting where and when it will strike the earth.

Engineers at the Ecole Polytechnique Federale de Lausanne’s (EPFL) School of Engineering built a simple and cheap system to forecast when lightning will strike. Farhad Rachidi led the research, which resulted in a technique of predicting lightning between 10 and 30 minutes before it hits, inside a 30km radius.  ....  "

IBM Wants to Change Weather Forecasting

Wondered Where IBM was Taking their big Weather Investment.  I note that American Airlines is testing some of this.

IBM hopes to change weather forecasting around the globe in CNBC
 a model offering high resolution forecasts globally with detail as small as 2 miles wide.

The system is called GRAF, or Global High Resolution Atmospheric Forecasting, and will have many applications globally for governments and industries including airlines, agriculture and retail.

GRAF will issue 12 trillion pieces of weather data every day and process forecasts every hour.

IBM became a leading player in meteorology when it purchased the Weather Company a few years ago, retaining the data gathering and forecasting units while splitting off the Weather Channel.   ... " 

A New LIDAR Claims Better all Weather Vision

LiDAR Sensing was often criticized for not working in rain or snow.  Now I see that Draper Apollo has announced that they have a Lidar that solves this.  Progress continues.

New LiDAR Detector Enables Self-Driving Cars to See in Sun, Rain, Fog and Snow

CAMBRIDGE, MA—As vision technologies improve and automakers begin testing and integrating the new technologies into their cars, self-driving cars are poised to see the road around them better. Draper recently tested its Hemera LiDAR detector and set a new performance standard for being able to see more objects in all weather conditions.

LiDAR detectors tend to go blind when they encounter too much light or too many obscurants, like rain, snow and fog. Draper’s Hemera advances the science by fusing technologies from biomedicine, optics and signal processing with advanced technologies, such as silicon photonics and proprietary algorithms, to produce an architecture that’s designed to enhance commercially available LiDARs. ....  "

Space Weather Implications for Technology

Space weather, and further implications of solar activity can have huge implications for tech on earth.  The ability to monitor it  will be important.  NASA's launch last week of the ICON spacecraft from an aircraft is an example of efforts underway.  The sun's Coronal Mass Ejections (CMEs)  have the ability to set our tech back for many years.  Continuing to follow.

NASA uses a plane to launch a craft to the very edge of space  By Georgina Torbet in Digitaltrends

NASA has launched a new spacecraft, the Ionospheric Connection Explorer (ICON), for exploring the radiation-filled and inhospitable border between our planet’s atmosphere and space. On Thursday, the ICON spacecraft was carried aboard a Northrop Grumman Stargazer L-1011 aircraft that took it to an altitude of 39,000 feet before being deployed on a Northrop Grumman Pegasus XL rocket, whose automated systems launched it into space.  ... ' 

Further tech detail in The Verge.

Predicting Severe Weather

I like this experiment because there is so much data gathered and involved,  and many current models, so it should be easy to do good comparisons of methods. I note that the emphasis may be on short term forecasting.

Machine learning and its radical application to severe weather prediction  by Eric Verbeten, University of Wisconsin-Madison

In the last decade, artificial intelligence ("AI") applications have exploded across various research sectors, including computer vision, communications and medicine. Now, the rapidly developing technology is making its mark in weather prediction.

The fields of atmospheric science and satellite meteorology are ideally suited for the task, offering a rich training ground capable of feeding an AI system's endless appetite for data. Anthony Wimmers is a scientist with the University of Wisconsin–Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS) who has been working with AI systems for the last three years. His latest research investigates how an AI model can help improve short-term forecasting (or "nowcasting") of hurricanes.

Known as DeepMicroNet, the model uses deep learning, a type of neural network arranged in "deep" interacting layers that finds patterns within a dataset. Wimmers explores how an AI system like DeepMicroNet can supplement and support conventional weather prediction systems.  .... " 

IBM Predicts Power Disruptions

Intriguing application, one that is using data from IBM's acquisition.     Not much detail here, except for Watson and Weather data.    Will edit in later findings.   See this from the Weather Company, part of IBM:  https://business.weather.com/products/outage-prediction   And more technical.

IBM develops new technology to help prevent power outages in Reuters

LONDON (Reuters) - IBM Corp. has developed technology to predict and monitor when and where trees and vegetation threaten power lines which could help improve power supply operations and reduce outages, it said on Wednesday.

Vegetation can cause disruption for energy companies, often growing over or obstructing power transmission lines. Energy suppliers usually deal with this by conducting regular inspections and trimming.

IBM’s system uses data collected by satellites, drones, aerial flights, sensors and weather models to help companies monitor the state and maintenance of hundreds of miles of transmission and distribution lines.

As well as identifying and predicting outage threats, the system can also help with grid reliability, wildfire prevention, storm management and assessment, the company said. ... "

Use of Weather Channel App Data

On the alleged gathering of data and how it is used.  Leads to further consideration about how such data is used now and in the future.   Must we now consider the future intent of data use?

L.A. Sues IBM's Weather Company over 'Deceptive' Weather Channel App

The Weather Channel’s app secretly sucks up users’ personal data and uses it for things like targeted marketing and hedge fund analysis, the Los Angeles city attorney has claimed in a lawsuit against The Weather Company, the IBM-owned firm that runs the app. The case was first reported Thursday in the New York Times, but City Attorney Mike Feuer will hold a press conference Friday morning. In a tweet, he said he was taking “action against one of America’s largest corporations for what we allege  ... " 

WCAI and Earth Networks

An interesting collaboration, reminiscent of work by IBM with the Weather Channel

Earth Networks and Wharton Customer Analytics Initiative Collaborate to Advance Weather-Driven Analytics Research

Wharton Customer Analytics Initiative (WCAI) Research Center will tap Earth Networks proprietary weather intelligence to expand commercial use cases of rich weather data assets ... "

Predicting Flash Floods with Weather Data

Good case Study from O'Reilly.   I like the use of weather data, which can be problematic.  Here converted to classification and thus prediction from embedded decision trees.      Could have used the idea in a long ago US stream and estuary model.

Random forests to save human lives
Flash flood prediction using machine learning has proven capable in the U.S. and Europe; we're now bringing it to East Africa. ..... " 

By Zachary Flamig and Race Clark February 28, 2017  .... " 

Watson Ads as Conversation

So what is a Watson Ad?   Its more like an intelligent brand conversation.  Like the idea, taking a closer look.    Any experiences out there?

The Weather Company | With Watson
The Weather Company and Watson have teamed up to bring you Now, for the first time ever, you can have an intelligent, two-way conversation with brands in an ad. Your favorite brands can help you prepare a new meal, shop for the perfect shoes, find your dream car...even relieve allergy symptoms. When you chat with Watson Ads on The Weather Channel App or weather.com, you never know where the conversation might go.  ... " 

More on Cognitive Ads.

Smarter Health Devices

An example of how predictive weather data might be used to make devices smarter.

IBM Is Using Weather Data To Help Predict Asthma Attacks
IBM is entering into the race to develop "smart" inhalers and software systems for asthma patients. ... "

IBM Integrates Weather APIs into Bluemix

We were early users of weather data in retail and supply chain analytics, this would have been very useful. Integration with cognitive links in particular are intriguing.  Note this includes both data and forecasting capabilities from the Weather Co.  Which could let you predict things like the demand of products influenced by weather, both current and future. Will be interesting to see what other things
come out of this evolution.

(Update)    I missed mentioning one of the most obvious direct applications: Agribusiness. When we were still in the coffee business could we have predicted flavor components based on past weather data? Which then could have been used to change components of green coffee blends?  .....

IBM Just Made It Easier to Build Apps That Harness the Weather

" ... Today’s announcement—putting the Weather Company API on BlueMix—means it will be less of a hassle for developers to add weather data to their next great app. “If you were building an app on BlueMix and wanted to add weather prior to this Weather Company add on, you’d have to open a new window go to the Weather Company’s platform, get an API key, deal with billing, subscription services, all the normal administrivia that comes from sourcing data from many places,” says Bryson Koehler, the Weather Company’s chief information and technology officer. Which isn’t necessarily a huge pain, but think about it this way: If you were building a couch, would you visit one store for the lumber, another for the screws, another for the saws to cut the lumber, another for the drill to use the screws … or would you just go to Home Depot and grab all that crap at once? .... " 

But hey, not everyone likes Home Depot. So how is BlueMix as a one-stop API shop? (If you don’t know, APIs are programs that let other programs and apps talk to each other.) Well, it’s IBM, so it is pretty thorough. Beyond the basic starter kits for various API fucntions (banking, Internet of Things, cloud computing), it lets you access cool stuff like Watson’s cognitive computing tools, so you can do creepy things like build psychological profiles of the consumers using your app. .... "

Watson Delivers Cognitive Artificial Intelligence Ads

A new automation play in the works with a number of participants.   Note also the natural language and voice interaction components.  New channels of automated consumer interaction.  Bots are not mentioned, but this provides an opening for more complete and compelling conversations.  This could change how advertising works.

Weather Company Incorporates IBM's Watson Into New Ad Product
Bets Artificial Intelligence Will Change Advertising ... By Jeanine Poggi.

The Weather Company is placing a bet on artificial intelligence. In its first major announcement since being acquired by IBM in January, Weather Company is introducing Watson Ads, which will allow consumers to ask questions via voice and text to receive information on products or brands. ... 

While there's no dearth of data available for marketers to make informed decisions about their customers and provide more relevant messaging and experiences, Watson's ability to understand natural language, reason and learn over time may allow for greater understanding of how consumers interact with brands and their perception of products.

Unilever will be one of the first advertisers to utilize Watson Ads for its Hellmanns and Country Crock brands. With Watson, consumers can ask their phone or computer for recipe ideas based on specific ingredients. .... " 

Also reported on in the WSJ.