̽��ֱ�� of Cambridge - Zoubin Ghahramani

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.

Yes

Cambridge researcher named as Turing AI World-Leading Researcher Fellow

Anonymous — Fri, 30 Jul 2021 09:26:42 +0000

̽��ֱ��other new Fellows are Professor Samuel Kaski from the ̽��ֱ�� of Manchester, Professor Mirella Lapata from the ̽��ֱ�� of Edinburgh, Professor Philip Torr from the ̽��ֱ�� of Oxford, and Professor Michael Wooldridge from the ̽��ֱ�� of Oxford.

̽��ֱ��fellowships, named after AI pioneer Alan Turing, are part of the UK’s commitment to further strengthen its position as a global leader in the field.

Retaining and attracting some of the best international research talent in a highly competitive international environment will increase the UK’s competitive advantage and capability in AI.

̽��ֱ��fellows’ research will have a transformative effect on the international AI research and innovation landscape by tackling some of the fundamental challenges in the field.

It could also deliver major societal impact in areas including decision-making in personalised medicine, synthetic biology and drug design, financial modelling, and autonomous vehicles.

Professor Ghahramani, from Cambridge’s Department of Engineering, is Senior Director and Distinguished Researcher at Google, former Chief Scientist at Uber and a Fellow of the Royal Society.

In his fellowship, which he will hold jointly while continuing to work at Google, he aims to develop the new algorithms and applications needed to address limitations faced by the AI systems that underpin technologies such as speech recognition and autonomous vehicles. This includes ensuring they can better adapt to new data and apply data-driven machine learning approaches to simulators to understand complex systems.

“ ̽��ֱ��Turing AI Fellowships provide a fantastic opportunity to grow the UK’s research talent in AI, and to build stronger relationships between industry and academia,” said Ghahramani. “Most modern AI systems are based on machine learning technology that learns from patterns in data. This research programme aims to improve such systems by making them more robust and reliable, so that they can better respond to changing circumstances, and better incorporate prior knowledge, symbolic reasoning and data.”

̽��ֱ��fellows are supported with an £18 million investment by UK Research and Innovation (UKRI).

In addition to this, 39 different collaborators including IBM, AstraZeneca and Facebook are making contributions worth £15.7 million to the fellows’ research programmes.

̽��ֱ��fellowships are being delivered by UKRI’s Engineering and Physical Sciences Research Council.

“ ̽��ֱ��Turing AI World-Leading Researcher Fellowships recognise internationally-leading researchers in AI, and provide the support needed to tackle some of the biggest challenges and opportunities in AI research,” said EPSRC Executive Chair Professor Dame Lynn Gladden. “These fellowships enable the UK to attract top international talent to the UK as well as retaining our own world-leaders. Attracting and retaining top talent is essential to keep the UK at the leading edge of AI research and innovation.”

̽��ֱ��Turing AI Fellowships investment is delivered in partnership by UKRI, the Office for AI, and ̽��ֱ��Alan Turing Institute, the national institute for data science and AI.

Five internationally-recognised researchers, including Cambridge’s Professor Zoubin Ghahramani, have been appointed as the first Turing AI World-Leading Researcher Fellows to conduct work on artificial intelligence’s (AI) biggest challenges.

UKRI

Professor Zoubin Ghahramani

̽��ֱ��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

Nine Cambridge researchers among this year’s Royal Society medal and award winners

Anonymous — Tue, 04 Aug 2020 05:00:00 +0000

He is one of the 25 Royal Society medals and awards winners announced today, nine of whom are researchers at the ̽��ֱ�� of Cambridge. ̽��ֱ��annual prizes celebrate exceptional researchers and outstanding contributions to science across a wide array of fields.

President of the Royal Society, Venki Ramakrishnan, said:

" ̽��ֱ��Royal Society’s medals and awards celebrate those researchers whose ground-breaking work has helped answer fundamental questions and advance our understanding of the world around us. They also champion those who have reinforced science’s place in society, whether through inspiring public engagement, improving our education system, or by making STEM careers more inclusive and rewarding.

"This year has highlighted how integral science is in our daily lives, and tackling the challenges we face, and it gives me great pleasure to congratulate all our winners and thank them for their work."

