This blog post is written by AI CDT student, Rachael Laidlaw
Last month, I took the exciting opportunity to attend AI UK 2023, a large-scale event organised by The Alan Turing Institute. It was my first conference outside of Bristol, held in the heart of London at the Queen Elizabeth II Centre – right by Westminster Abbey and Big Ben – and it promised to offer a diverse programme of activities with a broad range of interactive content. As such, the sessions were packed with novel material delivered by leading international thinkers across multiple disciplines, resulting in an in-depth exploration of how data science and AI can be used to solve real-world challenges.
On the day
After a short walk to the venue from my hotel in Piccadilly Circus, I signed in and collected my demonstrator lanyard before heading up to the third floor of the building to meet my colleagues from the Jean Golding Institute. We would be spending the day manning a stall for the Local Air initiative in the environmental section of the Fleming room, engaging with attendees from both academia and industry about a pollution monitoring system designed to be mounted on e-scooters.
Highlights included:
- using ground coffee to simulate particulate matter in the air and generate a live response from the prototype which was shown on the screen behind us,
- contemplating alternative applications for the noise-pollution sound sensors (i.e., for use in the study of bats) with representatives from the UK Centre for Ecology and Hydrology, and
- considering media coverage possibilities for the project with a journalist from the Financial Times.
Into the afternoon
When lunchtime arrived, I began circling the floor to visit the other stalls. Whilst wandering, I encountered displays of lots of innovative concepts, some of my favourites being:
- a family of domestic social robot pets developed by the company Konpanion to alleviate loneliness,
- progress on the tool BoneFinder, created by academics at University of Manchester for use in clinical practice to segment skeletal structures,
- a cardiac digital twin produced at King’s College London,
- SketchX’s headset that gives you the ability to build your own metaverse from rough virtual drawings, and
- the Data Hazards project, complete with holographic stickers and hi-vis jackets worn by another University of Bristol team to really bring data-oriented risk assessments to life.
Of the above, BoneFinder stood out to me in particular, owing to the fact that my current specialist focus is ecological computer vision, and, thus, seeing the same sort of technique being used for a medical application piqued my interest.
The talks
During a quiet period at the stall, I jumped at the chance of sitting in on a very well-attended talk by Gary Marcus from NYU on the power of ChatGPT and the unknowns surrounding the future of such pieces of technology. This was especially thought-provoking and relevant to my ongoing work towards a potential CHI publication.
After re-energising with some delicious cookies in the break, I also made it to an insightful panel discussion on shaping public perceptions of artificial intelligence, featuring Tracey Brown (the director of Sense About Science), Tania Duarte (the co-founder and CEO of We and AI) and David Leslie (a specialist in ethics and responsible innovation). This reminded me of the importance of keeping stakeholders in mind during all stages of research.
Closing moments
To round off the day, everyone came together to mingle and expand their networks over canapés and a significant amount of complimentary wine. We then gathered our belongings and headed out for dinner and to be tourists in London for the evening.
All in all, it was an incredibly fun and informative experience alongside a great team, and I’m already looking forward to future conferences!
tasks without updating their weights from examples given in the model’s input prompt. Models like GPT3 can perform in-context learning from small numbers of examples. Garg et al. presented an interesting paper that triez to understand what classes of functions can be learned in this way [1]. They were able to train Transformers that learn function classes including linear functions and two-layer neural networks.
Vosoughi [3] learns sentence embeddings usingimage and audio data alongside a text training set. The method works by creating pairs of images (or audio) using data augmentation, which are then embedded and fed through a BERT-like transformer to provide additional data for contrastive learning. This is especially useful for low-resource languages and domains, and it is really interesting that we can learn from different modalities without any parallel examples.
Many machine learning researchers are concerned with models that produce well-calibrated probabilities, but what difference does calibration make to end users? Vodrahalli, Gerstenberg and Zou [4] investigated a binary prediction task in which a classifier provides advice ta user, along with its confidence. They found that exaggerating the model’s confidence led the user to perform better. So, the classifier was uncalibrated and had higher training loss but the complete human-AI system was more effective, which shows how important it is for ML researchers to consider real-world use cases for their models.
deep learning aims to quantify uncertainty in complex neural network models, but is challenging to apply as it is difficult to specify a suitable prior distribution. Ideally, we’d specify a prior over the functions that the network encodes, rather than over individual network weights. Tran et al. [4] introduce a method for setting functional priors in Bayesian neural networks, by aligning them with Gaussian processes. It will be interesting to try out their approach in some deep learning applications where quantifying uncertainty is important.
few-shot generalization and personalization, which learns from semantic descriptions of classes, providing a way for instruct models through text. 
and constraints. She emphasized that the way people refer to desired actions provides important information about their preferences, and therefore we can infer, from a user’s language, reward functions that reflect their preferences. Aida Nematzadeh compared self-supervised pretraining to language learning in childhood, which involves interacting with other people. Her talk focused on the evaluation of neural representations, and she called for real-world evaluations, strong baselines and probing to provide a much more thorough way of uncovering the strengths and weaknesses of pretrained models.
techniques to software libraries and benchmark datasets. For example, PyTAIL [2] is a Python library for active learning that collects new labelling rules and customizes lexicons as well as collecting labels. Mohanty et al. [3] developed the IGLU challenge, in which an agent has to perform tasks by following natural language instructions; their presentation at InterNLP explained how they collected the data. The RL4M library [4] provides a way to optimize language generation models using reinforcement learning, as a way to adapt to human preferences; the paper [4] also presents a benchmark, GRUE, for evaluating RL methods for language generation. Majumder and McAuley [5] investigate the use of explanations to debias NLP models while maintaining a good trade-off between predictive performance and bias mitigation.
discussion – thanks to John Langford, Karthik Narasimhan, Aida Nematzadeh, and Alane Suhr for taking part, and thanks to the audience for some great interactions too. The wide-ranging discussion touched on the evaluation of interactive systems (how to use static data for evaluation, evaluating how well models adapt to user input), working with researchers and users from other fields, different forms of interaction besides language, and challenges that are specific to interactive NLP.
