Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com Recently, Feedback was delighted to peruse the raciest conference invitation we have ever received. We get a lot of conference invites from organisers labouring under the delusion we are doing something akin to science journalism, and they are mostly a little prosaic: what's new in G-protein signalling, more findings about the biology of molluscs, that kind of thing. Here is the opening line: "From its groundbreaking inception in London to its spectacular evolution in the vibrant heart of China, the Love and Sex with Robots Conference is gearing up for its most thrilling chapters yet: its landmark 12th International edition, scheduled for June 2026."

There has been difficulty utilising conditional statements as part of the neural network graph (e.g. if input $> x$, pass input to network $N$). This is due to the inability to backpropagate through branching conditions. The Linear Array of Conditions, TOpologies with Separated Error-backpropagation (LACTOSE) Algorithm addresses this issue and allows the conditional use of available machine learning layers for supervised learning models. In this paper, the LACTOSE algorithm is applied to a simple use of DDSP, however, the main point is the development of the "if" conditional for DDSP use. The LACTOSE algorithm stores trained parameters for each user-specified numerical range and loads the parameters dynamically during prediction.