The WIRED Guide to Artificial Intelligence
Wired.com, February 1, 2018
By: Tom Simonite
Superintelligent algorithms aren’t about to take all the jobs or wipe out humanity. But software has gotten significantly smarter of late. It’s why you can talk to your friends as an animated poop on the iPhone X using Apple’s Animoji, or ask your smart speaker to order more paper towels.
Tech companies’ heavy investments in AI are already changing our lives and gadgets, and laying the groundwork for a more AI-centric future.
The current boom in all things AI was catalyzed by breakthroughs in an area known as machine learning. It involves “training” computers to perform tasks based on examples, rather than by relying on programming by a human. A technique called deep learning has made this approach much more powerful. Just ask Lee Sedol, holder of 18 international titles at the complex game of Go. He got creamed by software called AlphaGo in 2016.
For most of us, the most obvious results of the improved powers of AI are neat new gadgets and experiences such as smart speakers, or being able to unlock your iPhone with your face. But AI is also poised to reinvent other areas of life. One is health care. Hospitals in India are testing software that checks images of a person’s retina for signs of diabetic retinopathy, a condition frequently diagnosed too late to prevent vision loss. Machine learning is vital to projects in autonomous driving, where it allows a vehicle to make sense of its surroundings.
There’s evidence that AI can make us happier and healthier. But there’s also reason for caution. Incidents in which algorithms picked up or amplified societal biases around race or gender show that an AI-enhanced future won’t automatically be a better one.
Moments that Shaped AI
1956 – The Dartmouth Summer Research Project on Artificial Intelligence coins the name of a new field concerned with making software smart like humans.
1965 – Joseph Weizenbaum at MIT creates Eliza, the first chatbot, which poses as a psychotherapist.
1975 – Meta-Dendral, a program developed at Stanford to interpret chemical analyses, makes the first discoveries by a computer to be published in a refereed journal.
1987 – A Mercedes van fitted with two cameras and a bunch of computers drives itself 20 kilometers along a German highway at more than 55 mph, in an academic project led by engineer Ernst Dickmanns.
1997 – IBM’s computer Deep Blue defeats chess world champion Garry Kasparov.
2004 – The Pentagon stages the Darpa Grand Challenge, a race for robot cars in the Mojave Desert that catalyzes the autonomous-car industry.
2012 – Researchers in a niche field called deep learning spur new corporate interest in AI by showing their ideas can make speech and image recognition much more accurate.
2016 – AlphaGo, created by Google unit DeepMind, defeats a world champion player of the board game Go.
Your AI Decoder Ring
Artificial intelligence – The development of computers capable of tasks that typically require human intelligence.
Machine learning – Using example data or experience to refine how computers make predictions or perform a task.
Deep learning – A machine learning technique in which data is filtered through self-adjusting networks of math loosely inspired by neurons in the brain.
Supervised learning – Showing software labeled example data, such as photographs, to teach a computer what to do.
Unsupervised learning – Learning without annotated examples, just from experience of data or the world—trivial for humans but not generally practical for machines. Yet.
Reinforcement learning – Software that experiments with different actions to figure out how to maximize a virtual reward, such as scoring points in a game.
Artificial general intelligence – As yet nonexistent software that displays a humanlike ability to adapt to different environments and tasks, and transfer knowledge between them.
Learn More from Wired.com
What The AI Behind AlphaGo Can Teach Us About Being Human
Drama, emotion, server racks, and existential questions. Find them all in our on-the-scene account from the triumph of Google’s Go-playing bot over top player Lee Sedol in South Korea.
John McCarthy, Father Of AI And Lisp, Dies At 84
WIRED’s 2011 obituary of the man who coined the term artificial intelligence gives a sense of the origins of the field. McCarthy’s lasting, and unfulfilled, dream of making machines as smart as humans still entrances many people working on AI today.
Are We Ready for Intimacy With Androids?
People have always put themselves into their technological creations—but what happens when those artificial creations look and act just like people? Hiroshi Ishiguro builds androids on a quest to reverse engineer how humans form relationships. His progress may provide a preview of issues we’ll encounter as AI and robotics evolve.
When it Comes to Gorillas, Google Photos Remains Blind
The limitations of AI systems can be as important as their capabilities. Despite improvements in image recognition over recent years, WIRED found Google still doesn’t trust its algorithms not to mix up apes and black people.
Why Artificial Intelligence is Not Like Your Brain—Yet
You might hear companies, marketers, or drinking companions say AI algorithms work like the brain. They’re wrong, and here’s why.
Artificial Intelligence Seeks an Ethical Conscience
As companies and governments rush to embrace ever-more powerful AI, researchers have begun to ponder ethical and moral questions about the systems they build, and how they’re put to use.
A ‘Neurographer’ Puts The Art In Artificial Intelligence
Some artists are repurposing the AI techniques tech companies use to process images into a new creative tool. Mario Klingemann’s haunting images, for example, have been compared to the paintings of Francis Bacon.
Plus! AI’s hallucination problem and more WIRED artificial intelligence coverage.
As Artificial Intelligence Advances, Here Are Five Tough Projects for 2018
Wired.com, December 27, 2017
By Tom Simonite
For all the hype about killer robots, 2017 saw some notable strides in artificial intelligence. A bot called Libratus out-bluffed poker kingpins, for example. Out in the real world, machine learning is being put to use improving farming and widening access to healthcare.
But have you talked to Siri or Alexa recently? Then you’ll know that despite the hype, and worried billionaires, there are many things that artificial intelligence still can’t do or understand. Here are five thorny problems that experts will be bending their brains against next year.
The meaning of our words
The reality gap impeding the robot revolution
Guarding against AI hacking
Graduating beyond boardgames
Teaching AI to distinguish right from wrong