1 Andrew Ng, Associate Professor of Computer Science Robots and Brains 2 Who wants a robot to clean your house? [Photo Credit: iRobot] 3 Stanford STAIR Robot [Credit: Ken Salisbury] 4 5 Whats missing? Control Perception The software 6 Stanford autonomous helicopter 7 Computer GPS Accelerometers Compass 8 9 Computer program to fly helicopter [Courtesy of David Shim] 10 Option 1 BLACK 11 Machine learning Option 2 12 Machine learning 13 Machine learning to fly helicopter 14 Whats missing? The software Control Perception 15 Robot, please find my coffee mug 16 Robot, please find my coffee mug Mug 17 Why is computer vision hard? But the camera sees this: 18 Computer programs (features) for vision SIFT Spin image HoG Textons Shape context GIST 19 Why is speech recognition hard? What a microphone records: Robot, please find my coffee mug. 20 Computer programs (features) for audio ZCR Spectrogram MFCC Rolloff Flux 21 The idea: Most of perception in the brain may be one simple program 22 Auditory cortex learns to see Auditory Cortex The one program hypothesis [Roe et al., 1992] 23 Somatosensory cortex learns to see The one program hypothesis Somatosensory Cortex [Roe et al., 1992] 24 Neurons in the brain 25 Neural Network x1x1 x2x2 x3x3 Output Layer L 1 Layer L 2 Layer L 4 Layer L 3 x4x4 26 How does the brain process images? Neuron #1 of visual cortex (model) Neuron #2 of visual cortex (model) Primary visual cortex looks for edges. 27 Comparing to Biology Learning algorithm Visual cortex [PICTURE] 28 Comparing to Biology Learning algorithm Auditory cortex [PICTURE] 29 Computer vision results (NORB benchmark) Neural Network: accuracy Classical computer vision (SVM): accuracy 30 Missed Mugs True positivesFalse positives 31 Missed Mugs True positivesFalse positives 32 Missed Mugs True positivesFalse positives 33 Missed Mugs True positivesFalse positives 34 Missed Mugs True positivesFalse positives Results using non-embodied vision 35 Missed Mugs True positivesFalse positives 36 Missed Mugs True positivesFalse positives Results using non-embodied vision 37 Missed Mugs True positivesFalse positives Classifications using embodied agent 38 Missed Mugs True positivesFalse positives 39 Missed Mugs True positivesFalse positives Results using non-embodied vision 40 Missed Mugs True positivesFalse positives 41 Missed Mugs True positivesFalse positives 42 Hope of progress in Artificial Intelligence