Lifeboat Foundation

Russian researchers from HSE University and Open University for the Humanities and Economics have demonstrated that artificial intelligence is able to infer people’s personality from ‘selfie’ photographs better than human raters do. Conscientiousness emerged to be more easily recognizable than the other four traits. Personality predictions based on female faces appeared to be more reliable than those for male faces. The technology can be used to find the ‘best matches’ in customer service, dating or online tutoring.

The article, “Assessing the Big Five personality traits using real-life static facial images,” will be published on May 22 in Scientific Reports.

Physiognomists from Ancient Greece to Cesare Lombroso have tried to link facial appearance to personality, but the majority of their ideas failed to withstand the scrutiny of modern science. The few established associations of specific facial features with personality traits, such as facial width-to-height ratio, are quite weak. Studies asking human raters to make personality judgments based on photographs have produced inconsistent results, suggesting that our judgments are too unreliable to be of any practical importance.

Microsoft and Altran release Code Defect AI to identify potential problems in software development and suggest fixes.

But Ben Shneiderman, a University of Maryland computer scientist who has for decades warned against blindly automating tasks with computers, thinks fully automated cars and the tech industry’s vision for a robotic future is misguided. Even dangerous. Robots should collaborate with humans, he believes, rather than replace them.

A computer scientist argues that the quest for fully automated robots is misguided, perhaps even dangerous. His decades of warnings are gaining more attention.

Microsoft’s FastSpeech text-to-speech model generates speech up to 38 times faster than the baseline without sacrificing quality.

Researchers trained an A.I. on over 31,000 selfies to help it learn how to judge our personalities based on facial features alone.

Sam Frons, CEO and founder of Addicaid, uses artificial intelligence and predictive analytics to drive her new app for addiction recovery.

Beijing start-up develops AI algorithm to determine how prone the VR participant is to drug use by tracking their pulse, brainwaves and electrical conductance of the skin.

Zhou Yi was terrible at math. He risked never getting into college. Then a company called Squirrel AI came to his middle school in Hangzhou, China, promising personalized tutoring. He had tried tutoring services before, but this one was different: instead of a human teacher, an AI algorithm would curate his lessons. The 13-year-old decided to give it a try. By the end of the semester, his test scores had risen from 50% to 62.5%. Two years later, he scored an 85% on his final middle school exam.

“I used to think math was terrifying,” he says. “But through tutoring, I realized it really isn’t that hard. It helped me take the first step down a different path.”

High-quality data is the fuel that powers AI algorithms. Without a continual flow of labeled data, bottlenecks can occur and the algorithm will slowly get worse and add risk to the system.

It’s why labeled data is so critical for companies like Zoox, Cruise and Waymo, which use it to train machine learning models to develop and deploy autonomous vehicles. That need is what led to the creation of Scale AI, a startup that uses software and people to process and label image, lidar and map data for companies building machine learning algorithms. Companies working on autonomous vehicle technology make up a large swath of Scale’s customer base, although its platform is also used by Airbnb, Pinterest and OpenAI, among others.

The COVID-19 pandemic has slowed, or even halted, that flow of data as AV companies suspended testing on public roads — the means of collecting billions of images. Scale is hoping to turn the tap back on, and for free.