̽��ֱ�� of Cambridge - Anna Korhonen

Opinion: Humans should be at the heart of AI

lw355 — Thu, 03 Apr 2025 16:48:27 +0000

With the right development and application, AI could become a transformative force for good. What's missing in current technologies is human insight, says Anna Korhonen.

Cambridge launches Institute for Technology and Humanity

fpjl2 — Tue, 21 Nov 2023 09:13:02 +0000

A major interdisciplinary initiative has been launched that aims to meet the challenges and opportunities of new technologies as they emerge, today and far into the future.

Cambridge and Google partner to facilitate AI research

hcf38 — Tue, 17 Oct 2023 07:54:22 +0000

̽��ֱ��new multi-year research agreement creates the potential for researchers and scientists from Google and the ̽��ֱ�� to more closely collaborate on foundational AI research projects in areas of shared interest across a range of disciplines, including climate and sustainability, and AI ethics and safety.

Google has also become the first funding partner for the university’s Centre for Human-Inspired Artificial Intelligence (CHIA), led by Professor Anna Korhonen, Professor Per Ola Kristensson and Dr. John Suckling, bringing together researchers and experts from computer science, engineering and multiple disciplines to develop AI that is grounded in human values and benefits humanity. Google’s unrestricted grant is helping enable the Centre’s AI research in areas like responsible AI, human-centred robotics, human-machine interaction, healthcare, economic sustainability and climate change. ̽��ֱ��donation is also funding students from underrepresented groups to carry out PhDs within the CHIA to help broaden diversity in the AI research community.

̽��ֱ��expanded partnership builds on years of collaboration between Google Research, Google DeepMind and the ̽��ֱ�� of Cambridge. Google provides funding for academic research, facilitates collaboration between faculty and Google researchers, and supports exceptional computer science students through its PhD Fellowship Programme. Google DeepMind funds scholarships for students from underrepresented backgrounds studying AI-related fields, as well as a postdoctoral Fellowship, to help build a stronger and more inclusive AI community. Google DeepMind also endowed the first DeepMind Professor of Machine Learning at the ̽��ֱ�� of Cambridge’s Department of Computer Science and Technology to help drive its machine learning and artificial intelligence research.

Matt Brittin, President of Google EMEA and ̽��ֱ�� of Cambridge alumnus, commented: “AI has huge potential to benefit people across the world - whether it’s through making daily life that bit easier, or by tackling some of society’s biggest challenges. It’s vital that we work together to seize this opportunity. By collaborating with one of our world-leading British academic institutions, we can enable AI research that is bold, responsible and designed to meet the needs of people across the country. This partnership also reaffirms Google’s commitment to the UK as a global AI and technology leader.”

Jessica Montgomery, Director of ai@cam, the ̽��ֱ�� of Cambridge’s flagship mission on artificial intelligence, commented: “ ̽��ֱ�� ̽��ֱ�� of Cambridge can be an engine for AI innovation and a steward of advancements in this exciting field. Translating advances in AI to benefits for science, citizens, and society requires interdisciplinary research that is deeply connected to real-word needs. ̽��ֱ��research collaboration agreement announced today will support research activities across the ̽��ֱ��. We want to leverage the world-leading expertise found across the ̽��ֱ�� to enable exciting new advances in responsible AI.”

Michelle Donelan, Secretary of State for Science, Innovation and Technology, added: "Artificial intelligence can offer us enormous opportunities - growing the economy, creating new jobs and making lives longer, healthier and happier for British people. To seize those opportunities, we must bring together insights from business and academia to encourage the safe and responsible development of AI. That is why we are welcoming the partnership which Google and the ̽��ֱ�� of Cambridge have announced today.

As we prepare for next month's AI Safety Summit, this partnership shows that the UK - home to world-leading research facilities as well as some of the biggest tech companies in the world - is perfectly placed to support the innovation that underpins this critical technology."

Professor Anna Korhonen, Director of CHIA, said: “Here at the Centre for Human-Inspired Artificial Intelligence our researchers are dedicated to making sure that people are put at the very heart of new developments in AI. As our first funding partner, Google has been with us from the start of our journey, helping enable the breakthrough interdisciplinary research that we do. Partnerships like this – between academia and industry – will continue to be vital for the successful development of human-inspired AI.”

