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Researchers Develop Algorithm to Predict an Actor’s Career

Hence, an actor’s success could be down to their circumstances rather than the acting ability

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Mathematics. Source: Pixabay

Researchers have developed an algorithm that predicts whether an actor’s career has peaked and also predicts their most successful days in future, with an accuracy of 85 per cent.

The research team discovered that the most productive years for actors, defined as the year with the largest number of credited jobs, are towards the beginning of their careers.

The study, published in the journal Nature Communications shows how around 70 per cent actors and actresses have careers that only last for one year.

“Our results shed light on the underlying social dynamics taking place in show business and raise questions about the fairness of the system. Our predictive model for actors is also far from the randomness that is displayed for scientists and artists,” study author from the Queen Mary University of London Oliver Williams said.

For the study, the researchers used data of Internet Movie Database (IMDb) and looked at the careers of over 2.4 million actors from around the world from 1888 to 2016 to analyse and predict success on the silver screen.

They found that careers are clustered into ‘hot’ and ‘cold’ streaks, as individuals do not tend to work at a steady rate in a business where unemployment rates hover at around 90 per cent.

New Algorithm That may Predict Your Intelligence
New Algorithm that may Predict actor’s career. (IANS)

There is also huge evidence of gender biases in the industry, as most of the patterns were observed different for actors and actresses, the study said.

According to the outcomes, the total number of jobs in a career is underpinned by the rich-get-richer phenomenon.

What is interesting about this observation is that the rich-get-richer effects are well known to develop out of arbitrary and unpredictable random events that get amplified.

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Hence, an actor’s success could be down to their circumstances rather than the acting ability.

“We think the approach and methods developed in this paper could be of interest to the film industry: for example, they could provide complementary data analytics to IMDb. This does also bring with it a number of open questions,” said Lucas Lacasa from the Queen Mary University of London. (IANS)

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Researchers Develop an Algorithm to Predict Storms, Cyclones

This research is an early attempt to show feasibility of AI-based interpretation of weather-related visual information

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Hurricane
In this image provided by NOAA, Tropical Storm Gordon approaches the United States. VOA

Using Artificial Intelligence (AI), researchers have developed an algorithm to detect cloud formations that lead to storms, hurricanes and cyclones.

The study, published in the journal IEEE Transactions on Geoscience and Remote Sensing, shows a model that can help forecasters recognise potential severe storms more quickly and accurately.

The researchers created a framework based on Machine Learning (ML) — a kind of AI — that detects rotational movements in clouds from satellite images that might have otherwise gone unnoticed.

“The very best forecasting incorporates as much data as possible, there’s so much to take in as the atmosphere is infinitely complex. By using the models and the data we have, we’re taking a snapshot of the most complete look of the atmosphere,” said Steve Wistar, Senior Forensic Meteorologist at AccuWeather in the US.

For the study, researchers analysed more than 50,000 US weather satellite images and identified and labelled the shape and motion of ‘comma-shaped’ clouds.

These cloud patterns are strongly associated with cyclone formations which can lead to severe weather events including hail, thunderstorms, high winds and blizzards, they said.

cyclone kenneth, torrential rain
An aerial shot shows widespread destruction caused by Cyclone Kenneth when it struck Ibo island north of Pemba city in Mozambique, May, 1, 2019 (Representational image). VOA

Then, using computer vision and ML techniques, the researchers taught computers to automatically recognize and detect ‘comma-shaped’ clouds in satellite images.

The computers could then assist experts by pointing out in real time where, in an ocean of data, could they focus their attention in order to detect the onset of severe weather.

“Because the ‘comma-shaped’ cloud is a visual indicator of severe weather events, our scheme can help meteorologists to forecast such events,” said study lead author Rachel Zheng from Penn State University in the US.

The researchers found that their method can effectively detect ‘comma-shaped’ clouds with 99 per cent accuracy, at an average of 40 seconds per prediction.

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It was also able to predict 64 per cent of severe weather events, outperforming other existing severe weather detection methods.

This research is an early attempt to show feasibility of AI-based interpretation of weather-related visual information. (IANS)