We assess existing attempts to build emotions and feelings in machines. We review our recently proposed design for machines possessing analogues of biological feeling. Key to our proposal is a homeostatic architecture that regulates internal states to maintain conditions compatible with life. In a first implementation of our design, we present results from a model of synaptic homeostasis in artificial neural networks. We introduce direct consequences to the network's function as a result of its own information processing activity. This model illustrates the benefits that may accrue to a homeostatic learner when it is placed in a needful and vulnerable relation to the objects over which it computes.