Latest Posts – Page 4 – Interactive AI CDT Blog

My Experience of Being a Student Ambassador

Posted on October 27, 2021April 5, 2022 by pr16096

This week’s blog post is written by CDT Student Grant Stevens

Widening participation involves the support of prospective students from underrepresented backgrounds to access university. The covered students include, but are not limited to, those:

from low-income backgrounds and low socioeconomic groups
who are the first in their generation to consider higher education
who attend schools and colleges where performance is below the national average
who are care experienced
who have a disability
from underrepresented ethnic backgrounds.

Due to my background and school, I matched the widening participation criteria for universities and was eligible for some fantastic opportunities. Without which I would not be in the position I am today.

I was able to attend a residential summer school in 2012 hosted by the University of Bristol. We were provided with many taster sessions for a wide variety of courses on offer from the university. I had such a good time that I applied the year after to attend the Sutton Trust Summer School, which was also held at Bristol uni.

A bonus of these opportunities was that those who took part were provided with a contextual offer (up to two grades below the standard entry requirement for their course), as well as a guaranteed offer (or guaranteed interview if the course required it). These types of provisions are essential to ensure fair access for those who may be disadvantaged by factors outside of their control. In my case, the reduced offer was vital as my final A-Level grades would have led to me missing out on the standard offer.

Although I enjoyed the taster sessions and loved the city, the conversations I had with the student ambassadors had the most impact on me and my aspirations for university. Hearing about their experiences and what they were working on at university was incredibly inspiring.

Due to how impactful it had been hearing from current students, I signed up to be a student ambassador myself when I arrived at Bristol. It’s a job that I have found very enjoyable and extremely rewarding. I have been very fortunate in having the opportunity to interact with so many people from many different backgrounds.

I am now entering my 7th year as a student ambassador for the widening participation and outreach team. I still really enjoy my job, and throughout this time, I have found that working on summer schools always turns out to be the highlight of my year. I’m not sure whether this is because I know first-hand the impact being a participant on these programmes can have or because over the week, you can really see the students coming out of their shells and realising that they’re more than capable of coming to a university like Bristol.

Being in this role for so long has also made me realise how much of an impact the pandemic can have on these types of programmes. I was relieved that at Bristol, many of these schemes have been able to continue in an online form. The option of 100% online programmes has its benefits but also its limitations. It allows us to expand our audience massively as we no longer have spacing restraints. However, zoom calls cannot replace physically visiting the university or exploring the city when choosing where to study. That’s why hearing from current students about their course and what to expect at university is more important than ever. For this reason, I have started to branch out to help with schemes outside of the university. I have presented talks for schools through the STEM Ambassador programme; I recently gave a talk to over 100 sixth formers during the Engineering Development Trust’s Insight into University course, and I also look forward to working with the Sutton Trust to engage with students outside of just Bristol.

In the last few years since starting my PhD, my message to students has changed a little. It has often been about “what computer science isnt.” clearing up misconceptions about what the subject at uni entails. That is still part of my talks; however, now I make sure to put in a section on how I got where I am today. It wasn’t plane sailing, and definitely not how I would have imagined a “PhD student’s” journey would go: from retaking a whole year in sixth form to having a very poor track record with exams at undergrad. I think it’s really important to let students know that regardless of their background, and even when things really don’t go to plan, it’s still possible to go on to do something big like a PhD. That is something I would’ve loved to hear back in school, so hopefully, it’s useful for those thinking (and potentially worrying) about uni now.

Some of the best people I’ve met and the best memories I’ve had at university have come from my student ambassador events. It’s something I feel very passionate about and find very enjoyable and rewarding. I have been very fortunate with the opportunities that have been available to me and am incredibly grateful as, without them, I wouldn’t be studying at Bristol, let alone be doing a PhD. By being a part of these projects, I hope I can inspire the next set of applicants in the same way my ambassadors inspired me.

Neglected Aspects of the COVID-19 pandemic

Posted on October 13, 2021April 5, 2022 by pr16096

This week’s post is written by IAI CDT student Gavin Leech.

I recently worked on two papers looking at neglected aspects of the COVID-19 pandemic. I learned more than I wanted to know about epidemiology.

The first: how much do masks do?

There were a lot of confusing results about masks last year.

We know that proper masks worn properly protect people in hospitals, but zooming out and looking at the population effect led to very different results, from basically nothing to a huge halving of cases.

