̽��ֱ�� of Cambridge - Computer Laboratory

̽��ֱ��codemakers

ps748 — Fri, 24 Jan 2025 11:58:18 +0000

̽��ֱ��2025 Darwin Lecture series looks at codes, be they computational, mathematical, biological, linguistic or even musical.

Four Cambridge researchers awarded European Research Council Advanced Grants

jg533 — Fri, 31 Mar 2023 09:43:20 +0000

̽��ֱ��European Research Council (ERC) has announced the award of 218 Advanced Grants to outstanding research leaders across Europe, as part of the Horizon Europe programme.

̽��ֱ��grants, totalling €544 million, support cutting-edge research in a wide range of fields from medicine and physics to social sciences and humanities.

̽��ֱ��ERC is the premier European funding organisation for excellent frontier research. ̽��ֱ��ERC Advanced Grant funding is amongst the most prestigious and competitive EU funding schemes, providing researchers with the opportunity to pursue ambitious, curiosity-driven projects that could lead to major scientific breakthroughs. Grants are awarded to established, leading researchers with a proven track record of significant research achievements over the past decade.

̽��ֱ�� ̽��ֱ�� of Cambridge’s grant awardees are:

Anna Korhonen, Professor of Natural Language Processing in the Faculty of Modern and Medieval Languages and Linguistics, for her project Towards Globally Equitable Language Technologies.

Richard Nickl, Professor of Mathematical Statistics in the Department of Pure Mathematics and Mathematical Statistics, for his project Statistical aspects of non-linear inverse problems.

Peter Sewell, Professor of Computer Science at the Computer Laboratory, for his project Secure Foundations: Verified Systems Software Above Full-Scale Integrated Semantics.

Sujit Sivasundaram, Professor of World History at the Faculty of History, for his project Colombo: Layered Histories in the Global South City.

'Many many congratulations to Anna, Richard, Peter and Sujit on their success. It is fantastic to see the highly innovative work of our researchers being recognised in international competition in this way. We are once again reminded of the commitment of the ERC to cutting edge research across all disciplines and we continue to urge government to swiftly secure association to Horizon Europe,' said Professor Anne Ferguson-Smith, Pro-Vice Chancellor for Research at the ̽��ֱ�� of Cambridge.

"This funding puts our 218 research leaders, together with their teams of postdoctoral fellows, PhD students and research staff, in pole position to push back the boundaries of our knowledge, break new ground and build foundations for future growth and prosperity in Europe,” said Mariya Gabriel, European Commissioner for Innovation, Research, Culture, Education and Youth.

"These new ERC Advanced Grantees are a testament to the outstanding quality of research carried out across Europe. I am especially pleased to see such a high number of female researchers in this competition and that they are increasingly successful in securing funding. We look forward to seeing the results of the new projects in the years to come, with many likely to lead to breakthroughs and new advances,” said Maria Leptin, ERC President.

̽��ֱ��laureates of this grant competition will carry out their projects at universities and research centres in 20 countries in Europe, with the highest number of projects in Germany (37), the UK (35), France (32) and Spain (16). ̽��ֱ��winners come from all over the world, with 27 nationalities represented, notably Germans (36 researchers), French (32), Italians (21), British (19).

This call for proposals attracted nearly 1,650 applications, which were reviewed by panels of renowned researchers. ̽��ֱ��overall success rate was 13.2%. Female researchers account for 23% of all applications, their highest participation rate in Advanced Grant calls up to now.

In addition to strengthening Europe's knowledge base, the grants are expected to create more than 2,000 jobs for postdoctoral fellows, PhD students, and other staff at the host institutions. Past recipients have included Nobel laureates and other leading scientists who have gone on to make major contributions to their respective fields..

Adapted from a press release by the ERC.

̽��ֱ��funding will enable these researchers to explore their most innovative and ambitious ideas.

It is fantastic to see the highly innovative work of our researchers being recognised in international competition in this way.

Anne Ferguson-Smith

yangwenshuang on Getty

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Licence type:

Attribution-Noncommerical

Cambridge researchers awarded European Research Council funding

cg605 — Wed, 09 Dec 2020 17:14:16 +0000

Three hundred and twenty-seven mid-career researchers were today awarded Consolidator Grants by the ERC, totalling €655 million. ̽��ֱ��UK has 50 grantees in this year’s funding round. ̽��ֱ��funding is part of the EU’s current research and innovation programme, Horizon 2020.