Zoubin Ghahramani, VP, Research, Google DeepMind is a Professor of Information Engineering at the ̽��ֱ�� of Cambridge and has spearheaded this expanded partnership. He commented: “Google and the ̽��ֱ�� of Cambridge share a deep commitment to developing AI responsibly, which means grounding innovation in scientific research, human values and our AI principles. We’re excited by CHIA’s potential to set new standards in responsible and human-centric AI development, and unlock AI discoveries that could benefit everyone.”

A recent report, commissioned by Google and compiled by Public First, quantified the opportunity AI presents to enhance the lives and businesses of everyone across the UK. It found AI-powered innovation could create over £400 billion in economic value for the UK economy by 2030. To ensure everyone can tap into that potential, regardless of whether they’re in higher education, Google has launched free training to offer people and businesses practical skills and knowledge to capture the benefits of AI.

̽��ֱ�� ̽��ֱ�� of Cambridge and Google are building on their long-standing partnership with a multi-year research collaboration agreement and a Google grant for the ̽��ֱ��’s new Centre for Human-Inspired AI to support progress in responsible AI that is inspired by and benefits people.

Cambridge FilmWorks

Research underway in the Centre for Human-Inspired Artificial Intelligence

How AI can help people with motor disabilities — like my cousin

“My cousin was the victim of a brutal attack, and left with life-changing injuries – but with AI technology, we aim to empower people like her.”

Aleesha Hamid, a PhD student at the Cambridge Centre for Human-Inspired AI, blogs on the Google website about why her research aims to make a real difference to people like her cousin, who was left with a traumatic brain injury and uses technology to communicate.

̽��ֱ��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.

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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

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Cambridge research centre puts people at the heart of AI

cjb250 — Tue, 12 Jul 2022 07:00:51 +0000

̽��ֱ��Centre for Human-Inspired Artificial Intelligence (CHIA) brings together researchers from engineering and mathematics, philosophy and social sciences; a broad range of disciplines to investigate how human and machine intelligence can be combined in technologies that best contribute to social and global progress.

Anna Korhonen, Director of CHIA and Professor of Natural Language Processing, said: “We know from history that new technologies can drive changes with both positive and negative consequences, and this will likely be the case for AI. ̽��ֱ��goal of our new Centre is to put humans at the centre of every stage of AI development – basic research, application, commercialisation and policymaking – to help ensure AI benefits everyone."

Artificial intelligence is a rapidly developing technology predicted to transform much of our society. While AI has the potential to tackle some of the world’s most pressing problems in healthcare, education, climate science and economic sustainability it will need to embrace its human origins to become responsible, transparent and inclusive.

Per-Ola Kristensson, Co-director of CHIA and Professor of Interactive Systems Engineering, said: “For true progress and real-life impact it’s critical to nurture a close engagement with industry, policy makers, non-governmental organisations and civil society. Few universities in the world can rival the breadth and depth of Cambridge making us ideally positioned to make these connections and engage with the communities who face the greatest impact from AI.”

Designed to deliver both academic and real-world impact, CHIA seeks partners in academic, industrial, third-sector and other organizations that share an interest in promoting human-inspired AI.

John Suckling, Co-director of CHIA and Director of Research in Psychiatric Neuroimaging, said: “Our students will be educated in an interdisciplinary environment with access to experts in the technical, ethical, human and industrial aspects of AI. Early-career researchers will be part of all our activities. We are committed to inclusivity and diversity as a way of delivering robust and practical outcomes.”

CHIA will educate the next generation of AI creators and leaders, with dedicated graduate training in human-inspired AI.

Professor Mark Girolami from the Department of Engineering, said: “As artificial intelligence becomes increasingly pervasive, it’s critical to align its development with societal interests. This new ̽��ֱ��-wide Centre will explore a human-centric approach to the development of AI to ensure beneficial outcomes for society. Cambridge's depth of expertise in AI and a focus on interdisciplinary collaboration make it an ideal home for CHIA.”

Apart from research and education, the CHIA will also host seminars, public events and international conferences to raise awareness of human-inspired AI. Forums will be convened around topics of ethical or societal concern with representation from all stakeholders.

