Author: j.crabbe

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

Posted on by j.crabbe

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

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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

Through the AI of the storm – Emily Vosper at the Allianz climate risk award 2022

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This blog post is written by CDT Student Emily Vosper

This December I travelled to Munich, Germany, to take part in the Allianz climate risk award. Allianz set up this initiative to acknowledge the work done by young scientists who aim to build resilience to and/or reduces the risk of extreme weather events that are exacerbated by climate change. The award is open to PhD candidates and post-doctoral researchers who first submit an essay that outlines their work and the top four are invited to Munich where they present to the Allianz team.

In previous years, finalists have been working on very different climate hazards, but by chance this year the finalists all came from a tropical cyclone and/or flooding background. The finalists consisted of Mona Hemmati (Columbia University) who is a postdoctal researcher specialising in flood-related risks in tropical cyclones, Peter Pfeiderer (Humboldt University Berlin) whose work includes studying seasonal forecasts of tropical cyclones and Daniel Kahl (University of California) who studies flood exposure on a demographic level to understand community vulnerability for his PhD.

On Monday evening, the finalists were invited to meet the Allianz climate risk team at a Bavarian tapas bar. This evening was a great opportunity to get to know a bit about each other in a more relaxed setting, and a chance to sample some of the local cuisine!

On Tuesday, we met at the Allianz offices for the award day. With an excited buzz in the air, the event commenced with a keynote talk by Dr. Nicola Ranger, Oxford University, who spoke on the need to implement climate resilient finance strategies and during the Q and A session there was active discussion on how this could be achieved effectively. We also heard from Chris Townsend, a member of the board of management for Allianz SE, who introduced us to Allianz’ legacy and highlighted the exciting work going on in the climate risk space. We then heard engaging talks from Mona and Peter before a coffee break, followed by an articulate talk from Daniel. As the final speaker, I rounded off the presentation with my talk about how I’ve been using a generative adversarial network to enhance the resolution of tropical cyclone rainfall data. All presentations were followed by a group Q and A session where we discussed the exciting possibility of a collaboration between the four of us as our projects are very complimentary in nature.

With the award in its sixth year, there is now an alumni network of previous finalists rich with expertise in climate hazards and ample opportunity for future collaboration, so watch this space!

Left to Right: Holger Tewes-Kampelmann (CEO Allianz Reinsurance), Peter Pfeiderer (Humboldt University Berlin), Dr. Sibylle Steimen (MD Advisory & Services, Allianz Reinsurance), Emily Vosper (University of Bristol), Mona Hemmati (Columbia University), Daniel Kahl (UC Irvine), Chris Townsend (Member of the Board of Management, Allianz SE) and Dr. Nicola Ranger (Smith School of Enterprise and the Environment, Oxford University).

BIAS 22 Review Day 1 – Daniel Bennett “Complexity and Embodiment in Human Computer Interaction”

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This blog post is written/edited by CDT Students  Amarpal Sahota and Oliver Deane

This was a thought provoking starting point and one that clearly has a large impact on human computer interaction.  Daniel stated that this is a line of research in psychology, cognitive science, and robotics, that has run somewhat parallel to mainstream psychology.

One of the initiators of this was James J Gibson. Gibson and others in the last 70 years did a lot of work on how we use resources outside of just the brain, in our environment and in our bodies, and coordinate all of these together to behave effectively.  Daniel stated that with the lens of embodiment we start focusing on processes, interactions, and relations, and the dynamics that follow and this is primarily a change in how we model things.

Therefore, to summarize one could consider the traditional cognitive model as a linear system. First we sense the world, then we form a representation of that world in our brain. Then the representation gets processed through a bunch of neatly defined modules, updates existing plans and intentions, and results in some representation of an action, which we then carry out. The embodied view is more complex as we are not simply in the world but also a part of it.  The world is changing constantly, and our behaviour and cognition is simply another physical process in this world.

At a high-level embodied approaches consider behaviour in the world as a kind of continual adjustment and adaptation, with most behaviours are grounded in a kind of improvisatory, responsive quality. Daniel shared a good example of this from Lucy Suchman related to canoeing where you may have an idea of your plan as you look down the river ‘I need to stay left there, slow down over there’ but at execution time you have to adapt your plan.

