Explore how brain computations can inspire new paths in artificial intelligence in this lecture by Gabriel Kreiman from Harvard University and Children's Hospital Boston. Delve into current computational models and their limitations, examining topics such as occluded objects, backward masking, and limiting presentation time. Analyze observations and interpretations at the neurophysiological level, including individual trials and computational models like RNN. Investigate object recognition, minimal context, and contextual reasoning, while evaluating model performance. Examine computer graphics, adversary images, and the challenges of understanding humor in images. Gain insights into the intersection of neuroscience and AI, uncovering potential avenues for advancing machine learning algorithms inspired by human brain function.