Professor Anne Ferguson-Smith, Pro-Vice-Chancellor for Research, said: “If we’re to ensure that AI works for everyone and does not widen inequalities, then we need to place people at its heart and consider the societal and ethical implications alongside its development. Cambridge, with its ability to draw on researchers across multiple disciplines, is uniquely positioned to be able to lead in this area.”

Neil Lawrence, DeepMind Professor of Machine Learning, added: “Artificial intelligence is provoking new questions in our societies. It’s vital that we deliver the answers in a people-centric manner. ̽��ֱ��Centre in Human-Inspired AI will provide a new interdisciplinary hub that delivers the solutions for these challenges.”

̽��ֱ�� ̽��ֱ�� of Cambridge today launches a new research centre dedicated to exploring the possibilities of a world shared by both humans and machines with artificial intelligence (AI).

Introducing ̽��ֱ��Centre for Human-Inspired Artificial Intelligence

Olemedia (Getty Images)

Illustration representing artificial intelligence

̽��ֱ��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.

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AI system may accelerate search for cancer discoveries

sc604 — Tue, 27 Nov 2018 12:09:25 +0000

̽��ֱ��system, called LION LBD and developed by computer scientists and cancer researchers at the ̽��ֱ�� of Cambridge, has been designed to assist scientists in the search for cancer-related discoveries. It is the first literature-based discovery system aimed at supporting cancer research. ̽��ֱ��results are reported in the journal Bioinformatics.

Global cancer research attracts massive amounts of funding worldwide, and the scientific literature is now so huge that researchers are struggling to keep up with it: critical hypothesis-generating evidence is now often discovered long after it was published.

Cancer is a complex class of diseases that are not completely understood and are the second-leading cause of death worldwide. Cancer development involves changes in numerous chemical and biochemical molecules, reactions and pathways, and cancer research is being conducted across a wide variety of scientific fields, which have variability in the way that they describe similar concepts.

“As a cancer researcher, even if you knew what you were looking for, there are literally thousands of papers appearing every day,” said Professor Anna Korhonen, Co-Director of Cambridge’s Language Technology Lab who led the development of LION LBD in collaboration with Dr Masashi Narita at Cancer Research UK Cambridge Institute and Professor Ulla Stenius at Karolinska Institutet in Sweden. “LION LBD uses AI to help scientists keep up-to-date with published discoveries in their field, but could also help them make new discoveries by combining what is already known in the literature by making connections between sources that may appear to be unrelated.”

̽��ֱ��‘LBD’ in LION LBD stands for Literature-Based Discovery, a concept developed in the 1980s which seeks to make new discoveries by combing pieces of information from disconnected sources. ̽��ֱ��key idea behind the original version of LBD is that concepts that are never explicitly linked in the literature may be indirectly linked through intermediate concepts.

̽��ֱ��design of the LION LBD system allows real-time search to discover indirect associations between entities in a database of tens of millions of publications while preserving the ability of users to explore each mention in its original context.

“For example, you may know that a cancer drug affects the behaviour of a certain pathway, but with LION LBD, you may find that a drug developed for a totally different disease affects the same pathway,” said Korhonen.

LION LBD is the first system developed specifically for the needs of cancer research. It has a particular focus on the molecular biology of cancer and uses state-of-the-art machine learning and natural language processing techniques, in order to detect references to the hallmarks of cancer in the text. Evaluations of the system have demonstrated its ability to identify undiscovered links and to rank relevant concepts highly among potential connections.

̽��ֱ��system is built using open data, open source and open standards, and is available as an interactive web-based interface or a programmable API.

̽��ֱ��researchers are currently working on extending the scope of LION-LBD to include further concepts and relations. They are also working closely with cancer researchers to help and improve the technology for end users.

̽��ֱ��system was developed in collaboration with ̽��ֱ�� of Cambridge Language Technology Lab, Cancer Research UK Cambridge Institute, and Karolinska Institutet in Sweden, and was funded by the Medical Research Council.

Reference:
Sampo Pyysalo et al. ‘LION LBD: a Literature-Based Discovery System for Cancer Biology.’ Bioinformatics (2018). DOI: 10.1093/bioinformatics/bty845

Searching through the mountains of published cancer research could be made easier for scientists, thanks to a new AI system.