Daniel stated that a lot of work has been done observing a wide range of human behaviours, from technology interaction, to manning air-traffic control centres and crewing ships. In all of these contexts it is argued that our embodied skills – our adaptation and our implicit skills of coordination with the mess of the situation as it plays out – are the most important factor in determining outcomes.

Human Computer Interaction is increasingly focused on complex behaviours. Daniel talked about the idea that we’re going to do more and more in augmented reality and virtual reality. Computing will be integrated to support a wide range of everyday behaviours, which are not conventionally “cognitive” – you’re not sitting and thinking and making only very small movements with your fingers on a keyboard.

Daniel has a particular interest in musical performance and coordination of musicians. His perspective is that musical performance with technology, technology supported sports training and gaming, particularly team multiplayer games, are cases where static models of cognition seem to break down. He believes modelling in terms of processes and synchronization has great power.

Daniel then spoke about how interaction effects are important in Human Computer Interaction. Firstly, giving the example that notifications influence a person to use their phone. Secondly, the more a person uses their phone the more they cause notifications to appear. He posed the interesting question, how does one disentangle this hypothesis to find out the degree to which notifications influence us?

Daniel then spoke about how reciprocal, interaction dominant effects also play a significant role in the organisation of our individual skilled behaviour. He gave us an overview of his own research where he found evidence of interaction dominant coordination processes in a simple skilful game task, where users are asked to control a cursor to herd sheep.

BIAS 22 – Review day 2 keynote – Prof. Liz Sonenberg: “Imperfectly rational, rationally imperfect, or perfectly irrational…”

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Imperfectly rational, rationally imperfect, or perfectly irrational: challenges for human-centered AI keynote by Prof. Liz Sonenberg

This blog post is written/edited by CDT Students  Isabella Degen and Oliver Deane

Liz opened the second day of BIAS 22 with her thought-provoking and entertaining keynote speech about automatic decision-making aids. She demonstrated how we humans make perfectly irrational decisions and spoke about the implications of using Explainable Artificial Intelligence (XAI) for better decision-making. Liz’s talk mentioned a great body of research spanning psychology, mathematics, and computer science for which she kindly provides all the references here https://tinyurl.com/4njp563e.

Starting off, Liz presented research demonstrating how subtle influences in our life can change the decisions we make despite us thinking that we are making them completely rationally. What we believe is human rational decision-making in fact is littered with cognitive biases. Cognitive bias is when we create a subjective reality based on a pattern we perceive regardless of how representative that pattern is of all the information. Anchoring is a type of cognitive bias that happens when a decision of a person is influenced by an anchor such as a random number being shown while the person knows that they are being shown a random number that has nothing to do with their decision. An example Liz shared is an experiment by Englich et al who used irrelevant anchors to change experts’ decision-making. In the experiment young judges were asked to discover the length of the sentence for a theft crime by throwing a dice. Unkown to the judges the dice was rigged: for one group of judges it would throw high numbers, for the other it would throw low numbers. The judges knew that throwing a dice should not influence their decision. However, the result was that the group with the dice giving low numbers gave a 5 months sentence while the group with the dice giving high numbers gave an 8 months sentence. This is not the only kind of cognitive bias. Human decision making also suffers from framing bias where the way in which data is presented can affect the decision we make. As well as confirmation bias where we tend to interpret new information as a confirmation of our existing beliefs without considering that we only ever observe a limited kind of information and so forth. With these examples Liz made us doubt how clearly and rationally we humans can make decisions.

