AI System Can Recreate Video Games It Observes

I wouldn’t be surprised if one day AI systems ruled the world. While there are skeptics, most people in the technological industry remain pretty optimistic. And with good reason. A system built by researchers at the Georgia Institute of Technology can recreate video games by observing them for only 2 minutes.

The team did this by training the AI on footage of two distinct types of players making their way through Level 1 of Super Mario Brothers. One that adopted an “explorer” style of play and the other a “speedrunner” style, where they headed straight for the goal.

The system managed to rebuild an accurate representation of the game with only minor deviations. It’s impressive and also far less creepy than an AI creating its own language.

“Our AI creates the predictive model without ever accessing the game’s code, and makes significantly more accurate future event predictions than those of convolutional neural networks,”

Okay, so its capabilities are still sort of spooky, but useful, nonetheless. The program, which is algorithm-based, could be vital in pattern recognition, among other things. In the end, the model is an effective training method that can also be easily controlled by users — phew!

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Teen Invents AI System To Diagnose Eye Disease

The new generation of innovators is getting younger by the decade. If a thirteen-year-old can generate clean energy from traffic, what more can other kids do? Apparently this high school junior invented an artificial intelligence system to diagnose her grandfather’s eye disease.

Eyeagnosis [is] a smartphone app plus 3D-printed lens that seeks to change the diagnostic procedure from a 2-hour exam requiring a multi-thousand-dollar retinal imager to a quick photo snap with a phone.

[Kavya] Kopparapu and her team… trained an artificial intelligence system to recognize signs of diabetic retinopathy in photos of eyes and offer a preliminary diagnosis.

Medical jargon aside, the device would make testing more efficient and accessible. Kopparapu is also passionate about empowering young girls interested in computer science. She not only founded the Girls Computing League, she regularly hosts coding workshops for marginalized kids.

In order to create Eyeagnosis, Kopparapu did a lot of Googling and contacted numerous experts. She then taught a retired system to do the work.

In November, she shipped her first 3D-printed prototype for the system’s lens to the hospital. When fitted onto a smartphone, the lens focuses the phone’s diffuse, off-centered flash to best illuminate a retina. The complete Eyeagnosis system has already been tried on five patients at the hospital, and in each case it made an accurate diagnosis.

It may be intimidating to the older, non-techie generation, but the world of science could use more kids like Kopparapu.

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