As a cancer researcher, even if you knew what you were looking for, there are literally thousands of papers appearing every day

Anna Korhonen

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Skin cancer cells from a mouse show how cells attach at contact points

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AI crossword-solving application could make machines better at understanding language

sc604 — Mon, 07 Mar 2016 09:38:00 +0000

Researchers have designed a web-based platform which uses artificial neural networks to answer standard crossword clues better than existing commercial products specifically designed for the task. ̽��ֱ��system could help machines understand language more effectively.

In tests against commercial crossword-solving software, the system, designed by researchers from the UK, US and Canada, was more accurate at answering clues that were single words (e.g. ‘culpability’ – guilt), a short combination of words (e.g. ‘devil devotee’ – Satanist), or a longer sentence or phrase (e.g. ‘French poet and key figure in the development of Symbolism’ – Baudelaire). ̽��ֱ��system can also be used a ‘reverse dictionary’ in which the user describes a concept and the system returns possible words to describe that concept.

̽��ֱ��researchers used the definitions contained in six dictionaries, plus Wikipedia, to ‘train’ the system so that it could understand words, phrases and sentences – using the definitions as a bridge between words and sentences. Their results, published in the journal Transactions of the Association for Computational Linguistics, suggest that a similar approach may lead to improved output from more general language understanding and dialogue systems and information retrieval engines in general. All of the code and data behind the application has been made freely available for future research.

“Over the past few years, there’s been a mini-revolution in machine learning,” said Felix Hill of the ̽��ֱ�� of Cambridge’s Computer Laboratory, one of the paper’s authors. “We’re seeing a lot more usage of deep learning, which is especially useful for language perception and speech recognition.”

Deep learning refers to an approach in which artificial neural networks with little or no prior ‘knowledge’ are trained to recreate human abilities using massive amounts of data. For this particular application, the researchers used dictionaries – training the model on hundreds of thousands of definitions of English words, plus Wikipedia.

“Dictionaries contain just about enough examples to make deep learning viable, but we noticed that the models get better and better the more examples you give them,” said Hill. “Our experiments show that definitions contain a valuable signal for helping models to interpret and represent the meaning of phrases and sentences.”

Working with Anna Korhonen from the Cambridge’s Department of Theoretical and Applied Linguistics, and researchers from the Université de Montréal and New York ̽��ֱ��, Hill used the model as a way of bridging the gap between machines that understand the meanings of individual words and machines that can understand the meanings of phrases and sentences.

“Despite recent progress in AI, problems involving language understanding are particularly difficult, and our work suggests many possible applications of deep neural networks to language technology,” said Hill. “One of the biggest challenges in training computers to understand language is recreating the many rich and diverse information sources available to humans when they learn to speak and read.”

However, there is still a long way to go. For instance, when Hill’s system receives a query, the machine has no idea about the user’s intention or the wider context of why the question is being asked. Humans, on the other hand, can use their background knowledge and signals like body language to figure out the intent behind the query.

Hill describes recent progress in learning-based AI systems in terms of behaviourism and cognitivism: two movements in psychology that effect how one views learning and education. Behaviourism, as the name implies, looks at behaviour without looking at what the brain and neurons are doing, while cognitivism looks at the mental processes that underlie behaviour. Deep learning systems like the one built by Hill and his colleagues reflect a cognitivist approach, but for a system to have something approaching human intelligence, it would have to have a little of both.

“Our system can’t go too far beyond the dictionary data on which it was trained, but the ways in which it can are interesting, and make it a surprisingly robust question and answer system – and quite good at solving crossword puzzles,” said Hill. While it was not built with the purpose of solving crossword puzzles, the researchers found that it actually performed better than commercially-available products that are specifically engineered for the task.

Existing commercial crossword-answering applications function in a similar way to a Google search, with one system able to reference over 1100 dictionaries. While this approach has advantages if you want to look up a definition verbatim, it works less well when you input a question or query that the model has never seen in training. It also makes it incredibly ‘heavy’ in terms of the amount of memory it requires. “Traditional approaches are like lugging many heavy dictionaries around with you, whereas our neural system is incredibly light,” said Hill.

According to the researchers, the results show the effectiveness of definition-based training for developing models that understand phrases and sentences. They are currently looking at ways of enhancing their system, specifically by combining it with more behaviourist-style models of language learning and linguistic interaction.