̽��ֱ��ERC Consolidator Grants are awarded to outstanding researchers of any nationality and age, with at least seven and up to 12 years of experience after PhD, and a scientific track record showing great promise.

̽��ֱ��research projects proposed by the new grantees cover a wide range of topics in physical sciences and engineering, life sciences, as well as social sciences and humanities.

From the ̽��ֱ�� of Cambridge, the following researchers were named as grantees: Professor Vasco Carvalho, Professor Tuomas Knowles, Dr Neel Krishnaswami, Professor Silvia Vignolini and Dr Kaisey Mandel.

Vasco Carvalho, Professor of Macroeconomics and Director of Cambridge-INET, Faculty of Economics

Project title: Micro Structure and Macro Outcomes.

What is your research about?

“Research under the project MICRO2MACRO takes as a starting point the organisation of production around supply chain networks and, within these networks, the increasing dominance of very large and central firms. This renders a small number of firms and technologies systemic in that they can influence aggregate economic performance.

“Within this broad agenda, MICRO2MACRO explores issues surrounding, first, market power and pro-competitive policies and, second, innovation, productivity and the diffusion of new technologies. ̽��ֱ��project also partners with one global financial institution to unlock relevant real-time, highly granular data that is necessary to study some of these questions.”

How do you feel about being named a grantee?

“I'm ecstatic. First, because it recognises the combined effort of colleagues around the world in developing a new micro-to-macro research agenda and understanding macroeconomic developments via a new lens. Second, because it provides the opportunity to inject otherwise scarce resources into early career researchers and PhD students, thereby adding to the human capital in this research area. Third, because it further highlights a decade of collective efforts at the Faculty of Economics here at Cambridge and helps ensure its continued growth as a hub for the development of new approaches to decades old questions in economics.”

Professor Tuomas Knowles, Yusuf Hamied Department of Chemistry

Project title: Digital Protein Biophysics of Aggregation.

What is your research about?

“Our work is focused on understanding the basic molecular principles that govern the activity of proteins in health and disease. In particular we are interested in how proteins come together to form machinery and compartments that underpin the functions of a living cell, and what happens when these processes fail.

“ ̽��ֱ��ERC project is focused on understanding how proteins condense together to form functional liquid organelles, and how such compartments can gel and form irreversible protein aggregates associated with disease. Such problems have been challenging to study previously due to the very high heterogeneity of the structures that are formed which make observation by conventional bulk techniques challenging. We will be developing new single molecule approaches to study this phenomenon aggregate by aggregate and cell by cell, and in this way shed light on the connection between the physical and structural properties of protein assemblies and their biological activity.”

How do you feel about being named a grantee?

“I am truly delighted by this support of my research and that of my group, which will allow us to develop fundamentally new approaches for probing a process at the core of biological function and malfunction.”

Dr Neel Krishnaswami, Computer Laboratory

Project title: Foundations of Type Inference for Modern Programming Languages.

What is your research about?

“Many modern programming languages, whether industrial or academic, are typed. Each phrase in a program is classified by its type (for example, as strings or integers), and at compile-time programs are checked for consistent usage of types, in a process called type-checking. Thus, the expression ‘3 + 4’ will be accepted, since the + operator takes two numbers as arguments, but the expression ‘3 + ‘hello’’ will be rejected, as it makes no sense to add a number and a string. Though this is a simple idea, sophisticated type systems can track properties like algorithmic complexity and program correctness.

“In general, programmers must write annotations to tell computers which types to check. In theory, it is easy to demand enough annotations to trivialize type-checking, but this can easily make the annotation larger than the program itself! So, to transfer results from formal calculi to real programming languages, we need type inference algorithms, which reconstruct missing types from partially-annotated programs.

“In TypeFoundry, we will use recent developments in proof theory and formal semantics to identify the theoretical structure underpinning type inference.”

How do you feel about being named a grantee?

“Naturally, I am happy to find out that my research is valued in such concrete, material terms, and I'm delighted to have the opportunity to have the chance to support PhD students and postdocs working in this area. I also feel this shows off the best international character of science. I am an Indian-American researcher working in the UK, judged and funded by my European peers. Consequently, I keenly feel both the opportunity and responsibility to carry on the cosmopolitan tradition of scientific work.”

