Friday November 15, 2019
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New Algorithm That may Predict Your Intelligence

The personality test they used divides personality into five scales -- openness to experience, conscientiousness, extraversion, agreeableness, neuroticism

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

Be careful if you lie about your IQ as researchers have developed a machine-learning algorithm that may tell how smart you are by looking at a scan of your brain.

The study showed that the new computing tool can predict a person’s intelligence from functional magnetic resonance imaging (fMRI) scans of their resting state brain activity.

The fMRI develops a map of brain activity by detecting changes in blood flow to specific brain regions.

“We found if we just have people lie in the scanner and do nothing while we measure the pattern of activity in their brain, we can use the data to predict their intelligence,” said co-author Ralph Adolphs from the California Institute of Technology (Caltech).

To train their algorithm on the complex patterns of activity in the human brain, the researchers downloaded the brain scans and intelligence scores from almost 900 individuals.

The researchers found that after processing the data, the algorithm was able to predict intelligence at statistically significant levels across the subjects.

algorithm
Representational image.

“The information that we derive from the brain measurements can be used to account for about 20 per cent of the variance in intelligence we observed in our subjects,” said Julien Dubois from the Cedars-Sinai Medical Centre.

In predicting intelligence from brain scans, the algorithm is doing something that humans cannot, because even an experienced neuroscientist cannot look at a brain scan and tell how intelligent a person is, the researchers said.

The researchers also conducted a parallel study, using the same test population and approach, that attempted to predict personality traits from fMRI brain scans.

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The personality test they used divides personality into five scales — openness to experience, conscientiousness, extraversion, agreeableness, neuroticism.

However, it has turned out to be much more difficult to predict personality using the method the team used for predicting intelligence.

The two studies are yet to be published in the separate journals Personality Neuroscience and Philosophical Transactions of the Royal Society. (IANS)

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Researchers Develop New Algorithm to Identify Cyber-bullies on Twitter

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn

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Business, Twitter, Invest, Investment, Start-up, Kochi
A man reads tweets on his phone in front of a displayed Twitter logo. VOA

Researchers have developed machine learning algorithms which can identify bullies and aggressors on Twitter with 90 per cent accuracy.

For the study published in the journal Transactions on the Web, the research team analysed the behavioural patterns exhibited by abusive Twitter users and their differences from other users.

“We built crawlers — programs that collect data from Twitter via variety of mechanisms,” said study researcher Jeremy Blackburn from Binghamton University in the US.

“We gathered tweets of Twitter users, their profiles, as well as (social) network-related things, like who they follow and who follows them,” Blackburn said.

The researchers then performed natural language processing and sentiment analysis on the tweets themselves, as well as a variety of social network analyses on the connections between users.

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Twitter is a social media app that encourages short tweets and brief conversations. Pixabay

They developed algorithms to automatically classify two specific types of offensive online behaviour, i.e. cyber-bullying and cyber-aggression.

The algorithms were able to identify abusive users — who engage in harassing behaviour like those who send death threats or make racist remarks — on Twitter with 90 per cent accuracy.

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“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn.

“Our research indicates that machine learning can be used to automatically detect users that are cyber-bullies, and thus could help Twitter and other social media platforms remove problematic users,” Blackburn added. (IANS)