Broadly, my research explores the reasoning capacity of neural networks. You might have seen these algorithms in action when using automatic face recognition on social media or issuing voice commands to your phone. Neural networks, also hidden behind media-friendly terms such as deep learning, are nowadays a go-to research direction if one is interested in attaining the state-of-the-art accuracy on a classification task associated with a large amount of data.
Despite their impressive practical performance, these models are limited in their ability to combine familiar ideas to arrive at new conclusions as they tend to simply memorise the data. Having learned from the examples of red squares and blue circles, a truly intelligent system surely shouldn’t be confused by a red circle. This is a core challenge in learning algorithms and I hope my research will contribute to the international efforts of the machine learning community to induce reasoning and generalisation in neural networks.
During my current internship at Mila Quebec AI Institute, I'm investigating how agents based on neural networks communicate with each other in order to solve simple games. These games draw inspiration from the studies on language evolution in humans. The communication aspect is particularly cool and exciting because by analysing the messages sent between the agents I can see how closely these algorithms mimic the reasoning process of a biological intelligent system.
I have been extremely fortunate with my supervisors (Mateja Jamnik and Sean Holden) as well as the welcoming and friendly nature of the Department of Computer Science and Technology. Cambridge provides students with a unique degree of freedom, independence and intellectual stimulation. What I particularly appreciate after my experience with competitive institutions in Poland and France is that Cambridge provides the best resources for obtaining a well-rounded education alongside the ‘hard skills’ in a student’s chosen field.
I’ve always liked the quote “the areas in which you struggle the most are the ones in which you have the most to give.” If you put a lot of effort into grasping a subject or solving a task that seems daunting to begin with, you are well-equipped to support others who struggle with the same task. I believe this also applies to challenges outside of research.
Embrace stepping out of the ‘good student’ role. The skills required in a research career, especially in science and technology, frequently won’t fully overlap with what led you to have the top grades in your previous education. Firstly, there won’t be nearly as much of the immediate positive feedback so it is crucial to enjoy the process apart from the results. Secondly, the work will never seem finished so it is more important to follow a healthy routine. Reach out to friendly experienced colleagues to find out how they cope with these challenges.
Work with a light and kind attitude to yourself and others. The trap of oscillating between imposter syndrome and ‘I’m like, a genius’ is real in research. At the end of the day you are learning, trying new things and having lots of fun, together with like-minded people.
Agnieszka Słowik is a PhD candidate in the Department of Computer Science and Technology, where she is a member of the artificial intelligence research group. Here, she tells us about neural networks and how they communicate with each other, the importance of supportive supervisors, and how to be a supportive team member.
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