Professor Silvia Vignolini, Yusuf Hamied Department of Chemistry

Project title: Sym-Bionic Matter: developing symbiotic relationships for light-matter interaction.

What is your research about?

“With this ERC grant I aim to develop new platforms and tools to study how different organisms build symbiotic interactions for light management and ‘evolve’ new symbiotic relationships, in which one of the organisms is replaced by an artificial material to generate a novel class of hybrid which I link to call ‘sym-BIonic matTEr’ – BiTe!”

How do you feel about being named a grantee?

“I was very excited to learn that I had been awarded an ERC grant and I look forward to starting the project. It’s an amazing opportunity for my team and me!

“When you receive the evaluation response, you get an email notification that invites you to log into the EU portal to see the outcome of the evaluation. In those few minutes that it takes to open the right form on the platform, I experienced pure panic! When I finally open the letter, I had to read it three times to convince myself that I had been awarded the grant! It was a great day!”

Dr Kaisey Mandel, Institute of Astronomy, Statistical Laboratory of the Department of Pure Mathematics and Mathematical Statistics, Kavli Institute for Cosmology

Project title: Next-Generation Data-Driven Probabilistic Modelling of Type Ia Supernova SEDs in the Optical to Near-Infrared for Robust Cosmological Inference.

What is your research about?

“My research focuses on utilising exploding stars called Type Ia supernovae to measure cosmological distances for tracing the history of cosmic expansion.

“I lead a project to develop state-of-the-art statistical models and advanced, data-driven techniques for analysing observations of these supernovae in optical and near-infrared light to determine more precise and accurate distances.

“Applying these novel methods to supernova data from the Hubble Space Telescope, new ground-based surveys, and, in the near future, the Vera Rubin Observatory's Legacy Survey of Space and Time, we will pursue new and improved constraints on the accelerating expansion of the Universe and the nature of dark energy.”

Five researchers at the ̽��ֱ�� of Cambridge have won consolidator grants from the European Research Council (ERC), Europe’s premiere funding organisation for frontier research.

iStock.com/ BarrySheene

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Artificial intelligence is growing up fast: what’s next for thinking machines?

cjb250 — Tue, 06 Feb 2018 09:11:12 +0000

We are well on the way to a world in which many aspects of our daily lives will depend on AI systems.

Within a decade, machines might diagnose patients with the learned expertise of not just one doctor but thousands. They might make judiciary recommendations based on vast datasets of legal decisions and complex regulations. And they will almost certainly know exactly what’s around the corner in autonomous vehicles.

“Machine capabilities are growing,” says Dr Stephen Cave, Executive Director of the Leverhulme Centre for the Future of Intelligence (CFI). “Machines will perform the tasks that we don’t want to: the mundane jobs, the dangerous jobs. And they’ll do the tasks we aren’t capable of – those involving too much data for a human to process, or where the machine is simply faster, better, cheaper.”

Dr Mateja Jamnik, AI expert at the Department of Computer Science and Technology, agrees: “Everything is going in the direction of augmenting human performance – helping humans, cooperating with humans, enabling humans to concentrate on the areas where humans are intrinsically better such as strategy, creativity and empathy.”

Part of the attraction of AI requires that future technologies perform tasks autonomously, without humans needing to monitor activities every step of the way. In other words, machines of the future will need to think for themselves. But, although computers today outperform humans on many tasks, including learning from data and making decisions, they can still trip up on things that are really quite trivial for us.

Take, for instance, working out the formula for the area of a parallelogram. Humans might use a diagram to visualise how cutting off the corners and reassembling it as a rectangle simplifies the problem. Machines, however, may “use calculus or integrate a function. This works, but it’s like using a sledgehammer to crack a nut,” says Jamnik, who was recently appointed Specialist Adviser to the House of Lords Select Committee on AI.

“When I was a child, I was fascinated by the beauty and elegance of mathematical solutions. I wondered how people came up with such intuitive answers. Today, I work with neuroscientists and experimental psychologists to investigate this human ability to reason and think flexibly, and to make computers do the same.”

Jamnik believes that AI systems that can choose so-called heuristic approaches – employing practical, often visual, approaches to problem solving – in a similar way to humans will be an essential component of human-like computers. They will be needed, for instance, so that machines can explain their workings to humans – an important part of the transparency of decision-making that we will require of AI.

