We are interested in open-endedness at Uber AI Labs because it offers the potential for generating a diverse and ever-expanding curriculum for machine learning entirely on its own. Having vast amounts of data often fuels success in machine learning, and we are thus working to create algorithms that generate their own training data in limitless quantities. In the normal practice of machine learning, the researcher identifies a particular problem (for example, a classification problem like ImageNet or a video game like Montezuma’s Revenge) and then focuses on finding or designing an algorithm to achieve top performance.
Radiology and DeepLearningDetecting pneumonia opacities from chest X-Ray images using deep learning. One day back in August, I was catching up with my best friend from high school who is now a radiology resident. One thing led to another, and we started talking about our interests in artificial intelligence and machine learning and its possible applications in radiology.
A couple of months after our talk, I stumbled upon a Kaggle challenge hosted by the Radiological Society of North American (RSNA).
By now, all of you have probably followed deep learning research for quite a while. In 1998, LeCun et al. proposed the very simple MNIST data set of handwritten digits and showed with their LeNet-5 that we can achieve a high validation accuracy on it. The data sets subsequently proposed became more complex (e.g., ImageNet or Atari games), hence the models performing well on them became more sophisticated, i.e. complex, as well.