In 2018, Algorithms will increasingly power every aspect of our lives, from voice recognition
to self-driving cars. But it's difficult for humans to understand such abstractions. "There's a real lack of imagery and visual metaphors for all these new and very abstract things that we have in our lives," explains Marcus Wendt, creative director at London-based art and technology studio FIELD. "We don't have anything that will help us decide whether we can really trust these systems, or which one to go for, whenever there is more than one option."
These images, created exclusively for WIRED by FIELD and based on the structure of computing code, are part of a project to make algorithms much more accessible to all of us by developing a new visual language around them. "We will need to understand them better because we need to decide if we want to let them into our lives," Wendt adds.
Amit Katwala is the author of The Athletic Brain (Simon & Schuster)
HOW SELF-DRIVING CARS SEE THE WORLD
To navigate the world safely, autonomous vehicles must build a picture of it. To do this, an algorithm integrates real-time feeds from a multitude of sensors including video, infrared, radar and ultrasound. It then passes that data through up to 150 processing stages and filters informed by prior learning. This image is based on Inception, Google's image-recognition model. It shows the inputs (on the right) being pulled in and processed (top left) into a model of the road ahead. Other vehicles are represented by the red boxes.
FACE HACKERS
In July 2017, researchers at the University of Washington revealed that they can make a believable video of a person speaking from an audio recording, using a neural network. They trained their model on footage of Barack Obama and put words in his mouth via recordings of impressionists. The fake-news possibilities of this are alarming. This image, which has Obama's face in the background, illustrates how the neural network learns how different sounds correspond to facial movements.
The blocks represent areas of the facial structure that play a key role when manipulating audio input
IMAGE CREATION
Algorithms receive feedback from humans to help them improve - but AI researchers are excited by generative adversarial networks. The idea is to pit two machine-learning programs against each other - one to create something, the other to act as a critic. Amazon is testing an application in which networks analyse images and then create similar ones. Although they can currently only create tiny images, the technique might one day be used in film-making.
The colourful strips indicate parts of the image that the AI critic considers to need improvement
FOLLOWING THE MONEY TRAIL
With initial coin offerings attracting attention and governments testing their own cryptocurrencies, digital money will continue to grow in influence in 2018. This image depicts transactions in Ethereum, a blockchain-based cryptocurrency. Each square represents a line in the distributed ledger that makes up a blockchain, with each following on from the last. The squares' colours are determined by the amount of money that is being moved.
Individual Ethereum transactions are represented by the small blue dots on this image
THE NEXT GENERATION OF VOICE ASSISTANTS
Alexa, Siri and Cortana will become much smarter in 2018. A computer-science breakthrough called dynamic program generation will help voice assistants understand more complex instructions and respond in ways that combine information from multiple sources. When the assistant is given a command, the technology parses the sound phonetically and processes it to extract the person's meaning and intent. It's then able to form a more precise response.
The voice input takes the form of a sound wave, which can be seen on the bottom layer of the image
http://www.wired.co.uk/gallery/algorithms-visualised-self-driving-cars-voice-assistants