With funding from the Engineering and Physical Sciences Research Council and the Leverhulme Trust, she is building systems that have begun to reason like humans through diagrams. Her aim now is to enable them to move flexibly between different “modalities of reasoning”, just as humans have the agility to switch between methods when problem solving.

Being able to model one aspect of human intelligence in computers raises the question of what other aspects would be useful. And in fact how ‘human-like’ would we want AI systems to be? This is what interests Professor José Hernandez-Orallo, from the Universitat Politècnica de València in Spain and Visiting Fellow at the CFI.

“We typically put humans as the ultimate goal of AI because we have an anthropocentric view of intelligence that places humans at the pinnacle of a monolith,” says Hernandez-Orallo. “But human intelligence is just one of many kinds. Certain human skills, such as reasoning, will be important in future systems. But perhaps we want to build systems that ‘fill the gaps that humans cannot reach’, whether it’s AI that thinks in non-human ways or AI that doesn’t think at all.

“I believe that future machines can be more powerful than humans not just because they are faster but because they can have cognitive functionalities that are inherently not human.” This raises a difficulty, says Hernandez-Orallo: “How do we measure the intelligence of the systems that we build? Any definition of intelligence needs to be linked to a way of measuring it, otherwise it’s like trying to define electricity without a way of showing it.”

̽��ֱ��intelligence tests we use today – such as psychometric tests or animal cognition tests – are not suitable for measuring intelligence of a new kind, he explains. Perhaps the most famous test for AI is that devised by 1950s Cambridge computer scientist Alan Turing. To pass the Turing Test, a computer must fool a human into believing it is human. “Turing never meant it as a test of the sort of AI that is becoming possible – apart from anything else, it’s all or nothing and cannot be used to rank AI,” says Hernandez-Orallo.

In his recently published book ̽��ֱ��Measure of all Minds, he argues for the development of “universal tests of intelligence” – those that measure the same skill or capability independently of the subject, whether it’s a robot, a human or an octopus.

His work at the CFI as part of the ‘Kinds of Intelligence’ project, led by Dr Marta Halina, is asking not only what these tests might look like but also how their measurement can be built into the development of AI. Hernandez-Orallo sees a very practical application of such tests: the future job market. “I can imagine a time when universal tests would provide a measure of what’s needed to accomplish a job, whether it’s by a human or a machine.”

Cave is also interested in the impact of AI on future jobs, discussing this in a report on the ethics and governance of AI recently submitted to the House of Lords Select Committee on AI on behalf of researchers at Cambridge, Oxford, Imperial College and the ̽��ֱ�� of California at Berkeley. “AI systems currently remain narrow in their range of abilities by comparison with a human. But the breadth of their capacities is increasing rapidly in ways that will pose new ethical and governance challenges – as well as create new opportunities,” says Cave. “Many of these risks and benefits will be related to the impact these new capacities will have on the economy, and the labour market in particular.”

Hernandez-Orallo adds: “Much has been written about the jobs that will be at risk in the future. This happens every time there is a major shift in the economy. But just as some machines will do tasks that humans currently carry out, other machines will help humans do what they currently cannot – providing enhanced cognitive assistance or replacing lost functions such as memory, hearing or sight.”

Jamnik also sees opportunities in the age of intelligent machines: “As with any revolution, there is change. Yes some jobs will become obsolete. But history tells us that there will be jobs appearing. These will capitalise on inherently human qualities. Others will be jobs that we can’t even conceive of – memory augmentation practitioners, data creators, data bias correctors, and so on. That’s one reason I think this is perhaps the most exciting time in the history of humanity.”

Inset image: read more about our AI research in the ̽��ֱ��'s research magazine; download a pdf; view on Issuu.

Our lives are already enhanced by AI – or at least an AI in its infancy – with technologies using algorithms that help them to learn from our behaviour. As AI grows up and starts to think, not just to learn, we ask how human-like do we want their intelligence to be and what impact will machines have on our jobs?