Sir Alan Fersht FMedSci FRS, Emeritus Professor in the Department of Chemistry and former Master of Gonville and Caius College, is awarded the Copley Medal for the development and application of methods to describe protein folding pathways at atomic resolution, revolutionising our understanding of these processes.

"Most of us who become scientists do so because science is one of the most rewarding and satisfying of careers and we actually get paid for doing what we enjoy and for our benefitting humankind. Recognition of one’s work, especially at home, is icing on the cake," said Sir Alan. "Like many Copley medallists, I hail from a humble immigrant background and the first of my family to go to university. If people like me are seen to be honoured for science, then I hope it will encourage young people in similar situations to take up science."

As the latest recipient of the Royal Society’s premier award, Sir Alan joins an elite group of scientists, that includes Charles Darwin, Albert Einstein and Dorothy Hodgkin, and more recently Professor John Goodenough (2020) for his research on the rechargeable lithium battery, Peter Higgs (2015), the physicist who hypothesised the existence of the Higgs Boson, and DNA fingerprinting pioneer Alec Jeffreys (2014).

Professor Barry Everitt FMedSci FRS, from the Department of Psychology and former Master of Downing College, receives the Croonian Medal and Lecture for research which has elucidated brain mechanisms of motivation and applied them to important societal issues such as drug addiction.

Professor Everitt said: "In addition to my personal pride about having received this prestigious award, I hope that it helps draw attention to experimental addiction research, its importance and potential."

Professor Herbert Huppert FRS of the Department of Applied Mathematics and Theoretical Physics, and a Fellow of King’s College, receives a Royal Medal for outstanding achievements in the physical sciences. He has been at the forefront of research in fluid mechanics. As an applied mathematician he has consistently developed highly original analysis of key natural and industrial processes. Further to his research, he has chaired policy work on how science can help defend against terrorism, and carbon capture and storage in Europe.

In addition to the work for which they are recognised with an award, several of this year’s recipients have also been working on issues relating to the COVID-19 pandemic.

Professor Julia Gog of the Department of Applied Mathematics and Theoretical Physics and a Fellow of Queens’ College, receives the Rosalind Franklin Award and Lecture for her achievements in the field of mathematics. Her expertise in infectious diseases and virus modelling has seen her contribute to the pandemic response, including as a participant at SAGE meetings. ̽��ֱ��STEM project component of her award will produce resources for Key Stage 3 (ages 11-14) maths pupils and teachers exploring the curriculum in the context of modelling epidemics and infectious diseases and showing how maths can change the world for the better.

̽��ֱ��Society’s Michael Faraday Prize is awarded to Sir David Spiegelhalter OBE FRS, of the Winton Centre for Centre for Risk and Evidence Communication and a Fellow of Churchill College, for bringing key insights from the disciplines of statistics and probability vividly home to the public at large, and to key decision-makers, in entertaining and accessible ways, most recently through the COVID-19 pandemic.

̽��ֱ��full list of Cambridge’s 2020 winners and their award citations:

Copley Medal
Alan Fersht FMedSci FRS, Department of Chemistry, and Gonville and Caius College
He has developed and applied the methods of protein engineering to provide descriptions of protein folding pathways at atomic resolution, revolutionising our understanding of these processes.

Croonian Medal and Lecture
Professor Barry Everitt FMedSci FRS, Department of Psychology and Downing College
He has elucidated brain mechanisms of motivation and applied them to important societal issues such as drug addiction.

Royal Medal A
Professor Herbert Huppert FRS, Department of Applied Mathematics and Theoretical Physics, and King’s College
He has been at the forefront of research in fluid mechanics. As an applied mathematician he has consistently developed highly original analysis of key natural and industrial processes.

Hughes Medal
Professor Clare Grey FRS, Department of Chemistry and Pembroke College
For her pioneering work on the development and application of new characterization methodology to develop fundamental insight into how batteries, supercapacitors and fuel cells operate.

Ferrier Medal and Lecture
Professor Daniel Wolpert FMedSci FRS, Department of Engineering and Trinity College
For ground-breaking contributions to our understanding of how the brain controls movement. Using theoretical and experimental approaches he has elucidated the computational principles underlying skilled motor behaviour.

