Cave with light coming through

Sometimes it’s hard for archeologists to tell if a location is an ancient burial site or simply where someone died long ago. One group of researchers trained a machine learning algorithm to decipher between these two possibilities, as well as other situations. While the results of this group’s study are heavily disputed by other archeologists (and for good reason since there are many factors that need to be taken into consideration), the researchers say their conclusions can’t be discounted.

Photo by Joshua Sortino on Unsplash

Large hail stones

A postdoctoral researcher at the US National Center for Atmospheric Research (NCAR) developed a simple convolutional neural network model to predict hailstorms. The researcher trained the model on 82,000 storm profiles and tested it on 32,000 storms. It was accurate 88 percent of the time. While this neural net is currently a proof of concept, its accuracy shows promise, piquing the interest of other meteorologists.

Photo Found Here: https://pixabay.com/en/hailstone-storm-highveld-1614239/

Half bright moon

Artificial intelligence is not only making a big impact on Earth, but in our solar system as well. AI has enabled astronomers to quickly identify 6,000 new craters on the moon. Using AI as a discovery method has big implications for astronomers, allowing them to spend less time combing through data and more time researching and theorizing.

Photo Found Here: https://pixabay.com/en/moon-crescent-night-sky-background-2373242/

A close-up on an eye

It’s been hot news for the last 24 hours: Google’s developed a machine learning algorithm that can scan images of your eyes and predict your risk of heart disease. While this tech is not ready for clinical use yet (it needs more testing), it holds a lot of promise—it predicts heart disease to about the same level of accuracy as other current medical methods, and it’s fast because testing doesn’t require analyzing blood results.

What are the implications? Once this tool goes live in a medical setting, it’ll save doctors and patients time, time that doctors can use to better treat patients.

Photo by Liam Welch on Unsplash

Ancient building facade

It’s hard to detect smaller earthquakes in areas that have few seismic stations. And the less data you have, the harder it can be. Now, with a convolutional neural network developed by Harvard and MIT researchers, seismologists can better sift through the data to find earthquakes. By feeding the network training sets from seismically inactive regions, the network can identify and disregard regular activity while parsing the data, allowing it to clearly identify tremors.

What are the implications? We can better identify earthquakes and tremors with less data.

Photo Found Here: https://pixabay.com/en/temple-shack-earthquake-burma-2740180/

Researchers at the University of Pennsylvania conducted a brain study where they used personalized algorithms to trigger brain-specific pulses, to hopefully improve their patients’ memories . . . and these algorithms did. Patients’ word recall increased by 15 percent. Essentially, they are working to build a brain-activity reader that can tell when the brain is effectively encoding memories. If it’s not, they send pulses pulses to certain areas of the brain that kick brain activity up a notch.

Photo Found Here: https://pixabay.com/en/walnut-nut-shell-nutshell-open-3072652/

In the 1800s, historians discovered an indecipherable text, now called the Voynich manuscript. It dates back to the 15th century, but they’ve never been able to figure out its language of origins or what it says. More recently, however, it’s been reported that researchers at the University of Alberta used AI to pinpoint that over 80 percent of the manuscript’s words were identified as Hebrew (initially they thought it was probably Arabic), and they believe they’ve translated the text’s first sentence. However, The Verge hotly disputes the original reporting of other news outlets, saying it wasn’t AI after all, and the original study is suspect.

Photo by Kiwihug on Unsplash

IBM researchers used AI to help predict the likelihood of study subjects developing psychosis. They employed AI to parse transcripts from a prior study and predict the potential for mental illness from linguistic indicators. The AI was 83 percent accurate in its predictions, and interestingly, one indicator was that people at risk used fewer possessive pronouns while talking than those who weren’t at risk.

Photo by Volkan Olmez on Unsplash