Perhaps we want to build systems that ‘fill the gaps that humans cannot reach’, whether it’s AI that thinks in non-human ways or AI that doesn’t think at all

José Hernandez-Orallo

̽��ֱ��District

Artificial intelligence

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Preparing for the future: artificial intelligence and us

lw355 — Fri, 02 Feb 2018 09:00:13 +0000

AI systems are now used in everything from the trading of stocks to the setting of house prices; from detecting fraud to translating between languages; from creating our weekly shopping lists to predicting which movies we might enjoy.

This is just the beginning. Soon, AI will be used to advance our understanding of human health through analysis of large datasets, help us discover new drugs and personalise treatments. Self-driving vehicles will transform transportation and allow new paradigms in urban planning. Machines will run our homes more efficiently, make businesses more productive and help predict risks to society.

While some AI systems will outperform human intelligence to augment human decision making, others will carry out repetitive, manual and dangerous tasks to augment human labour. Many of the greatest challenges we face, from understanding and mitigating climate change to quickly identifying and containing disease outbreaks, will be aided by the tools of AI.

What we’ve seen of AI so far is only the leading edge of the revolution to come./system/files/issue_35_research_horizons_new.pdf

Yet the idea of creating machines that think and learn like humans has been around since the 1950s. Why is AI such a hot topic now? And what does Cambridge have to offer?

Three major advances are enabling huge progress in AI research: the availability of masses of data generated by all of us all the time; the power and processing speeds of today’s supercomputers; and the advances that have been made in mathematics and computer science to create sophisticated algorithms that help machines learn.

Unlike in the past when computers were programmed for specific tasks and domains, modern machine learning systems know nothing about the topic in question, they only know about learning: they use huge amounts of data about the world in order to learn from it and to make predictions about future behaviour. They can make sense of complex datasets that are difficult to use and have missing data.

That these advances will provide tremendous benefits is becoming clear. One strand of the UK government’s Industrial Strategy is to put the UK at the forefront of the AI and data revolution. In 2017, a report by PricewaterhouseCoopers described AI as “the biggest commercial opportunity in today’s fast-changing economy”, predicting a 10% increase in the UK’s GDP by 2030 as a result of the applications of AI.

Cambridge ̽��ֱ�� is helping to drive this revolution – and to prepare for it.

Our computer scientists are designing systems that are cybersecure, model human reasoning, interact in affective ways with us, uniquely identify us by our face and give insights into our biological makeup.

Our engineers are building machines that are making decisions under uncertain conditions based on probabilistic estimation of perception and for the best course of action. And they’re building robots that can carry out a series of actions in the physical world – whether it’s for self-driving cars or for picking lettuces.

Our researchers in a multitude of different disciplines are creating innovative applications of AI in areas as diverse as discovering new drugs, overcoming phobias, helping to make police custody decisions and forecasting extreme weather events.

Our philosophers and humanists are asking fundamental questions about the ethics, trust and humanity of AI system design, and the effect that the language of discussion has on the public perception of AI. Together with the work of our engineers and computer scientists, these efforts aim to create AI systems that are trustworthy and transparent in their workings – that do what we want them to do.

All of this is happening in a university research environment and wider ecosystem of start-ups and large companies that facilitates innovative breakthroughs in AI. ̽��ֱ��aim of this truly interdisciplinary approach to research at Cambridge is to invent transformative AI technology that will benefit society at large.

However, transformative advances may carry negative consequences if we do not plan for them carefully on a societal level.

̽��ֱ��fundamental advances that underpin self-driving cars may allow dangerous new weapons on the battlefield. Technologies that automate work may result in livelihoods being eliminated. Algorithms trained on historical data may perpetuate, or even exacerbate, biases and inequalities such as sex- or race-based discrimination. Without careful planning, systems for which large amounts of personal data is essential, such as in healthcare, may undermine privacy.

Engaging with these challenges requires drawing on expertise not just from the sciences, but also from the arts, humanities and social sciences, and requires delving deeply into questions of policy and governance for AI. Cambridge has taken a leading position here too, with the recent establishment of the Leverhulme Centre for the Future of Intelligence and the Centre for the Study of Existential Risk, as well as being one of the founding partners of ̽��ֱ��Alan Turing Institute based in London.

In the longer term, it is not outside the bounds of possibility that we might develop systems able to match or surpass human intelligence in the broader sense. There are some who think that this would change humanity’s place in the world irrevocably, while others look forward to the world a superintelligence might be able to co-create with us.