Michael Faraday Prize and Lecture
Sir David Spiegelhalter OBE FRS, Winton Centre for Risk and Evidence Communication and Churchill College
For bringing key insights from the disciplines of statistics and probability vividly home to the public at large, and to key decision-makers, in entertaining and accessible ways, most recently through the COVID-19 pandemic.

Milner Award and Lecture
Professor Zoubin Ghahramani FRS, Department of Engineering and St John’s College
For his fundamental contributions to probabilistic machine learning.

Rosalind Franklin Award and Lecture
Professor Julia Gog, Department of Applied Mathematics and Theoretical Physics, and Queens’ College
For her achievements in the field of mathematics and her impactful project proposal with its potential for a long-term legacy.

Royal Society Mullard Award
Professor Stephen Jackson FMedSci FRS, Gurdon Institute, Department of Biochemistry
For pioneering research on DNA repair mechanisms and synthetic lethality that led to the discovery of olaparib, which has reached blockbuster status for the treatment of ovarian and breast cancers.

̽��ֱ��full list of medals and awards, including their description and past winners can be found on the Royal Society website: https://royalsociety.org/grants-schemes-awards/awards/

Adapted from a Royal Society press release.

A leading pioneer in the field of protein engineering, Sir Alan Fersht FMedSci FRS, has been named as the 2020 winner of the world’s oldest scientific prize, the Royal Society’s prestigious Copley Medal.

Yes

AI: Life in the age of intelligent machines

lw355 — Fri, 22 Feb 2019 14:00:18 +0000

We are said to be standing on the brink of a fourth industrial revolution – one that will see new forms of artificial intelligence (AI) underpinning almost every aspect of our lives. ̽��ֱ��new technologies will help us to tackle some of the greatest challenges that face our world.

In fact AI is already very much part of our daily lives, says Dr Mateja Jamnik, one of the experts who appear in the film. “Clever algorithms are being executed in clever ways all around us... and we are only a decade away from a future where we are able to converse across multiple languages, where doctors will be able to diagnose better, where drivers will be able to drive more safely.”

Ideas around AI “are being dreamt up by thousands of people all over the world – imaginative young people who see a problem and think about how they can solve it using AI… whether it’s recommending a song you’ll like or curing us of cancer,” says Professor Stephen Cave.

Much of the excitement relates to being able to leverage the power of Big Data, says Professor Zoubin Ghahramani. Without AI, how else could we make sense of the vastly complex interconnected systems we now have at our fingertips?

But what do we think about AI and the future it promises? Our perceptions are shaped by our cultural prehistory, stretching right back to Homer, says Dr Sarah Dillon. How we feel about the dawning of a new technology is linked to centuries-old thinking about robotics, automatons and intelligence beyond our own.

And what happens when we come to rely on the tools we are empowering to do these amazing things? Professor Lord Martin Rees reflects on the transition to a future of AI-aided jobs: what will this look like? How will we ensure that the wealth created by AI will benefit wider society and avoid worsening inequality?

Our researchers are asking fundamental questions about the ethics, trust and humanity of AI system design. “It can’t simply be enough for the leading scientists as brilliant as they are to be pushing ahead as quickly as possible,” says Dr Seán Ó hÉigeartaigh. “We also need there to be ongoing conversations and collaborations with the people who are thinking about the ethical impacts of the technology.

“ ̽��ֱ��idea that AI can help us understand ourselves and the universe at a much deeper level is about as far reaching a goal for AI as could be.”

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

In a new film, leading Cambridge ̽��ֱ�� researchers discuss the far-reaching advances offered by artificial intelligence – and consider the consequences of developing systems that think far beyond human abilities.

̽��ֱ��idea that AI can help us understand ourselves and the universe at a much deeper level is about as far reaching a goal for AI as could be

Seán Ó hÉigeartaigh

AI: Humanity's Last Invention?

Yes

̽��ֱ��uncertain unicycle that taught itself and how it’s helping AI make good decisions

lw355 — Wed, 14 Feb 2018 13:51:19 +0000

In the centre of the screen is a tiny unicycle. ̽��ֱ��animation starts, the unicycle lurches forward and falls. This is trial #1. It’s now trial #11 and there’s a change – an almost imperceptible delay in the fall, perhaps an attempt to right itself before the inevitable crash. “It’s learning from experience,” nods Professor Carl Edward Rasmussen.

