AI UK 2023 Conference – Rachael Laidlaw

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!

Highlights from NeurIPS 2022 and the 2nd Interactive Learning for NLP Workshop – Dr Edwin Simpson

This blog post is written by lecturer in Computer Science, Dr Edwin Simpson

In November I was lucky enough to attend NeurIPS 2022 in person in New Orleans, and take part as a co-organiser of InterNLP, our second interactive learning for NLP workshop. I had many interesting discussions around posters, talks and coffee breaks and took loads of photos of posters. It was hard to write up my highlights and without the post becoming endlessly long, so here is my attempt to pick out a handful of papers that caught my eye and tell you a little bit about how our workshop unfolded.

Main Conference

One topic generating a lot of buzz was in-context learning, where language models learn to perform new 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.

 

However, for few-shot learning, in-context learning may not be the best solution: Liu et al. [2] showed that fine-tuning a model by introducing a small number of additional weights can be cheaper and produce more accurate models.

 

 

Another interesting NLP paper from Jian, Gao and 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.

Sticking with the topic of uncertainty, Bayesian 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.

At the poster sessions, I also enjoyed learning about the wide range of new benchmarks and datasets that will enable lots of exciting future work. For example, one that relates to my own work that I’d like to make use of is BIGBIO [5], which makes a number of biomedical NLP datasets more accessible and will hopefully to more reproducible results.

Juho Kim, who is associate professor at Korea Advanced Institute of Science and Technology (KAIST), gave a keynote on his vision of Interaction-Centric AI. He called on AI researchers to move beyond data-centric or model-centric research by rethinking the complete AI research process around the user experience of AI. Juho’s talk gave examples of how an interaction-centric approach may affect the way we evaluate models, which cases we focus on when trying to improve accuracy, how to incentivise users to engage with AI, and several other aspects of interaction-centric AI that his lab has been working on. He demonstrated Stylette, a tool that lets you use natural language to change the appearance of a website. The keynote ended with a call to action for AI researchers to rethink performance metrics, the design process and collaboration, particularly with HCI researchers.

Geoff Hinton appeared remotely from home to present the Forward-Forward algorithm, a method for training neural networks without backpropagation that could give insights into how learning in the cortex takes place. His experiments showed some promising early results, and in the Q&A Geoff talked about coding the experiments himself. A preliminary arXiv paper is now out [6].

1. Garg et al., What Can Transformers Learn In-Context? A Case Study of Simple Function Classes, https://arxiv.org/abs/2208.01066

2. Liu et al., Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning, https://arxiv.org/abs/2205.05638

3. Jian, Gao and Vosoughi, Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings, https://arxiv.org/pdf/2209.09433.pdf

4. Vodrahalli, Gerstenberg and Zou, Uncalibrated Models Can Improve Human-AI Collaboration, https://arxiv.org/abs/2202.05983

5. Fries et al., BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing, https://arxiv.org/abs/2206.15076

6. Hinton, The Forward-Forward Algorithm: Some Preliminary Investigations, https://arxiv.org/abs/2212.13345

InterNLP Workshop

2022 was our second edition of the InterNLP workshop, and we were very happy that the community grew, this year with 20 accepted papers and a chance to meet in person!  Some of the videos are on youtube at https://www.youtube.com/@InterNLP. Others will hopefully be available soon on the NeurIPS archives

The programme was packed with impressive invited talks from Karthik Narasimhan (Princeton), John Langford (Microsoft), Dan Weld (UWashington), Anca Dragan (UCBerkeley) and Aida Nematzadeh (DeepMind). To pick out just a couple, Karthik presented recent work on semantic supervision [1] for few-shot generalization and personalization, which learns from semantic descriptions of classes, providing a way for instruct models through text. Anca Dragan talked about interactive agents that go beyond following instructions about how exactly to perform a task, to inferring the user’s goals, preferences, 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.

The contributed talks and posters showcased a wide range of work from human-in-the-loop learning 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.

 

 

 

 

At the end of the day, I got to ask a lot of questions to some very smart people during our panel 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.

We plan to be back at a future conference (not sure which one yet!) for the next iteration of InterNLP. Large language models and in-context learning are clearly revolutionizing this space in some ways, but I’m convinced we still have a lot of work to do to design interactive machine learning systems that are accountable, reliable, and require fewer resources.

Thank you to Nguyễn Xuân Khánh for letting us include his InterNLP workshop photos.