As the ̽��ֱ�� where the great mathematician Alan Turing was an undergraduate and fellow, it seems entirely fitting that Cambridge’s scholars are exploring questions of such significance to prepare us for the revolution to come. Turing once said: “we can only see a short distance ahead, but we can see plenty there that needs to be done.”

Inset image: read more about our AI research in the ̽��ֱ��'s research magazine; download a pdf; view on Issuu.

Dr Mateja Jamnik (Department of Computer Science and Technology), Dr Seán Ó hÉigeartaigh (Centre for the Study of Existential Risk and the Leverhulme Centre for the Future of Intelligence, CFI), Dr Beth Singler (Faraday Institute for Science and Religion and CFI) and Dr Adrian Weller (Department of Engineering, CFI and ̽��ֱ��Alan Turing Institute).

Today we begin a month-long focus on research related to artificial intelligence. Here, four researchers reflect on the power of a technology to impact nearly every aspect of modern life – and why we need to be ready.

What we’ve seen of AI so far is only the leading edge of the revolution to come.

Mateja Jamnik, Seán Ó hÉigeartaigh, Beth Singler and Adrian Weller

Jonathan Settle / ̽��ֱ�� of Cambridge

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Celebrity Twitter accounts display ‘bot-like’ behaviour

sc604 — Tue, 01 Aug 2017 23:01:58 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, used data from Twitter to determine whether bots can be accurately detected, how bots behave, and how they impact Twitter activity.

They divided accounts into categories based on total number of followers, and found that accounts with more than 10 million followers tend to retweet at similar rates to bots. In accounts with fewer followers however, bots tend to retweet far more than humans. These celebrity-level accounts also tweet at roughly the same pace as bots with similar follower numbers, whereas in smaller accounts, bots tweet far more than humans. Their results will be presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) in Sydney, Australia.

Bots, like people, can be malicious or benign. ̽��ֱ��term ‘bot’ is often associated with spam, offensive content or political infiltration, but many of the most reputable organisations in the world also rely on bots for their social media channels. For example, major news organisations, such as CNN or the BBC, who produce hundreds of pieces of content daily, rely on automation to share the news in the most efficient way. These accounts, while classified as bots, are seen by users as trustworthy sources of information.

“A Twitter user can be a human and still be a spammer, and an account can be operated by a bot and still be benign,” said Zafar Gilani, a PhD student at Cambridge’s Computer Laboratory, who led the research. “We’re interested in seeing how effectively we can detect automated accounts and what effects they have.”

Bots have been on Twitter for the majority of the social network’s existence – it’s been estimated that anywhere between 40 and 60% of all Twitter accounts are bots. Some bots have tens of millions of followers, although the vast majority have less than a thousand – human accounts have a similar distribution.

In order to reliably detect bots, the researchers first used the online tool BotOrNot (since renamed BotOMeter), which is one of the only available online bot detection tools. However, their initial results showed high levels of inaccuracy. BotOrNot showed low precision in detecting bots that had bot-like characteristics in their account name, profile info, content tweeting frequency and especially redirection to external sources. Gilani and his colleagues then decided to take a manual approach to bot detection.

Four undergraduate students were recruited to manually inspect accounts and determine whether they were bots. This was done using a tool that automatically presented Twitter profiles, and allowed the students to classify the profile and make notes. Each account was collectively reviewed before a final decision was reached.

In order to determine whether an account was a bot (or not), the students looked at different characteristics of each account. These included the account creation date, average tweet frequency, content posted, account description, whether the user replies to tweets, likes or favourites received and the follower to friend ratio. A total of 3,535 accounts were analysed: 1,525 were classified as bots and 2010 as humans.

̽��ֱ��students showed very high levels of agreement on whether individual accounts were bots. However, they showed significantly lower levels of agreement with the BotOrNot tool.

̽��ֱ��bot detection algorithm they subsequently developed achieved roughly 86% accuracy in detecting bots on Twitter. ̽��ֱ��algorithm uses a type of classifier known as Random Forests, which uses 21 different features to detect bots, and the classifier itself is trained by the original dataset annotated by the human annotators.

̽��ֱ��researchers found that bot accounts differ from humans in several key ways. Overall, bot accounts generate more tweets than human accounts. They also retweet far more often, and redirect users to external websites far more frequently than human users. ̽��ֱ��only exception to this was in accounts with more than 10 million followers, where bots and humans showed far more similarity in terms of the volume of tweets and retweets.

