In the case of the UPS truck, both the computer system and the human were attempting to drive the car through a tight spot with very little wiggle room to the left or right. In most cases, the driver takes over by yanking the steering wheel in the opposite direction the software is trying to turn. That movement wasn't possible under these circumstances, however, leaving it unclear whether the car or the human was in control. The struggle for control was amplified by the lack of a sharp turn, preventing the driver from cranking the wheel to regain his steering input from the software.

Google has just released the latest version of its image captioning system as an open source model in TensorFlow. The new iteration is capable of providing image captions that are 93.9 percent accurate. According to Google, the new release brings forth significant improvements. The system is much quicker to train and can produce more accurate and detailed image descriptions when compared to the original. "Today's code release initializes the image encoder using the Inception V3 model, which achieves 93.9 percent accuracy on the ImageNet classification task," says Chris Shallue, a Google Brain team software engineer.