Reference:
Hill, Felix et al. Learning to Understand Phrases by Embedding the Dictionary. Transactions of the Association for Computational Linguistics, [S.l.], v. 4, p. 17-30, feb. 2016. ISSN 2307-387X.

A web-based machine language system solves crossword puzzles far better than commercially-available products, and may help machines better understand language.

Despite recent progress in AI, problems involving language understanding are particularly difficult.

Felix Hill

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Crossword

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Mining the language of science

lw355 — Fri, 18 Nov 2011 09:00:55 +0000

Ask any biomedical scientist whether they manage to keep on top of reading all of the publications in their field, let alone an adjacent field, and few will say yes. New publications are appearing at a double-exponential rate, as measured by MEDLINE – the US National Library of Medicine’s biomedical bibliographic database – which now lists over 19 million records and adds up to 4,000 new records daily.

For a prolific field such as cancer research, the number of publications could quickly become unmanageable and important hypothesis-generating evidence may be missed. But what if scientists could instruct a computer to help them?

To be useful, a computer would need to trawl through the literature in the same way that a scientist would: reading the literature to uncover new knowledge, evaluating the quality of the information, looking for patterns and connections between facts, and then generating hypotheses to test. Not only might such a program speed up the progress of scientific discovery but, with the capacity to consider vast numbers of factors, it might even discover information that could be missed by the human brain.

̽��ֱ��aim of Dr Anna Korhonen and researchers in the Natural Language and Information Processing Group in the ̽��ֱ�� of Cambridge's Computer Laboratory is to develop computers that can understand written language in the same way that humans do. One of the projects she is involved in has recently developed a method of ‘text mining’ one of the most literature-dependent areas of biomedicine: cancer risk assessment of chemicals.

Every year, thousands of new chemicals are developed, any one of which might pose a potential risk to human health. Complex risk assessment procedures are in place to determine the relationship between exposure and the likelihood of developing cancer, but it’s a lengthy process, as Royal Society ̽��ֱ�� Research Fellow Dr Korhonen explained: “ ̽��ֱ��first stage of any risk assessment is a literature review. It’s a major bottleneck. There could be tens of thousands of articles for a single chemical. Performed manually, it’s expensive and, because of the rising number of publications, it’s becoming too challenging to manage.”

CRAB, the tool her team has developed in collaboration with Professor Ulla Stenius’ group at the Institute of Environmental Medicine at Sweden’s Karolinska Institutet, is a novel approach to cancer risk assessment that could help risk assessors move beyond manual literature review.

̽��ֱ��approach is based on text-mining technology, which has been pioneered by computer scientists, and involves developing programs that can analyse natural language texts, despite their complexity, inconsistency and ambiguity. ̽��ֱ��tool Dr Korhonen has developed with her colleagues is the first text-mining tool aimed at aiding literature review in chemical risk assessment.

At the heart of CRAB, the development of which was funded by the Medical Research Council and the Swedish Research Council among others, is a taxonomy that specifies scientific evidence used in cancer risk assessment, including key events that may result in cancer formation. ̽��ֱ��system takes the textual content of each relevant MEDLINE abstract and classifies it according to the taxonomy. At the press of a button, a profile is rapidly built for any particular chemical using all of the available literature, describing highly specific patterns of connections between chemicals and toxicity.

“Although still under development, the system can be used to make connections that would be difficult to find, even if it had been possible to read all the documents,” added Dr Korhonen. “In a recent experiment, we studied a group of chemicals with unknown mode of action and used the CRAB tool to suggest a new hypothesis that might explain their male-specific carcinogenicity in the pancreas.”

̽��ֱ��tool will be available for end-users via an online web interface. However, research into improving text mining will continue. One of the biggest current challenges is to develop adaptive technology that can be ported easily between different text types, tasks and scientific fields.

One day, rather than being at the mercy of the flourishing rate of publication, scientists will have at their fingertips a system to work alongside them that will not only point them towards those references that are relevant to their search, but will also tell them why.

Scientists are developing a computer that can read vast amounts of scientific literature, make connections between facts and develop hypotheses.

Although still under development, the system can be used to make connections that would be difficult to find, even if it had been possible to read all the documents.

Dr Anna Korhonen

Categorising textual information

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