Colorectal Cancer Rates Rising In Young People | What An AI Learns From A Baby

Colorectal cancer is becoming increasingly common among adults in their 20s, 30s, and 40s. Plus, associating images and sounds from a child’s daily life helped teach a computer model a set of basic nouns.

Colorectal Cancer Rates Are Rising In Young People

Gastrointestinal medicine practitioners have noticed something strange in recent years: More and more young people are being diagnosed with colorectal cancer.

It used to be incredibly rare for anyone under the age of 50 to be diagnosed with colorectal cancer. Cases were generally limited to people with excess weight who live a sedentary lifestyle. But practitioners are increasingly seeing people in their 40s, 30s, and even 20s without prior risk factors being diagnosed with colorectal cancer.

Jennifer Fijor is one nurse practitioner who has seen this rise in cases firsthand at Virginia Mason Franciscan Health in Seattle, Washington. Jennifer has been spreading awareness about this rise on her social media accounts.

Jennifer speaks with guest host Kathleen Davis about the warning signs of colorectal cancer, such as sudden changes in bowel movements, and how patients can advocate for themselves to get screened early.

What An AI Learns From A Baby’s-Eye View Of The World

There’s a lot to learn in the first couple of years of a child’s life—not the least of which is how to talk. But little kids don’t sit down and study a vocabulary book. They soak up language from daily experiences, which are often filled with parents and caregivers saying things like “look at the kitty cat.” Scientists wondered whether an artificial intelligence model could learn about language using a similar strategy—not by being fed a curated set of pictures and words, but by eavesdropping on the day-to-day activities of a small child.

They found that associating images and sounds from 60 hours of video captured by a camera mounted on a baby’s head could teach a computer model a set of several dozen basic nouns, such as “car,” “cat,” and “ball.” And the learning was generalizable, meaning that the computer was able to properly identify cars and cats that it had not seen before.

Dr. Wai Keen Vong, a research scientist in the Center for Data Science at New York University and one of the authors of a study recently published in the journal Science, joins SciFri’s Kathleen Davis to talk about the research and what it can teach us about learning.

Transcripts for each segment will be available the week after the show airs on sciencefriday.com.

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