6 areas where artificial neural networks outperform humans

Five years ago, researchers made an abrupt and rather large leap in the accuracy of software that can interpret images. The artificial neural networks behind it underpin the recent boom we are now seeing in the AI industry. We are, however, still nowhere near achieving a reality similar to those in The Terminator or The Matrix.

Currently, researchers are trying to focus on teaching machines how to do one thing extremely well. Unlike a human’s brain, which processes multiple things at once, robots must “think” in a linear way. Regardless, in some fields, AI beats humans. Deep neural networks have learned to converse, drive cars, beat video games and Go champions, paint pictures, and help make scientific discoveries.

Here are six areas where artificial neural networks prove they can surpass human intelligence.

Machines have a strong record of besting humans in image and object recognition. Сapsule networks invented by Geoff Hinton almost halved the best previous error rate on a test that challenges software to recognize toys. Using an increased amount of these capsules over various scans allows the system to better identify an object, even if the view is different than those analyzed prior.

Another example comes from a state-of-the-art network that was trained on a database of labeled images and was able to classify objects better than a Ph.D. student, who trained on the same task for over 100 hours.

Google’s DeepMind uses a deep learning technique referred to as deep reinforcement learning. Researchers used this method to teach a computer to play the Atari game Breakout. The computer wasn’t taught or programmed in any specific way to play the game. Instead, it was given control of the keyboard while watching the score, and its goal was to maximize the score. After two hours of playing, the computer became an expert at the game.

The deep learning community is in a race to train computers to beat people at almost every game you can think of, including Space Invaders, Doom, Pong, and World of Warcraft. In the majority of these games, deep learning networks already outperform experienced players. The computers were not programmed to play the games; they just learned by playing.

Last year, Google released WaveNet and Baidu released Deep Speech. Both are deep learning networks that generate a voice automatically. The systems learn to mimic human voices by themselves and improve over time. Differentiating their speech from that of a real human is much harder to do than one might imagine.