Two problems: these were, of course, observational studies, since we don’t run experiments on the scale of millions. (Or not intentionally anyway.) So there’s always a risk of missing some key factor and inferring the completely wrong thing.

And there wasn’t much data on the number of people actually wearing masks, so we tended to use the timing of governments making it mandatory to wear masks, assuming that this caused the transition to wearing behaviour.

It turns out that the last assumption is mostly false: across the world, people started to wear masks before governments told them to. (There are exceptions, like Germany.) The correlation between mandates and wearing was about 0.32. So mask mandate data provide weak evidence about the effects of mass mask-wearing, and past results are in question.

We use self-reported mask-wearing instead: the largest survey of mask wearing (n=20 million, stratified random sampling) and obtain our effect estimates from 92 regions across 6 continents. We use the same model to infer the effect of government mandates to wear masks and the effect of self-reported wearing. We do this by linking confirmed case numbers to the level of wearing or the presence of a government mandate. This is Bayesian (using past estimates as a starting point) and hierarchical (composed of per-region submodels).

For an entire population wearing masks, we infer a 25% [6%, 43%] reduction in R, the “reproduction number” or number of new cases per case (B).

In summer last year, given self-reported wearing levels around 83% of the population, this cashed out into a 21% [3%, 23%] reduction in transmission due to masks (C).

One thing which marks us out is being obsessive about checking this is robust; that different plausible model assumptions don’t change the result. We test 123 different assumptions about the nature of the virus, of the epidemic monitoring, and about the way that masks work. It’s heartening to see that our results don’t change much (D)

It was an honour to work on this with amazing epidemiologists and computer scientists. But I’m looking forward to thinking about AI again, just as we look forward to hearing the word “COVID” for the last time.

The second: how much does winter do?

We also look at seasonality: the annual cycle in virus potency. One bitter argument you heard a lot in 2020 was about whether we’d need lockdown in the summer, since you expect respiratory infections to fall a lot in the middle months.

We note that the important models of what works against COVID fail to account for this. We look at the dense causal web involved:

This is a nasty inference task, and data is lacking for most links. So instead, we try to directly infer a single seasonality variable.

It looks like COVID spreads 42% less [25% – 53%, 95% CI] from the peak of winter to the peak of summer.

Adding this variable improves two of the cutting-edge models of policy effects (as judged by correcting bias in their noise terms).

One interesting side-result: we infer the peak of winter, we don’t hard-code it. (We set it to the day with the most inferred spread.) And this turns out to be the 1st January! This is probably coincidence, but the Gregorian calendar we use was also learned from data (astronomical data)…

—

BIAS Day 4 Review: ‘Data-Driven AI’

Posted on October 6, 2021April 5, 2022 by pr16096

This review of the 4th day of the BIAS event, ‘Data-Driven AI’, is written by CDT Student Stoil Ganev.

The main focus for the final day of BIAS was Data-Driven AI. Out of the 4 pillars of the Interactive AI CDT, the Data-Driven aspect tends to have a more “applied” flavour compared to the rest. This is due to a variety of reasons but most of them can be summed up in the statement that Data-Driven AI is the AI of the present. Most deployed AI algorithms and systems are structured around the idea of data X going in and prediction Y coming out. This paradigm is popular because it easily fits into modern computer system architectures. For all of their complexity, modern at-scale computer systems generally function like data pipelines. One part takes in a portion of data, transforms it and passes it on to another part of the system to perform its own type of transformation. We can see that, in this kind of architecture, a simple “X goes in, Y comes out” AI is easy to integrate, since it will be no different from any other component. Additionally, data is a resource that most organisations have in abundance. Every sensor reading, user interaction or system to system communication can be easily tracked, recorded and compiled into usable chunks of data. In fact, for accountability and transparency reasons, organisations are often required to record and track a lot of this data. As a result, most organisations are left with massive repositories of data, which they are not able to fully utilise. This is why Data-Driven AI is often relied on as a straight forward, low cost solution for capitalising on these massive stores of data. This “applied” aspect of Data-Driven AI was very much present in the talks given at the last day of BIAS. Compared to the other days, the talks of the final day reflected some practical considerations in regards to AI.

