If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The process of predicting accurate weather patterns has always been a challenge for meteorologists. The complexity is in observing and processing vast amounts of data, and because most of the weather stations would never be able to collect, process and store so much information. With traditional models, the systems have to read huge data sets from several weather stations that would take many hours to predict the weather accurately. The good news is artificial intelligence (AI) has been able to outperform these traditional methods by streamlining the weather forecasting process while bringing accuracy and reliability in assessing weather reports. The amount of data received from global sensors, weather stations, satellites, and radar, is staggering. The estimation often goes well beyond trillions of data points and is expected to continue to grow.
Over the course of an hour, an unsolicited email skips your inbox and goes straight to spam, a car next to you auto-stops when a pedestrian runs in front of it, and an ad for the product you were thinking about yesterday pops up on your social media feed. What do these events all have in common? It's artificial intelligence that has guided all these decisions. And the force behind them all is machine-learning algorithms that use data to predict outcomes. Now, before we look at how machine learning aids data analysis, let's explore the fundamentals of each.
Had the severity grown to crisis levels, Lucas McDonald, a former TV weatherman who leads the chain's emergency operations, might have called in dozens of workers to support the handful who are posted at the division's command center in 24/7 shifts. The full-house team--typically assembled only a few times a year--would help coordinate relief efforts, adjust supply routes and disseminate information to affected stores, a playbook the company has perfected through two exceptionally hectic hurricane seasons. "Right now, we're having conversations with some of our merchants on when the right time to ship more supplies into places like Florida and the Southeast would be ahead of any possible redevelopment from Dorian after it makes its way through Hispaniola," McDonald says. Meanwhile, in Dallas, meteorologists at Southwest Airlines mapped out contingency plans for rerouting and canceling flights given various possible hurricane scenarios. And in the Atlanta nerve center of IBM-owned Weather Company, forecasters relayed storm data and analysis to corporate clients like State Farm, which in turn used it to inform IBM Watson conversational ad units that spread safety information.
Now, there is a computer model that can help forecasters recognize potential severe storms more quickly and accurately, thanks to a team of researchers at Penn State, AccuWeather, Inc., and the University of Almería in Spain. They have developed a framework based on machine learning linear classifiers -- a kind of artificial intelligence -- that detects rotational movements in clouds from satellite images that might have otherwise gone unnoticed. This AI solution ran on the Bridges supercomputer at the Pittsburgh Supercomputing Center. Steve Wistar, senior forensic meteorologist at AccuWeather, said that having this tool to point his eye toward potentially threatening formations could help him to make a better forecast. "The very best forecasting incorporates as much data as possible," he said.
Meteorologists are starting to experiment with deep learning tech to predict severe weather patterns. David Gagne, a postdoctoral researcher at the US National Center for Atmospheric Research (NCAR), developed a simple convolutional neural network model to forecast the chances of hailstorms. In the last decade, severe storms caused about $14bn worth of damage and killed 94 people per year, Gagne said during a presentation at the GPU Technology Conference in San Jose, California. Meteorologists begin warning people of severe weather conditions the day before a hail event, but it's difficult to be precise. So Gagne wanted to see if deep learning could accurately identify the weather patterns leading up to hailstorms and reduce false alarms.
We present a data resource which can be useful for research purposes on language grounding tasks in the context of geographical referring expression generation. The resource is composed of two data sets that encompass 25 different geographical descriptors and a set of associated graphical representations, drawn as polygons on a map by two groups of human subjects: teenage students and expert meteorologists.
Our general weather in New England hasn't changed too much over the past 70 years. We still have our four seasons and a wild variety of all sorts of storms and temperatures. But one thing that has changed quite dramatically in that time is the way we view and receive forecasts for what's ahead. Weather observations were few and far between most of the time. A meteorologist had to depend on scattered reports from airports, fishermen, and phone calls from weather savvy locals.
Google released a new AI tool on Wednesday designed to let anyone train its machine learning systems on a photo dataset of their choosing. The software is called Cloud AutoML Vision. In an accompanying blog post, the chief scientist of Google's Cloud AI division explains how the software can help users without machine learning backgrounds harness artificial intelligence.
With climatologists looking towards the Arctic to examine the link between climate change and the recent bout of extremely cold weather, Arctic ice mapping has become crucial. The Danish Meteorological Institute – or DMI – is working on a radical new plan to improve its sea ice reports: artificial intelligence that can train itself to recognize ice in satellite images – and deliver more accurate ice reports, much faster.
Scientists are seeking the public's assistance in rescuing a unique set of weather records gathered at the summit of the UK's highest mountain. From 1883 to 1904, meteorologists were stationed atop Ben Nevis, logging temperature, precipitation, wind and other data around the clock. Their measurements are held in five big volumes that now need to be digitised to be useful to modern researchers. It will involve copying tables into a database. There are also likely insights to be gained on the peculiarities of mountain weather.