The irrationality of humans is an interesting challenge to consider for researchers attempting to create intelligent systems that help us humans make better decisions. Should we copy the imperfect human rationality in intelligent agents, or should we make them more rational than humans? And what does that mean for interactions between human and intelligent systems? Research shows that it is important that human operators have a sense of what the machine is doing to be able to interact with it. From accidents such as the Three Mile Island’s partial meltdown of a nuclear reactor, we can learn how important it is to design systems in a way that does not overwhelm the human operator with information. The information presented should be just enough to enable an operator to make a high-quality decision. It should help the operator to know when they can trust the decision the machine made and when to interrupt. When designing these systems, we need to keep in mind that people suffer from biases such as automation bias. Automation bias happens when a human cannot make a decision based on the information the machine provides and instead decides to just go with the machine’s decision knowing that the machine is more often right than the human. Sadly, this means that a human interacting with a machine might not be able to interrupt the machine at the right moment. We know that human decision-making is imperfectly rational. And while automation bias appears to be an error, it is in fact a rational decision in the context of limited information and time available to the human operator.

One promise of XAI is to use explanations to counteract various cognitive biases and with that help a human operator to make better decisions together with an intelligent system. Liz made a thought-provoking analogy to the science of magic. Magicians use our limited memory and observation abilities to manipulate our feelings and deceive us and make the impossible appear possible. A magician knows that the audience tries to spot how the trick works. And on the other hand, the audience also knows that the magician tries to deceive them and that they are trying to discover how the trick works. Magicians understand their audience well. They know what humans really do and exploit the limited resources they have. Like in magic human-centered AI systems ought to anticipate how perfectly irrational we humans make decisions to enable us to make better decisions and counteract our biases.

BIAS 22 – Review day 2 talk – Dr Oliver Ray: “Knowledge-driven AI”

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This blog post is written/edited by CDT Students  Daniel Collins and Matt Clifford

BIAS 22 – Day 2, Dr Oliver Ray: “Knowledge-driven AI”

The second talk of day two was delivered by Dr Oliver Ray (University of Bristol), on the topic of human-in-the-loop machine learning using Inductive Logic Programming (ILP) and its application in cyber threat elucidation.

Cyber threat elucidation is the task of analysing network activity to identify ransomware attacks, and to better understand how they unfold. Ransomware is a type of malware which infects victims’ devices, encrypts their data, and demands money from them to restore access. Infection typically occurs through human error. For example, a person may be unwittingly tricked into downloading and running a “trojan” – malware that has been disguised as a legitimate and benign file. The executed ransomware encrypts data, and backups of that data, on the infected system, and the perpetrator can then demand a ransom payment for decryption services. However, ransomware does not always start encrypting data immediately. Instead, it may lay relatively dormant whilst it spreads to other networked systems, and spend time gathering sensitive information, and creating back-ups of itself to block data recovery. If an attack can be identified at this stage or soon after it has started encrypting data, it can be removed before most of the data has been affected.

Ransomware is a persistent threat to cyber security, and each new attack can be developed to behave in unpredictable ways. Dr Ray outline the need for better tools to prepare for new attacks – when faced with a new attack, there should be systems to help a user understand what is happening and what has happened already so that ransomware can be found and removed as quickly as possible, and relevant knowledge can be gained from the attack.

To identify and monitor threats, security experts may perform forensic analysis of Network Monitoring Systems (NMS) data from around the time of infection. This data exists in the form of network logs – relational databases containing a time-labelled record of events and activity occurring across the networked systems. However, there are very large amounts of log data, and most of it is associated with benign activity, unrelated to the threat, making it difficult to find examples of malicious activity. Further, in the case of new threats, there are little to no labelled examples of logs known to be related to an attack. Human knowledge and reasoning are therefore crucial for identifying relevant information in the logs.

ILP based machine learning (ML) was then presented by Dr Ray as a promising alternative to more ‘popular’ traditional ML methods for differentiating ransomware activity from benign activity in large network logs.  This is because ILP is better suited for working with relational data, an area where deep learning and traditional ML methods can struggle since often require tabular or vectorisable data formats. ILP not only gives the ability to make predictions on relational data, but it also produces human interpretable logic rules through which it is possible to uncover and learn about the system itself. This could provide valuable insights into how the infection logs are generated, and which features of the logs are important for identification, as opposed to guessing which features might be important.

Dr Ray went on to detail the results of his work with Dr Steve Moyle (Amplify Intelligence UK and Cyber Security Centre, University of Oxford), on a novel proof-of-concept for an ILP based “eXplanatory Interactive Relational Machine Learning” (XIRML) system called “Acuity”. This human-in-the-loop system allows ILP and cyber security experts to direct the cyber threat elucidation process, through interactive functionality for guided data-caching on large network logs, and hypothesis-shaping for rebutting or altering learned logic rules.

