The next time you order a takeaway, it might be flown directly to your door. Today, Deliveroo has launched its first drone delivery service for customers in Ireland. Drones travelling at speeds of up to 50 miles per hour (80 kph) will carry food from restaurants to customers in as little as three minutes. Upon arrival, the drone will hover above the customer's home and gently lower the food to the ground on a tether before returning to the delivery hub. Launching in Blanchardstown, on the outskirts of Dublin, the trial will cover a 1.8-mile (3km) radius, reaching up to 150,000 people.

Pretrained language models have demonstrated extraordinary capabilities in language generation. However, real-world tasks often require controlling the distribution of generated text in order to mitigate bias, promote fairness, and achieve personalization. Existing techniques for controlling the distribution of generated text only work with quantified distributions, which require pre-defined categories, proportions of the distribution, or an existing corpus following the desired distributions. However, many important distributions, such as personal preferences, are unquantified. In this work, we tackle the problem of generating text following arbitrary distributions (quantified and unquantified) by proposing Nano, a few-shot human-in-the-loop training algorithm that continuously learns from human feedback. Nano achieves state-of-the-art results on single topic/attribute as well as quantified distribution control compared to previous works. We also show that Nano is able to learn unquantified distributions, achieves personalization, and captures differences between different individuals' personal preferences with high sample efficiency.