An important leap for artificial intelligence in recent years is machines' ability to teach themselves, through endless practice, to solve problems, from mastering ancient board games to navigating busy roads. But a few subtle tweaks in the training regime can poison this "reinforcement learning," so that the resulting algorithm responds--like a sleeper agent--to a specified trigger by misbehaving in strange or harmful ways. "In essence, this type of back door gives the attacker some ability to directly control" the algorithm, says Wenchao Li, an assistant professor at Boston University who devised the attack with colleagues. Their recent paper is the latest in a growing body of evidence suggesting that AI programs can be sabotaged by the data used to train them. As companies, governments, and militaries rush to deploy AI, the potential for mischief could be serious.

Artificial intelligence (AI) is being harnessed by researchers to track down genes that cause disease. A KAUST team is taking a creative, combined deep learning approach that uses data from multiple sources to teach algorithms how to find patterns between genes and diseases. Machine learning uses algorithms and statistical models to identify patterns and associations among data to solve specific problems. By inputting enough known data, like tagged images of "Jack," the system can eventually learn to suggest other nontagged images that include Jack. Researchers are using this application of AI to find genes that cause diseases.

Artificial intelligence (AI) is being harnessed by researchers to track down genes that cause disease. A KAUST team is taking a creative, combined deep learning approach that uses data from multiple sources to teach algorithms how to find patterns between genes and diseases. Machine learning uses algorithms and statistical models to identify patterns and associations among data to solve specific problems. By inputting enough known data, like tagged images of "Jack," the system can eventually learn to suggest other nontagged images that include Jack. Researchers are using this application of AI to find genes that cause diseases.

Artificial intelligence (AI) is being harnessed by researchers to track down genes that cause disease. A KAUST team is taking a creative, combined deep learning approach that uses data from multiple sources to teach algorithms how to find patterns between genes and diseases. Machine learning uses algorithms and statistical models to identify patterns and associations among data to solve specific problems. By inputting enough known data, like tagged images of "Jack," the system can eventually learn to suggest other nontagged images that include Jack. Researchers are using this application of AI to find genes that cause diseases.