In his concluding remarks, Dr Ray shared his thoughts on the future of this technology. As he sees it, the goal is to develop safe, auditable systems that could be used in practice by domain experts alone, without the need for an ILP expert in the loop. To this end, he suggests that system usability and human-interpretable outputs are both crucial factors for the design of future systems.

BIAS 22 – Review Day 2 talk – Dr Nirav Ajmeri: “Ethics in Sociotechnical Systems'”

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This blog post is written/edited by CDT Students Jonathan Erkine and Jack Hanslope

Following from a great keynote by Liz Sonenberg, Dr Nirav Ajmeri presented a discussion on Ethics in Socio-Technical Systems (STS).

As is common practice in discussions on AI, we began by looking inwards to what kind of human behaviour we are trying to replicate – what aspect of intelligence have we defined as our objective? In this case it was the ability of machines to make ethical decisions. Dr. Ajmeri referred to Kantian and Aristotelian ethical frameworks which describe moral duty and virtuous behaviour to establish an ethical baseline, which led to the first main takeaway of the discussion:

We must be capable of expressing how humanity defines, quantifies, and measures ethics before discussing how we might synthesise ethical behaviour.

Dr. Ajmeri clarified that ethical systems must be robust to situations where there are “no good choices”. That is, when even a human might struggle to see the most ethical path forwards.  Keen to move away from the trolley problem, Nirav described a group of friends who can’t agree on a restaurant for their evening meal, expounding on the concepts of individual utility, rationality, and fairness to explain why science might fail to resolve the problem.

The mathematical solution might be a restaurant that none of them enjoy, and this could be the same restaurant for every future meal which they attend. From this example, the motivation behind well-defined ethics in socio-technical systems becomes clear; computers lack the ability to apply emotion when reasoning about the impact of their decisions, leading to the second lesson which we took from this talk;

Ethical integration of AI into society necessitates the design of socio-technical systems which can artificially navigate “ethical gridlock”.

Dr. Ajmeri then described the potential of multiagent systems research for designing ethical systems by incorporating agents’ value preferences (ethical requirements) and associated negotiation techniques. This led to a good debate on the merits and flaws of attempting to incorporate emotion into socio-technical systems, with questions such as:

Can the concept of emotion be heuristically defined to enable pseudo-emotional decision making in circumstances when there is no clear virtuous outcome?

Is any attempt to incorporate synthetic emotion inherently deceitful?

These questions were interesting by the very nature that they couldn’t be answered, but the methods described by Nirav did, in the authors opinion, describe a system which could achieve what was required of it – to handle ethically challenging situations in a fair manner.

What must come next is the validation of these systems, with Nirav prompting that the automated handling of information with respect to the (now not-so-recent) GDPR regulations would provide a good test bed, prompting the audience to consider what this implementation might involve.

The end of this talk marked the halfway point of the BIAS summer school, with plenty of great talks and discussions still to come. We would like to thank Dr. Nirav Ajmeri for this discussion, which sits comfortably in the wheelhouse of problems which the Interactive AI CDT has set out to solve.

BIAS 22 – Review Day 1 – Dr James Cussens: ‘Algorithms for learning Bayesian networks’

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BIAS 22 DAY 1, TALK 1 

This blog post is written by CDT Students  Roussel Desmond Nzoyem, Davide Turco and Mauro Comi 

This Tuesday 06th September 2022 marked the start of the second edition of the Bristol Interactive AI Summer School (BIAS): a unique blend of events (talks, workshops, etc.) focusing on machine learning and other forms of AI explored in the Interactive AI CDT.

Following the tradition, BIAS22 began with a few words of introduction from the CDT director, Professor Peter Flach. He welcomed and warmly thanked the roomful of attendees from academia and industry.

Prof Flach proceeded with a passionate presentation of the range of speakers while giving the audience a brief taste of what to expect during the 3-day long event: talks, workshops, along with a barbecue! He remarked on the variety of Interactive AI ingredients that would be touched: data-driven AI, knowledge-driven AI, human-AI interaction, and Responsible AI.