1. Aggarwal, Deshpande and Narasimhan, SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification, https://arxiv.org/pdf/2301.11309.pdf

2. Mishra and Diesner, PyTAIL: Interactive and Incremental Learning of NLP Models with Human in the Loop for Online Data, https://internlp.github.io/documents/2022/papers/24.pdf

3. Mohanty et al., Collecting Interactive Multi-modal Datasets for Grounded Language Understanding, https://internlp.github.io/documents/2022/papers/17.pdf

4. Ramamurthy et al., Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization, https://arxiv.org/abs/2210.01241

5. Majumder and McAuley, InterFair: Debiasing with Natural Language Feedback for Fair Interpretable Predictions, https://arxiv.org/abs/2210.07440

2023 AAAI Conference Blog – Amarpal Sahota

This blog post is written by AI CDT Student Amarpal Sahota

I attended the 37th AAAI Conference on Artificial Intelligence from the 7th of February 2023 to the 14th February. This was my first in person conference and I was excited to travel to Washington D.C.

The conference schedule included Labs and Tutorials February 7th – 8th , the main conference February 9th – 12th followed by the workshops on February 13th – 14th.

Arriving and Labs / Tutorials

I arrived at the conference venue on 7th February to sign in and collect my name badge. The conference venue (Walter E. Washington Convention Center) was huge and had within it everything you could need from areas to work or relax to restaurants and of course many halls / lecture theatres to host talks.

I was attending the conference to present a paper at the Health Intelligence Workshop. Two of my colleagues from the University of Bristol (Jeff and Enrico) were also attending to present at this workshop (we are pictured together below!).

The tutorials were an opportunity to learn from experts on topics that you may not be familiar with yourself. I attended tutorials on Machine Learning for Causal Inference, Graph Neural Networks and AI for epidemiological forecasting.

The AI for epidemiological forecasting tutorial was particularly engaging. The speakers were very good at giving an overview of historical epidemiological forecasting methods and recent AI methods used for forecasting before introducing state of the art AI methods that use machine learning combined with our knowledge of epidemiology. If you are interested, the materials for this tutorial can be accessed at : https://github.com/AdityaLab/aaai-23-ai4epi-tutorial .

Main conference Feb  9th – Feb 12th

The main conference began with a welcome talk in the ‘ball room’. The room was set up with a stage and enough chairs to seat thousands. The welcome talk introduced included an overview of the different tracks within the conference (AAAI Conference of AI, Innovative Application of AI, Educational Advances in AI) , statistics around conference participation / acceptance and introduced the conference chairs.

The schedule for the main conference each day included invited talks and technical talks running from 8:30 am to 6pm. Each day this would be followed by a poster session from 6pm – 8pm allowing us to talk and engage with researchers in more detail.

For the technical talks I attended a variety of sessions from Brain Modelling to ML for Time-Series / Data Streams and Graph-based Machine Learning. Noticeably, all of the sessions were not in person. They were hybrid, with some speakers presenting online. This was disappointing but understandable given visa restrictions for travel to the U.S.

I found that many of the technical talks became difficult to follow very quickly with these talks largely aimed at experts in the respective fields. I particularly enjoyed some of the time-series talks as these relate to my area of research. I also enjoyed the poster sessions that allowed us to talk with fellow researchers in a more relaxed environment and ask questions directly to understand their work.

For example, I enjoyed the talk ‘SVP-T: A Shape-Level Variable-Position Transformer for Multivariate Time Series Classification‘ by PhD researcher Rundong Zhuo. At the poster session I was able to follow up with Rundong to ask more questions and understand his research in detail.  We are pictured together below!

Workshops Feb 13th – 14th

I attended the 7th International Workshop On Health Intelligence from 13th to 14th February. The workshop began with opening remarks from the Co-chair Martin Michalowski before a talk by our first keynote speaker. This was Professor Randi Foraker who  spoke about her research relating to building trust in AI for Improving Health Outcomes.

This talk was followed by paper presentations with papers on related topics grouped into sessions. My talk was in the second session of the day titled ‘Classification’. My paper (pre-print here) is titled ‘A Time Series Approach to Parkinson’s Disease Classification from EEG’. The presentation went reasonably smoothly and I had a number of interesting questions from the audience about  applications of my work and the methods I had used. I am pictured giving the talk below!

The second half of the day focused on the hackathon. The theme of the hackathon was biological age prediction. Biological ageing is a latent concept with no agreed upon method for estimation. Biological age tries to capture a sense of how much you have aged in the time you have been alive. Certain factors such as stress and poor diet can be expected to age individuals faster. Therefore two people of the same chronological age may have different biological ages.

The hackathon opened with a talk on biological age prediction by Morgan Levin (The founding Principal Investigator at Altos Labs). Our team for the hackathon included four people from the University of Bristol – myself , Jeff , Enrico and Maha. Jeff (pictured below) gave the presentation for our team. We would have to wait until the second day of the conference to find out if we won one of the three prizes.

The second day of the workshop consisted of further research talks, a poster session and an awards ceremony in the afternoon. We were happy to be awarded the 3rd place prize of $250 for the hackathon! The final day concluded at around 5pm. I said my good byes and headed to Washington D.C. airport for my flight back to the U.K