Innovation

The Mysterious Uncrackable Video Game

On their quest to unearth lost video game coding secrets, researchers John Aycock and Tara Copplestone of the Universities of Calgary and York stumbled accross a piece of code from the 1982 Atari 2600 game Entombed which they could not reverse engineer. The video game archaeologists contacted employees involved in developing the game, and they too recalled not being able to decipher the logic behind a particular data table which was used to generative valid maze structures within the game.

3D Ken Burns Effect From Single Image

Researchers at Portland State University and Adobe have demonstrated being able to generate a 3D Ken Burns effect (parallax) using a single image. The system uses neural networks to generate depth predictions and object boundaries, and context aware in-painting to generate the missing pieces of the video to simluate a moving point of focus. We can tick another Star Trek TNG sci-fi concept off the list.

Radiative Sky Cooling

Researchers at UCLA have produced a prototype energy unit that takes advantage of radiative sky cooling to generate electricity at night. As heat is radiated from objects facing the sky at night, a thermoelectric generator can be used to generate electricity from the temperature difference, in this case 25 milliwatts per square metre using equipment costing $30.

AI Voice Mimic Heist

AI generated voice mimicking software was used to persuade a director of a German energy subsidiary in the UK that their boss was on the phone and allowed theives to order the director to transfer funds to their bank account! This is believed to the first voice-AI assisted theft, so convincing that the director in question who made the transfer said that the software even imitated the tonality of the boss' voice.

Latent Knowledge Identificaiton Using ML

Researchers used machine learning to analyse 3.3 million material science abstracts from 1922 to 2018. They found that the ML system captured fundamental knowledge within the field and also historically identified new materials and research to study before the new materials were discovered in real life. The research shows how machine learning can be used to identify latent knoweldge more quickly.

Lyft Self Driving Dataset

Lyft have released a huge self driving level 5 dataset comprising 55,000 human labelled 3D annotated frames, a driveable surface map and an underlying spacial semantic map to contextualise the data. The release of the dataset is part of a competition with a prize of $25,000, aimed at researchers to help Lyft train AI algorithms to help them reach their goal of a Level 5 (fully automated) self driving car.

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