After a minute, the unicycle is gently rocking back and forth as it circles on the spot. It’s figured out how this extremely unstable system works and has mastered its goal. “ ̽��ֱ��unicycle starts with knowing nothing about what’s going on – it’s only been told that its goal is to stay in the centre in an upright fashion. As it starts falling forwards and backwards, it starts to learn,” explains Rasmussen, who leads the Computational and Biological Learning Lab in the Department of Engineering. “We had a real unicycle robot but it was actually quite dangerous – it was strong – and so now we use data from the real one to run simulations, and we have a mini version.”

Rasmussen uses the self-taught unicycle to demonstrate how a machine can start with very little data and learn dynamically, improving its knowledge every time it receives new information from its environment. ̽��ֱ��consequences of adjusting its motorised momentum and balance help the unicycle to learn which moves were important in helping it to stay upright in the centre.

“This is just like a human would learn,” explains Professor Zoubin Ghahramani, who leads the Machine Learning Group in the Department of Engineering. “We don’t start knowing everything. We learn things incrementally, from only a few examples, and we know when we are not yet confident in our understanding.”

Ghahramani’s team is pioneering a branch of AI called continual machine learning. He explains that many of the current forms of machine learning are based on neural networks and deep learning models that use complex algorithms to find patterns in vast datasets. Common applications include translating phrases into different languages, recognising people and objects in images, and detecting unusual spending on credit cards.

“These systems need to be trained on millions of labelled examples, which takes time and a lot of computer memory,” he explains. “And they have flaws. When you test them outside of the data they were trained on they tend to perform poorly. Driverless cars, for instance, may be trained on a huge dataset of images but they might not be able to generalise to foggy conditions.

“Worse than that, the current deep learning systems can sometimes give us confidently wrong answers, and provide limited insight into why they have come to particular decisions. This is what bothers me. It’s okay to be wrong but it’s not okay to be confidently wrong.”

̽��ֱ��key is how you deal with uncertainty – the uncertainty of messy and missing data, and the uncertainty of predicting what might happen next. “Uncertainty is not a good thing – it’s something you fight, but you can’t fight it by ignoring it,” says Rasmussen. “We are interested in representing the uncertainty.”

It turns out that there’s a mathematical theory that tells you what to do. It was first described by 18th-century English statistician Thomas Bayes. Ghahramani’s group was one of the earliest adopters in AI of Bayesian probability theory, which describes how the probability of an event occurring (such as staying upright in the centre) is updated as more evidence (such as the decision the unicycle last took before falling over) becomes available.

Dr Richard Turner explains how Bayes’ rule handles continual learning: “the system takes its prior knowledge, weights it by how accurate it thinks that knowledge is, then combines it with new evidence that is also weighted by its accuracy.

“This is much more data-efficient than the way a standard neural network works,” he adds. “New information can cause a neural network to forget everything it learned previously – called catastrophic forgetting – meaning it needs to look at all of its labelled examples all over again, like relearning the rules and glossary of a language every time you learn a new word.

“Our system doesn’t need to revisit all the data it’s seen before – just like humans don’t remember all past experiences; instead we learn a summary and we update it as things go on.” Ghahramani adds: “ ̽��ֱ��great thing about Bayesian machine learning is the system makes decisions based on evidence – it’s sometimes thought of as ‘automating the scientific method’ – and because it’s based on probability, it can tell us when it’s outside its comfort zone.”

Ghahramani is also Chief Scientist at Uber. He sees a future where machines are continually learning not just individually but as part of a group. “Whether it’s companies like Uber optimising supply and demand, or autonomous vehicles alerting each other to what’s ahead on the road, or robots working together to lift a heavy load – cooperation, and sometimes competition, in AI will help solve problems across a huge range of industries.”

One of the really exciting frontiers is being able to model probable outcomes in the future, as Turner describes. “ ̽��ֱ��role of uncertainty becomes very clear when we start to talk about forecasting future problems such as climate change.”

Turner is working with climate scientists Dr Emily Shuckburgh and Dr Scott Hosking at the British Antarctic Survey to ask whether machine learning techniques can improve understanding of climate change risks in the future.