The first talk was given by Professor Robert Jenssen from The Arctic University of Norway. It focused on work he had done with his students on automated monitoring of electrical power lines. More specifically how to utilise unmanned aerial vehicles (UAVs) to automatically discover anomalies in the power grid. A point he made in the talk was that the amount of time they spent on engineering efforts was several times larger than the amount spent on novel research. There was no off the shelf product they could use or adapt, so their system had to be written mostly from scratch. In general, this seems to be a pattern with AI systems where even, if the same model is utilised, the resulting system ends up extremely tailored to its own problem and cannot be easily reused for a different problem. They ran into a similar problem with the data set, as well. Given that the problem of monitoring power lines is rather niche, there was no directly applicable data set they could rely on. I found their solution to this problem to be quite clever in its simplicity. Since gathering real world data is rather difficult, they opted to simulate their data set. They used 3D modelling software to replicate the environment of the power lines. Given that most power masts sit in the middle of fields, that environment is easy to simulate. For more complicated problems such as autonomous driving, this simulation approach is not feasible. It is impossible to properly simulate human behaviour, which the AI would need to model, and there is a large variety in urban settings as well. However, for a mast sitting in a field, you can capture most of the variety by changing the texture of the ground. Additionally, this approach has advantages over real world data as well. There are types of anomalies that are so rare that they might simply not be captured by the data gathering process or be too rare for the model to notice them. However, in simulation, it is easy to introduce any type of anomaly and ensure it has proper representation in the data set. In terms of the architecture of the system, they opted to structure it as a pipeline of sub-tasks. There are separate models for component detection, anomaly detection, etc. This piecewise approach is very sensible given that most anomalies are most likely independent of each other. Additionally, the more specific a problem is, the easier and faster it is to train a model for it. However, this approach tends to have larger engineering overheads. Due to the larger amount of components, proper communication and synchronisation between them needs to be ensured and is not a given. Also, depending on the length of the pipeline, it might become difficult to ensure that it perform fast enough. In general I think that the work Professor Jenssen and his students did in this project is very much representative of what deploying AI systems in the real world is like. Often your problem is so niche that there are no readily available solutions or data sets, so a majority of the work has to be done from scratch. Additionally, even if there is limited or even no need for novel AI research, a problem might still require large amounts of engineering efforts to solve.

The second talk of the day was given by Jonas Pfeiffer, a PhD student from the Technical University of Darmstadt. In this talk he introduced us to his research on Adapters for Transformer models. Adapters are a light weight and faster approach to fine tuning Transformer models to different tasks. The idea is rather simple, the Adapters are small layers that are added between the Transformer layers, which are trained during fine tuning, while keeping the transformer layers fixed. While pretty simple and straight forward, this approach appears to be rather effective. However, other than focusing on his research on Adapters, Jonas is also one of the main contributors to AdapterHub.ml, a framework for training and sharing Adapters. This brings our focus to an important part of what is necessary to get AI research out of the papers and into the real world – creating accessible and easy to use programming libraries. We as researchers often neglect this step or consider it to be beyond our responsibilities. That is not without sensible reasons. A programming library is not just the code it contains. It requires training materials for new users, tracking of bugs and feature requests, maintaining and following a development road map, managing integrations with other libraries that are dependencies or dependers, etc. All of these aspects require significant efforts by the maintainers of the library. Efforts that do not contribute to research output and consequently do not contribute to the criteria by which we are judged as successful scientists. As such, it is always a delight when you see a researcher willing to go this extra mile, to make his or her research more accessible. The talk by Jonas also had a tutorial section where he led us though the process of fine tuning an off the shelf pre-trained Transformer. This tutorial was delivered through Jupyter notebooks easily accessible from the projects website. Within minutes we had our own working examples, for us to dissect and experiment with. Given that Adapters and the AdapterHub.ml framework are very recent innovations, the amount and the quality of documentation and training resources within this project is highly impressive. Adapters and the AdapterHub.ml framework are excellent tools that, I believe, will be useful to me in the future. As such, I am very pleased to have attended this talk and to have discovered these tools though it.

The final day of BIAS was an excellent wrap up to the summer school. With its more applied focus, it showed us how the research we are conducting can be translated to the real world and how it can have an impact. We got a flavour of both, what it is like to develop and deploy an AI system, and what it is like to provide a programming library for our developed methods. These are all aspects of our research that we often neglect or overlook. Thus, this day served as great reminder that our research is not something confined within a lab but that it is work that lives and breathes within the context of the world that surrounds us.

