Preserving Outputs Precisely while Adaptively Rescaling Targets

Multi-task learning – allowing a single agent to learn how to solve many different tasks – is a longstanding objective for artificial intelligence research. Recently, there has been a lot of excellent progress, with agents likeDQN able to use the same algorithm to learn to play multiple games including Breakout and Pong. These algorithms were used to train individual expert agents for each task. As artificial intelligence research advances to more complex real world domains, building a single general agent – as opposed to multiple expert agents – to learn to perform multiple tasks will be crucial. However, so far, this has proven to be a significant challenge.One reason is that there are often differences in the reward scales our reinforcement learning agents use to judge success, leading them to focus on tasks where the reward is arbitrarilyhigher. For example, in the Atari game Pong, the agent receives a reward of either -1, 0, or +1 per step. In contrast, an agent playing Ms. Pac-Man can obtain hundreds or thousands of points in a single step. Even if the size of individual rewards is comparable, the frequency of rewards can change over time as the agent gets better.Read More

Using AI to plan head and neck cancer treatments

Early results from our partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust suggest that we are well on our way to developing an artificial intelligence (AI) system that can analyse and segment medical scans of head and neck cancer to a similar standard as expert clinicians. This segmentation process is an essential but time-consuming step when planning radiotherapy treatment. The findingsalso show that our system can complete this process in a fraction of the time.Speeding up the segmentation processMore than half a million people are diagnosed each year with cancers of the head and neck worldwide. Radiotherapy is a key part of treatment, but clinical staff have to plan meticulously so that healthy tissue doesnt get damaged by radiation: a process which involves radiographers, oncologists and/or dosimetrists manually outlining the areas of anatomy that need radiotherapy, and those areas that should be avoided.Although our work is still at an early stage, we hope it could one day reduce the waiting time between diagnosis and treatment, which could potentially improve outcomes for cancer patients.Read More

Learning to Recognize the Irrelevant

A central task of natural-language-understanding systems, like the ones that power Alexa, is domain classification, or determining the general subject of a user’s utterances. Voice services must make finer-grained determinations, too, such as the particular actions that a customer wants executed. But domain classification makes those determinations much more efficient, by narrowing the range of possible interpretations.Read More