Prof Flach’s introduction ended with an acknowledgement of the organisers of the event.

 

Dr James Cussens: ‘Algorithms for learning Bayesian networks’

The first talk of the day was an introduction to Bayesian networks and methods to learn them, given by our very own James Cussens.

Bayesian networks (BN) are directed acyclic graphs, in which each node represents a random variable. The important aspects of these networks, as Dr Cussens highlighted, is that they both define probabilistic distributions and causality relationships: this makes Bayesian networks a popular tool in complex fields such as epidemiology and medical sciences.

Learning BNs is a form of unsupervised learning, based on the assumption that the available data (real or simulated) is generated by an underlying BN. There are multiple reasons for learning a BN from data, such as learning a data-generating probability distribution or learning conditional independence relationships between variables; the talk, however, focused on learning a BN in order to estimate a causal model of the data, which is a task not easy to complete with other machine learning approaches we study and use in the CDT.

A popular algorithm for learning the structure of a BN, the so-called DAG, is constraint-based learning: the basic idea behind this algorithm is to perform statistical tests on data and find a DAG which is consistent with the outcomes of the tests. However, this approach presents some issues: for example, different DAGs could encode the same set of conditional independence relationships.

Dr Cussens then proceeded to introduce DAGgity, a widely used software for creating DAGs and analysing their causal structure. It is important to note that DAGgity does not learn DAGs from data, but allows the researcher to perform interventions and graph surgery. For example, it could allow a clinician to infer a treatment-response causal effects without doing that in practice. The talk also included a small excursus on score-based learning of BNs, which is a Bayesian approach to learning these networks, I.e., it has a prior formulation.

There are many different methods for learning BNs and evaluation is key for choosing the best method. Dr Cussens introduced benchpress, a framework for performing method evaluation and comparison, and showed some results from the benchpress paper, including the evaluation of his own method, GOBNILP (Global Optimum Bayesian Network via Inductive Logic Programming).

We are thankful to James Cussens for opening the BIAS22 with his talk; it was great to get an introduction to these methods that put together many aspects of our CDT, such as causality and graphical models.

 

BIAS 22 – Review Day 1: Professor James Ladyman: “Attributing cognitive and affective states to AI systems”

Posted on by j.crabbe

This blog post is written by CDT Student Henry Addison

BIAS 22 – Human stupidity about artificial intelligence: My thoughts on Professor James Ladyman’s talk “Attributing cognitive and affective states to AI systems”,  Tuesday 6th September 2022

The speaker arrives, sweaty, excited after his dash across town. The AI never stops working so perhaps he cannot either. He is a doom-monger. How are the lying liars in charge lying to you? When those people-in-charge run large tech companies, how do they take advantage of our failures to both miss and over-attribute cognitive and affective states and agency to AI in order to manipulate us?

AI is a field that seeks to decouple capabilities that in humans are integrated. For humans interacting with them, this can surprise us. In adult humans intelligence is wrapped up with sentience, autonomy is wrapped up with responsibility. Not so the chess robot – very good at chess (which for a human requires intelligence) but not sentient – nor the guided targeting system for which the responsibility of target-picking is left to a human.

Humans are overkeen to believe an AI cares, understands, is autonomous. These words have many meanings to humans, allowing the decoupling of capabilities to confuse us. “Who cares for granny?” This may be a request for the nurse (human or robot) who assists your grandmother in and out of the bath. Or it may be a request of despair by a parent trying to get their children to help prepare a birthday party. If an AI is autonomous, is it a moral agent that is responsible for what it does?

The flip side of the coin are the capabilities that we do not attribute to an AI, perhaps because they are capabilities human do not have. We lose sight of important things. Like how the machines notice and store away far more than we expect and then use these data to serve us ads, recommend us films, deny us loans, guide us to romantic partners, get us hooked on the angry ramblings of narcissists, lock us up.

