To your question 1)
Your question assumes that neural networks have no representation. Fact is, that neural networks have a layout (of layers, convolutional weight schemes) that are setup before training starts, and it highly depends on this layout what can be learned in that particular NN and whether it can solve a certain task. During learning, the weights in these layers are trained, and the overall configuration of weights again obtains a structure that allows to predict what is learned. This structure can be seen as a representation. Provocatively asked: the human brain is a neural network, so do you think humans cannot represent anything in their head? It is true that we cannot visualize the representation easily (see question 2).
Apart from this comment, I would say that the new version of Google Translate is probably the best example of a system "without representation" that applies CNN on a large scale in a productive system.
The research paper is here: Wu, Y., Schuster, M., Chen, Z., Le, Q. V, Norouzi, M., Macherey, W., ..., Dean, J. (2016). Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv:1609.08144 https://arxiv.org/abs/1609.08144
To your question 2)
Scientists are trying to visualize the decisions done in a neural network. This is especially important due to new laws that will require AI systems to give an account of how a decision was made. So far only logical systems provide a way to explain their decisions. If neural networks and other systems that do not use an explicit symbolic representation shall find their way into daily life, they will need to be able to explain their reasoning, or we will only be able to use logic programming and other rule-based systems like ontologies.
One recent work about explaining the reasoning of a neural network, already cited quite a bit, is `Abbeel, P., Chen, X., Duan, Y., Houthooft, R., Schulman, J., & Sutskever, I. (2016). InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. NIPS.' http://papers.nips.cc/paper/6399-infogan-interpretable-representation-learning-by-information-maximizing-generative-adversarial-nets.pdf