“We think this is probably because bots aren’t that good at creating original Twitter content, so they rely a lot more on retweets and redirecting followers to external websites,” said Gilani. “While bots are getting more sophisticated all the time, they’re still pretty bad at one-on-one Twitter conversations, for instance – most of the time, a conversation with a bot will be mostly gibberish.”

Despite the sheer volume of Tweets produced by bots, humans still have better quality and more engaging tweets – tweets by human accounts receive on average 19 times more likes and 10 times more retweets than tweets by bot accounts. Bots also spend less time liking other users’ tweets.

“Many people tend to think that bots are nefarious or evil, but that’s not true,” said Gilani. “They can be anything, just like a person. Some of them aren’t exactly legal or moral, but many of them are completely harmless. What I’m doing next is modelling the social cost of these bots – how are they changing the nature and quality of conversations online? What is clear though, is that bots are here to stay.”

References:
Zafar Gilani, Reza Farahbakhsh, Gareth Tyson, Liang Wang, Jon Crowcroft. Of Bots and Humans (on Twitter). Paper presented at 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'17). Sydney, New South Wales, Australia.

Zafar Gilani, Ekaterina Kochmar, Jon Crowcroft. Classification of Twitter Accounts into Automated Agents and Human Users. Paper presented at 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'17). Sydney, New South Wales, Australia.

‘Celebrity’ Twitter accounts – those with more than 10 million followers – display more bot-like behaviour than users with fewer followers, according to new research.

A Twitter user can be a human and still be a spammer, and an account can be operated by a bot and still be benign.

Zafar Gilani

Esther Vargas

Twitter

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Licence type:

Attribution-ShareAlike

Cambridge to host transatlantic cyber security competition

Anonymous — Thu, 20 Jul 2017 08:55:49 +0000

̽��ֱ��“Cambridge2Cambridge” cyber security competition, backed by government and industry, is the brainchild of the ̽��ֱ�� of Cambridge and the Massachusetts Institute of Technology (MIT) in the US, and will see talented students pitted against each other in a three-day showdown.

In total, 110 students from 25 universities from the UK and USA will form mixed transatlantic teams and battle against a fictional rogue state in the life-like cyber security competition backed by the National Cyber Security Centre (NCSC) and Cabinet Office.

̽��ֱ��annual event is now in its second year with prize money up for grabs for the winners. It will be held from 24-26 July at Trinity College, Cambridge.

With major cyber-attacks on the increase, according to the NCSC, the need for cyber security experts is more important than ever before.

Professor Frank Stajano, Head of the Academic Centre of Excellence in Cyber Security Research at Cambridge’s Computer Laboratory and the co-founder of Cambridge2Cambridge, said that the competition has been designed to promote greater cyber security collaboration between the UK and USA, and to give students the platform to explore creative ways to combat global cyber-attacks.

“ ̽��ֱ��aim of the competition is also to bring together different individuals in a fun and inclusive environment, where they can apply their cyber security abilities in a collaborative and competitive setting, allowing students to implement the skills they have been taught, while learning new ones in the process,” he said.

It also gives budding cyber enthusiasts the opportunity to meet like-minded individuals, and learn more about careers in the sector by introducing them to key players in the industry and government.

https://cambridge2cambridge.csail.mit.edu/

A major cyber security challenge, aimed at educating and inspiring the next generation of cyber defenders from across the UK and US, will be held at the ̽��ֱ�� of Cambridge next week.

̽��ֱ��aim of the competition is to bring together different individuals in a fun and inclusive environment, where they can apply their cyber security abilities in a collaborative and competitive setting.

Frank Stajano

Inter-ACE Cyber Challenge 2017

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Researchers design AI system to assess pain levels in sheep

sc604 — Wed, 31 May 2017 23:02:29 +0000

̽��ֱ��researchers have developed an AI system which uses five different facial expressions to recognise whether a sheep is in pain, and estimate the severity of that pain. ̽��ֱ��results could be used to improve sheep welfare, and could be applied to other types of animals, such as rodents used in animal research, rabbits or horses.