AI is shit. AI is ruining us. AI is enabling a descent into fascism. But presumably there is enough hope for him to bother building up such a sweat to come and talk to us. We must do more philosophizing so more people can understand AI and avoid unconscious manipulation by it. The business models of data that take advantage of us are our fault, they are human creations not technical necessities but that means we can change them.

Then again how was the speaker lying to me? How is your lying liar of an author lying to you?

AI Worldbuilding Contest – Future Life Institute

Posted on by j.crabbe

This blog post is written by CDT Students Tashi Namgyal and  Vanessa  Hanschke.

Two Interactive AI CDT students were part of a team that won third place in the AI Worldbuilding Contest run by the Future of Life Institute along with their three non-CDT teammates. In this blog post, we would like to tell you more about the competition, its goals and our team’s process of creating the submission.

The Future of Life Institute describe themselves as “an independent non-profit that works to reduce extreme risks from transformative technologies, as well as steer the development and use of these technologies to benefit life”. Besides running contests, their work consists of running grants programs for research projects, educational outreach or engaging in AI policymaking internationally and nationally in the US.

The worldbuilding competition was aimed at creating a discussion around a desirable future, in which Artificial General Intelligence (AI that can complete a wide range of tasks roughly as well as humans) played a major role in shaping the world. The deliverables included a timeline of events until 2045, two “day in the life” short stories, 13 answers to short question prompts and a media piece.

While dystopian or utopian visions of our future are quite commonplace in science fiction, the particular challenge of the competition was to provide an account of the future that was both plausible and hopeful. This formulation raised a lot of questions such as: For whom will the future be hopeful in 2045? How do we resolve or make progress towards existing crises such as climate change that threaten our future? We discussed these questions at length in our meetings before we even got to imagining concrete future worlds.

Our team was composed of different backgrounds and nationalities: we had two IAI CDT PhD students, one civil servant, one Human Computer Interaction researcher and one researcher in Creative Informatics. We were brought together by our shared values, interests, friendship, and our common homes, Bristol and Edinburgh. We tried to exploit these different backgrounds to provide a diverse picture of what the future could look like. We generated future visions for domains that could be influenced by Artificial General Intelligence (AGI), that are often low-tech, but a core part of human society such as art and religion.

To fit the project into our full-time working week, we decided that we would meet weekly during the brainstorming phase to collect ideas and create drafts for stories, events and question prompts on a Miro board. Each week we would also set each other small tasks to build a foundation of our world in 2045, for example everyone had to write a day in the life story for their own life in 2045. We then chose a weekend closer to the deadline, where we had a “Hackathon”-like intense two days to work on more polished versions of all the different parts of the submission. During this weekend we went through each other’s answers, gave each other feedback and made suggestions to make the submission more cohesive. Our team was selected as one of the 20 finalists out of 144 entries and there was a month for the public to give feedback on whether people felt inspired by or would like to live in such worlds, before the final positions were judged by FLI.

Thinking about how AI tools may be used or misused in the future is a core part of the Interactive AI CDT. The first-year taught module on Responsible AI introduces concepts such as fairness, accountability, transparency, privacy and trustworthiness in relation to AI systems. We go through case studies of where these systems have failed in each regard so we can see how ethics, law and regulation apply to our own PhD research, and in turn how our work might impact these things in the future. In the research phase of the programme, the CDT organises further workshops on topics such as Anticipation & Responsible Innovation and Social & Ethical Issues and there are international conferences in this area we can join with our research stipends, such as FAccT.

If you are curious, you can view our full submission here or listen to the podcast, which we submitted as media piece here. In our submission, we really tried to centre humanity’s place in this future. In summary, the world we created was to make you feel the future, really imagine your place in 2045. Current big tech is not addressing the crises of our times including inequality, climate change, war, and pestilence. Our world seeks to imagine a future where human values are still represented – our propensity for cooperation, creativity, and emotion. But we had to include a disclaimer for our world: our solutions are still open to risk of human actors using them for ill purposes. Our solution for regulating AGI was built on it being an expensive technology in the hand of few companies and regulated internationally, but we tried to think beyond the bounds of AGI. We imagine a positive future grounded in a balanced climate, proper political, social and economic solutions to real world problems, and where human dignity is maintained and respected.

 

 

 

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