“We need to quantify the future risk and impacts of extreme weather at a local scale to inform policy responses to climate change,” explains Shuckburgh. “ ̽��ֱ��traditional computer simulations of the climate give us a good understanding of the average climate conditions. What we are aiming to do with this work is to combine that knowledge with observational data from satellites and other sources to get a better handle on, for example, the risk of low-probability but high-impact weather events.”

“It’s actually a fascinating machine learning challenge,” says Turner, who is helping to identify which area of climate modelling is most amenable to using Bayesian probability. “ ̽��ֱ��data are extremely complex, and sometimes missing and unlabelled. ̽��ֱ��uncertainties are rife.” One significant element of uncertainty is the fact that the predictions are based on our future reduction of emissions, the extent of which is as yet unknown.

“An interesting part of this for policy makers, aside from the forecasting value, is that you can imagine having a machine that continually learns from the consequences of mitigation strategies such as reducing emissions – or the lack of them – and adjusts its predictions accordingly,” adds Turner.

What he is describing is a machine that – like the unicycle – feeds on uncertainty, learns continuously from the real world, and assesses and then reassesses all possible outcomes. When it comes to climate, however, it’s also a machine of all possible futures.

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

Cambridge researchers are pioneering a form of machine learning that starts with only a little prior knowledge and continually learns from the world around it.

This is just like a human would learn. We don’t start knowing everything. We learn things incrementally, from only a few examples, and we know when we are not yet confident in our understanding

Zoubin Ghahramani

̽��ֱ��District

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

Yes

Cambridge and AI: what makes this city a good place to start a business?

sc604 — Tue, 13 Feb 2018 08:00:00 +0000

On any given day, some of the world’s brightest minds in the areas of AI and machine learning can be found riding the train between Cambridge and London King’s Cross.

Five of the biggest tech companies in the world – Google, Facebook, Apple, Amazon and Microsoft – all have offices at one or both ends of the train line. Apart from the tech giants, however, both cities (and Oxford, the third corner of the UK’s so-called golden triangle) also support thriving ecosystems of start-ups. Over the past decade, start-ups based on AI and machine learning, in Cambridge and elsewhere, have seen explosive growth.

Of course, it’s not unexpected that a cluster of high-tech companies would sprout up next to one of the world’s leading universities. But what is it that makes Cambridge, a small city on the edge of the Fens, such a good place to start a business?

“In my experience, Silicon Valley is 10% tech and 90% hype, but Cambridge is just the opposite,” says Vishal Chatrath, CEO of PROWLER.io, a Cambridge-based AI company. “As an entrepreneur, I want to bring world-changing technology to market. ̽��ֱ��way you do that is to make something that’s never existed before and create the science behind it. Cambridge, with its rich history of mathematicians, has the kind of scientific ambition to do that.”

“ ̽��ֱ��ecosystem in Cambridge is really healthy,” says Professor Carl Edward Rasmussen from Cambridge’s Department of Engineering, and Chair of PROWLER.io. “ ̽��ֱ��company has been expanding at an incredible rate, and I think this is something that can only happen in Cambridge.”

PROWLER.io is developing what it calls the world’s first ‘principled’ AI decision-making platform, which could be used in a variety of sectors, including autonomous driving, logistics, gaming and finance. Most AI decision-making platforms tend to view the world like an old-fashioned flowchart, in which the world is static. But in the real world, every time a decision is made, there are certain parameters to take into account.

“If you could take every decision-making point and treat it as an autonomous AI agent, you could understand the incentives under which the decision is made,” says Chatrath. “Every time these agents make a decision, it changes the environment, and the agents have an awareness of all the other agents. All these things work together to make the best decision.”

For example, autonomous cars running PROWLER.io’s platform would communicate with one another to alleviate traffic jams by re-routing automatically. “Principled AI is almost an old-fashioned way of thinking about the world,” says Chatrath. “Humans are capable of making good decisions quickly, and probabilistic models like ours are able to replicate that, but with millions of data points. Data isn’t king: the model is king. And that’s what principled AI means.”

Could PROWLER.io be the next big success story from the so-called ‘Cambridge cluster’ of knowledge-intensive firms? In just under two years, the company has grown to more than 60 employees, has filed multiple patents and published papers. Many of the people working at the company have deep links with the ̽��ֱ�� and its research base, and many have worked for other Cambridge start-ups. Like any new company, what PROWLER.io needs to grow is talent, whether it’s coming from Cambridge or from farther afield.

