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The idea is that features of the raw input coming into the bottom layers - such as pixels in an image - trigger some of those neurons, which then pass on a signal to neurons in the layer above according to simple mathematical rules. Each neuron is connected to others in layers above and below it.
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Loosely modelled on the architecture of the brain, they are software structures made up of large numbers of digital neurons arranged in many layers. “Everybody was saying, ‘Wow, this is amazing, computers are finally able to understand the world,’” says Jeff Clune at the University of Wyoming in Laramie, who is also a senior research manager at Uber AI Labs in San Francisco, California.īut AI researchers knew that DNNs do not actually understand the world.
In 2011, Google revealed a system that could recognize cats in YouTube videos, and soon after came a wave of DNN-based classification systems. These kinds of system will, some experts think, form the story of the coming decade in AI research.
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To move beyond the flaws, he and others say, researchers need to augment pattern-matching DNNs with extra abilities: for instance, making AIs that can explore the world for themselves, write their own code and retain memories. “There are no fixes for the fundamental brittleness of deep neural networks,” argues François Chollet, an AI engineer at Google in Mountain View, California. In their efforts to work out what’s going wrong, researchers have discovered a lot about why DNNs fail. Another suggested that a hacker could use these weaknesses to hijack an online AI-based system so that it runs the invader’s own algorithms 3. But pixels maliciously added to medical scans could fool a DNN into wrongly detecting cancer, one study reported this year 2. Deep-learning systems are increasingly moving out of the lab into the real world, from piloting self-driving cars to mapping crime and diagnosing disease. Like many scientists, he has come to see them as the most striking illustration that DNNs are fundamentally brittle: brilliant at what they do until, taken into unfamiliar territory, they break in unpredictable ways. These problems are more concerning than idiosyncratic quirks in a not-quite-perfect technology, says Dan Hendrycks, a PhD student in computer science at the University of California, Berkeley.
Yet making alterations to inputs - in the form of tiny changes that are typically imperceptible to humans - can flummox the best neural networks around. They are part of daily life, running everything from automated telephone systems to user recommendations on the streaming service Netflix. These have proved incredibly successful at correctly classifying all kinds of input, including images, speech and data on consumer preferences. These are just some examples of how easy it is to break the leading pattern-recognition technology in AI, known as deep neural networks (DNNs). And they have tricked speech-recognition systems into hearing phantom phrases by inserting patterns of white noise in the audio. They have deceived facial-recognition systems by sticking a printed pattern on glasses or hats.
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Researchers have already demonstrated how to fool an AI system into misreading a stop sign, by carefully positioning stickers on it 1. Such an event hasn’t actually happened, but the potential for sabotaging AI is very real. These fooled the car’s onboard artificial intelligence (AI) into misreading the word ‘stop’ as ‘speed limit 45’. An accident report later reveals that four small rectangles had been stuck to the face of the sign. A self-driving car approaches a stop sign, but instead of slowing down, it accelerates into the busy intersection.