AI/ML platforms, trade secrets can include the structure of the AI/ML model, proprietary training data, a particular method of using the AI/ML …

AI/ML platforms, trade secrets can include the structure of the AI/ML model, proprietary training data, a particular method of using the AI/ML model, any output calculated by the AI/ML model that is converted into an end product for a customer, and aspects of the platform.

AI systems can sometimes be tricked into seeing something that's not actually there, as when Google's software "saw" a 3-D-printed turtle as a rifle. A way to stop these potential attacks is crucial before the technology can be widely deployed in safety-critical systems like the computer vision software behind self-driving cars. At MIT Technology Review's annual EmTech Digital conference in San Francisco this week, Google Brain researcher Ian Goodfellow explained how researchers can protect their systems. Goodfellow is best known as the creator of generative adversarial networks (GANs), a type of artificial intelligence that makes use of two networks trained on the same data. One of the networks, called the generator, creates synthetic data, usually images, while the other network, called the discriminator, uses the same data set to determine whether the input is real.

Recent progress in robotics and artificial intelligence has blurred the line between man and machine. After more than 50 years of research, artificial intelligence (AI) systems haven't quite matched their biological analogues, but they're catching up: Uber's self-driving truck drove 2,000 cases of beer over 120 miles, Google knows almost exactly what we're searching for before we've typed it out in full; and many of us speak naturally to assistants waiting in our pockets or our kitchens. This is only the beginning. As technology improves, our worldview must evolve to keep pace. It's time to start talking seriously about ethics in AI.