Old Habit of Predicting Early Spring through a Ground Hog Should Be Replaced by AI Animal
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Thousands gathered on Sunday at Gobbler's Knob in Punxsutawney, Pennsylvania, to watch a famous groundhog deliver his forecast. Punxsutawney Phil emerged from his burrow around 7:25 a.m. and did not see his shadow, which meant it would be an early spring for us all.
It's a tradition that stretches back more than a century, but that obviously does not have a very good track record.
RELATED:PETA SUGGESTS REPLACING PUNXSUTAWNEY PHIL WITH A ROBOT GROUNDHOG
That's why new ideas have been proposed for this old tradition. PETA, for instance, has sent an open letter to Punxsutawney to stop terrorizing the innocent rodent, and let it roam the earth freely. They suggest that Phil should be replaced with an animatronic groundhog that uses AI to predict the weather. In addition to being more fun to watch, it may make for a more accurate prediction of the weather.
In the letter to The Punxsutawney Groundhog Club, PETA President Ingrid Newkirk writes, “As a prey species, groundhogs actively avoid humans. Being in close proximity to the public causes these animals great stress. When Phil is dragged out of his hole and held up to flashing lights and crowds, he has no idea what's happening. Being relegated to a library "habitat" for the other days of the year doesn't allow him or the other groundhog there to dig, burrow, or forage. It's no kind of life for these animals.” It has been for a while argued that AI might be best at predicting the weather.
The enormous data sets required to analyze the Earth's atmosphere makes predicting future events very tricky, indeed. Current computer models are required to make judgments of several large-scale phenomena.
These include things like how the Sun heats the Earth's atmosphere, how pressure differences affect wind patterns, and how water-changing phases (ice to water to vapor) affect energy flow through the atmosphere.
They also need to consider the Earth's rotation in space, which helps churn the atmosphere throughout the day. Basically, even the tiniest change in one variable can profoundly change future events.
AI could be employed to improve the accuracy and reliability of weather forecasting. Furthermore, AI can use computer-generated mathematical programs and computational problem-solving methods on vast data sets to identify patterns and make a relevant hypothesis, generalizing the data.
Given the inherent complexity involved in weather prediction, scientists are now using AI for weather forecasting to obtain refined and accurate results. By using deep learning mathematical models, AI could learn from past weather records to predict the future.
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