BIAS Day 3 Review: ‘Responsible AI’

Posted on October 1, 2021April 5, 2022 by pr16096

This review of the 3rd day of the BIAS event, ‘Responsible AI’, is written by CDT Student Emily Vosper.

Monday was met with a swift 9:30am start, made easier to digest with a talk on AI and Ethics, why all the fuss? By Toby Walsh. This talk, and subsequent discussion, covered the thought-provoking topic of fairness within AI. The main lesson considered whether we actually need new ethical principles to govern AI, or whether we can take inspiration from well-established areas, such as medicine. Medicine works by four key principles: Beneficence, non-maleficence, autonomy and justice and AI brings some new challenges to this framework. The new challenges include autonomy, decision making and culpability. Some interesting discussions were had around reproducing historical biases when using autonomous systems, for example within the justice system such as predictive policing or parole decision making (COMPAS).

The second talk of the day was given by Nirav Ajmeri and Pradeep MuruKannaiah on ethics in sociotechnical systems. They broke down the definition of ethics as distinguishing between right and wrong which is a complex problem full of ethical dilemmas. Such dilemmas include examples such as Les Miserables where the actor steals a loaf of bread, stealing is obviously bad, but the bread is being stollen to feed a child and therefore the notion of right and wrong becomes nontrivial. Nirav and Pradeep treated ethics as a multiagent concern and values were brought in as the building blocks of ethics. Using this values-based approach the notion of right and wrong could be more easily broken down in a domain context i.e. by discovering what the main values and social norms are for a certain domain rules can be drawn up to better understand how to reach a goal within that domain. After the talk there were some thought provoking discussions surrounding how to facilitate reasoning at both the individual and the societal level, and how to satisfy values such as privacy.

In the afternoon session, Kacper Sokol ran a practical machine learning explainability session where he introduced the concept of Surrogate Explainers – explainers that are not model specific and can therefore be used in many applications. The key takeaways are that such diagnostic tools only become explainers when their properties and outputs are well understood and that explainers are not monolithic entities – they are complex with many parameters and need to be tailer made or configured for the application in hand.

The practical involved trying to break the explainer. The idea was to move the meaningful splits of the explainer so that they were impure, i.e. they contain many different classes from the black box model predictions. Moving the splits means the explainer doesn’t capture the black box model as well, as a mixture of points from several class predictions have been introduced to the explainer. Based on these insights it would be possible to manipulate the explainer with very impure hyper rectangles. We found this was even more likely with the logistical regression model as it has diagonal decision boundaries, while the explainer has horizontal and vertical meaningful splits.

Welcome to the first Interactive AI Blog Post

Posted on September 22, 2021April 5, 2022 by pr16096

Welcome to the Interactive AI CDT Blog. This Centre for Doctoral Training (CDT) has been funded by UKRI to train the next generation of innovators in human-in-the-loop AI systems, enabling them to responsibly solve societally important problems.

The overarching aim of this CDT in Interactive Artificial Intelligence is to establish an internationally leading Centre that will train the next generations of innovators in responsible, data and knowledge-driven human-in-the-loop AI systems. The CDT offers an innovative cohort-based training experience that will equip students with the skills to design and implement complex AI pipelines solving societally important problems in responsible ways.

The specific objectives are:

to recruit highly motivated postgraduate students from computer science, mathematics, and cognate disciplines, and create an intensive and complementary training experience which comprises a bespoke taught programme in the first year, delivered within a distinctive cohort training environment;
to set the highest standards of responsible innovation with particular emphasis on AI-specific issues such as transparency, explainability, accountability, fairness, trustworthiness and privacy;
to promote a human-in-the-loop (HITL) ethos that emphasises technology design for and with different groups of end users;
to be an exemplar for technology-supported cohort-based training, using the latest developments and insights in flipped classroom teaching and teamwork, open software development tools and platforms, and cross-cohort mentoring;
to train “AI ambassadors” who, through their deep understanding of the strengths and limitations of artificial intelligence, can contribute to the public debate on AI and its relationship to society;
to build long-term relationships between the CDT and its industrial partners, enhancing the cohort provision through industry placements, co-supervised projects and research collaboration;
to support the development of entrepreneurial skills and intellectual property;
to contribute to the “democratisation of AI” and reduce inequality within the sector by promoting take-up of relevant AI techniques in SMEs;
to consolidate and expand existing expertise at Bristol and become a leading international research training centre with research and training relationships with global partners.