Building on earlier work which teaches computers to recognise emotions and expressions in human faces, the system is able to detect the distinct parts of a sheep’s face and compare it with a standardised measurement tool developed by veterinarians for diagnosing pain. Their results will be presented today (1 June) at the 12th IEEE International Conference on Automatic Face and Gesture Recognition in Washington, DC.

Severe pain in sheep is associated with conditions such as foot rot, an extremely painful and contagious condition which causes the foot to rot away; or mastitis, an inflammation of the udder in ewes caused by injury or bacterial infection. Both of these conditions are common in large flocks, and early detection will lead to faster treatment and pain relief. Reliable and efficient pain assessment would also help with early diagnosis.

As is common with most animals, facial expressions in sheep are used to assess pain. In 2016, Dr Krista McLennan, a former postdoctoral researcher at the ̽��ֱ�� of Cambridge who is now a lecturer in animal behaviour at the ̽��ֱ�� of Chester, developed the Sheep Pain Facial Expression Scale (SPFES). ̽��ֱ��SPFES is a tool to measure pain levels based on facial expressions of sheep, and has been shown to recognise pain with high accuracy. However, training people to use the tool can be time-consuming and individual bias can lead to inconsistent scores.

In order to make the process of pain detection more accurate, the Cambridge researchers behind the current study used the SPFES as the basis of an AI system which uses machine learning techniques to estimate pain levels in sheep. Professor Peter Robinson, who led the research, normally focuses on teaching computers to recognise emotions in human faces, but a meeting with Dr McLennan got him interested in exploring whether a similar system could be developed for animals.

“There’s been much more study over the years with people,” said Robinson, of Cambridge’s Computer Laboratory. “But a lot of the earlier work on the faces of animals was actually done by Darwin, who argued that all humans and many animals show emotion through remarkably similar behaviours, so we thought there would likely be crossover between animals and our work in human faces.”

According to the SPFES, when a sheep is in pain, there are five main things which happen to their faces: their eyes narrow, their cheeks tighten, their ears fold forwards, their lips pull down and back, and their nostrils change from a U shape to a V shape. ̽��ֱ��SPFES then ranks these characteristics on a scale of one to 10 to measure the severity of the pain.

“ ̽��ֱ��interesting part is that you can see a clear analogy between these actions in the sheep’s faces and similar facial actions in humans when they are in pain – there is a similarity in terms of the muscles in their faces and in our faces,” said co-author Dr Marwa Mahmoud, a postdoctoral researcher in Robinson’s group. “However, it is difficult to ‘normalise’ a sheep’s face in a machine learning model. A sheep’s face is totally different in profile than looking straight on, and you can’t really tell a sheep how to pose.”

To train the model, the Cambridge researchers used a small dataset consisting of approximately 500 photographs of sheep, which had been gathered by veterinarians in the course of providing treatment. Yiting Lu, a Cambridge undergraduate in Engineering and co-author on the paper, trained the model by labelling the different parts of the sheep’s faces on each photograph and ranking their pain levels according to SPFES.

Early tests of the model showed that it was able to estimate pain levels with about 80% degree of accuracy, which means that the system is learning. While the results with still photographs have been successful, in order to make the system more robust, they require much larger datasets.

̽��ֱ��next plans for the system are to train it to detect and recognise sheep faces from moving images, and to train it to work when the sheep is in profile or not looking directly at the camera. Robinson says that if they are able to train the system well enough, a camera could be positioned at a water trough or other place where sheep congregate, and the system would be able to recognise any sheep which were in pain. ̽��ֱ��farmer would then be able to retrieve the affected sheep from the field and get it the necessary medical attention.

“I do a lot of walking in the countryside, and after working on this project, I now often find myself stopping to talk to the sheep and make sure they’re happy,” said Robinson.

Reference
Yuting Lu, Marwa Mahmoud and Peter Robinson. ‘Estimating sheep pain level using facial action unit detection.’ Paper presented to the IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC. 30 May – 3 June, 2017. http://www.fg2017.org/.

Inset image: Left: Localised facial landmarks; Right: Normalised sheep face marked with feature bounding boxes.

An artificial intelligence system designed by researchers at the ̽��ֱ�� of Cambridge is able to detect pain levels in sheep, which could aid in early diagnosis and treatment of common, but painful, conditions in animals.

You can see a clear analogy between these actions in the sheep’s faces and similar facial actions in humans when they are in pain.

Marwa Mahmoud

Sheep

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes