[LINK] WEF's AI Autumn

[Stephen Loosley]

Roger notes,

 > ... Executives at the World Economic Forum (WEF) say they are grappling
> with how to turn early demos into money-makers..  the months ahead may
> even feel like an "AI letdown".  "Everyone's  like, yeah, I can build these cool
> demos, but where's the real value?"
>
> https://www.itnews.com.au/news/ai-buzzes-davos-but-ceos-wrestle-with-how-to-make-it-pay-604155


Must say, personally one can’t care much about making AI pay

I’d prefer we might make AI helpful for humanity, and the planet

One future example ..

“Cyclone forecasting boosted by artificial intelligence offers earlier path tracking”

European AI model predicts tropical cyclone nearing the Queensland coast closer to Rockhampton than Cairns by late next Thursday

By Peter Hannam  Thu 18 Jan 2024 10.20 AEDT
https://www.theguardian.com/australia-news/2024/jan/18/cyclone-forecasting-boosted-by-artificial-intelligence-offers-earlier-forecasts


Cyclone forecasting is getting a boost from artificial intelligence models that can predict the risks of these extreme weather events with increasing accuracy.

Queenslanders told to be on alert by the Bureau of Meteorology for a second tropical cyclone to hit the state in just over a month now have extra ways to monitor the risks via online trackers predicting the potential path of the cyclone.

As of Wednesday afternoon, the bureau was rating the possibility that a low pressure system in the Coral Sea would form into a cyclone as a 60% chance by next Tuesday. Should it form, the cyclone would be named Kirrily.

Some models had the system turning towards the Queensland coast, said Daniel Hayes, a Cairns-based community information officer for the bureau.

“If it moves back towards the coast, probably somewhere between Cairns and Rockhampton is the more likely area for it to head towards,” Hayes said, adding, “it’s not expected to have any direct impact on the coast within the next seven days”.

Other weather agencies, such as the well-regarded European Centre for Medium-Range Weather Forecasts (ECMWF) has a clear cyclone forming by next Friday.

However, forecasting generated by machine learning is increasingly available to the public, including via the European centre itself.

Its Artificial Intelligence/Integrated Forecasting System (AIFS) model, for instance, has cyclone Kirrily nearing the Queensland coast much closer to Rockhampton than Cairns by late next Thursday.

https://charts.ecmwf.int/products/medium-mslp-wind850?base_time=202401161200&projection=opencharts_australasia&valid_time=202401261200

[An animation generated by the European Centre for Medium-Range Weather Forecasts’ experimental AI-based model showing the track the potential tropical cyclone Kirrily might take. European Centre for Medium-Range Weather Forecasts]

The ECMWF launched its experimental model last October and also carries several other versions on its website produced using AI technology, such as by Google’s GraphCast and China’s Huawei Pangu model.

https://charts.ecmwf.int/products/graphcast_medium-mslp-wind850?base_time=202401161200&projection=opencharts_australasia&valid_time=202401261200

https://charts.ecmwf.int/products/pangu_medium-mslp-wind850?base_time=202401151200&projection=opencharts_australasia&valid_time=202401231200

The centre said the more traditional physics-based numerical weather prediction models, such as its Integrated Forecasting System, remain “still key” to its predictions. One reason is the numerical models provide the initial conditions and the training datasets that the machine-learning versions work from.

Also, the AI models are based on single calculations, not the multiple calculations that produce the ensemble of results that the ECMWF would rely on to produce probability maps for cyclones.

Guardian Australia approached the bureau for comment. The bureau’s own Access model shows the system remaining well off the coast by Tuesday.

http://www.bom.gov.au/australia/charts/viewer/index.shtml

Jyoteeshkumar Reddy Papari, a postdoctoral researcher at the Csiro, said it remained “hard to more precisely predict the tropical cyclone (TC) track a week out even with the AI models”.

“Overall, the AI model is doing a reasonably good job in spinning the [tropical cyclone] and providing the possible track as good as the dynamical model,” Papari said.

Still, “people should be cautious about the possibility of [tropical cyclone] landfall [and] should take the official warnings” from official agencies such as the bureau.

Nevertheless, “they are useful guides to what’s coming”, he said. “We can expect more AI weather models even with ensemble-style forecasts and [they will] become more accessible to the public.”

Other models are throwing up a variety of tracks for the cyclone, such as the US’s Global Ensemble Forecast System.

Scientists working for Google’s DeepMind division last year published a peer-reviewed paper in the journal Science stating their GraphCast machine learning-based model had outperformed the most accurate operational systems on 90% of 1,380 targets.

https://www.science.org/doi/10.1126/science.adi2336

Its forecasts also supported “better severe event prediction including tropical cyclone tracking, atmospheric rivers and for extreme temperatures”, the researchers said.

--