“There’s so much talent here already, but it’s also relatively easy to convince people to move to Cambridge,” says Rasmussen. “Even with the uncertainty that comes along with working for a start-up, there’s so much going on here that even if a start-up isn’t ultimately successful, there are always new opportunities for talented people because the ecosystem is so rich.”

“Entrepreneurs in Cambridge really support one another – people often call each other up and bounce ideas around,” says Carol Cheung, an Investment Associate at Cambridge Innovation Capital (CIC). “You don’t often see that degree of collaboration in other places.”

CIC is a builder of high-growth technology companies in the Cambridge Cluster and has been an important addition to the Cambridge ecosystem. It provides long-term support to companies that helps to bridge the critical middle stage of commercial development – the ‘valley of death’ between when a company first receives funding and when it begins to generate steady revenue – and is a preferred investor for the ̽��ֱ�� of Cambridge. One of CIC’s recent investments was to lead a £10 million funding round for PROWLER.io, and it will work with the company to understand where the best commercial applications are for their platform.

AI and machine learning companies like PROWLER.io are clearly tapping into what could be a massive growth area for the UK economy: PwC estimates that AI could add £232 billion to the economy by 2030, and the government’s Industrial Strategy describes investments aimed at making the UK a global centre for AI and data-driven innovation. But given the big salaries that can come with a career in big tech, how can universities prevent a ‘brain drain’ in their computer science, engineering and mathematics departments?

̽��ֱ�� ̽��ֱ�� has a long tradition of entrepreneurial researchers who have built and sold multiple companies while maintaining their academic careers, running labs and teaching students. “People from academia are joining us and feeding back into academia – in Cambridge, there’s this culture of ideas going back and forth,” says Chatrath.

“Of course some people will choose to pursue a career in industry, but Cambridge has this great tradition of academics choosing to pursue both paths – perhaps one will take precedence over the other for a time, but it is possible here to be both an academic and an entrepreneur.”

“I don’t know of any other university in the world that lets you do this in terms of IP. It’s a pretty unique set-up that I can start a business, raise venture capital, and still retain a research position and do open-ended research. I feel very lucky,” says Dr Alex Kendall, who recently completed his PhD in Professor Roberto Cipolla’s group in the Department of Engineering, and founded Wayve, a Cambridge-based machine learning company. “A lot of other universities wouldn’t allow this, but here you can – and it’s resulted in some pretty amazing companies.”

“I didn’t get into this field because I thought it would be useful or that I’d start lots of companies – I got into it because I thought it was really interesting,” says Professor Zoubin Ghahramani, one of Cambridge’s high-profile entrepreneurial academics, who splits his time between the Department of Engineering and his Chief Scientist role at Uber. “There were so many false starts in AI when people thought this is going to be very useful and it wasn’t. Five years ago, AI was like any other academic field, but now it’s changing so fast – and we’ve got such a tremendous concentration of the right kind of talent here in Cambridge to take advantage of it.”

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

What makes a city as small as Cambridge a hotbed for AI and machine learning start-ups? A critical mass of clever people obviously helps. But there’s more to Cambridge’s success than that.

In my experience, Silicon Valley is 10% tech and 90% hype, but Cambridge is just the opposite.

Vishal Chatrath

̽��ֱ��District

Cambridge Cluster

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

Yes

Artificial intelligence: computer says YES (but is it right?)

lw355 — Thu, 20 Oct 2016 14:17:17 +0000

There would always be a first death in a driverless car and it happened in May 2016. Joshua Brown had engaged the autopilot system in his Tesla when a tractor-trailor drove across the road in front of him. It seems that neither he nor the sensors in the autopilot noticed the white-sided truck against a brightly lit sky, with tragic results.

Of course many people die in car crashes every day – in the USA there is one fatality every 94 million miles, and according to Tesla this was the first known fatality in over 130 million miles of driving with activated autopilot. In fact, given that most road fatalities are the result of human error, it has been said that autonomous cars should make travelling safer.

Even so, the tragedy raised a pertinent question: how much do we understand – and trust – the computers in an autonomous vehicle? Or, in fact, in any machine that has been taught to carry out an activity that a human would do?

We are now in the era of machine learning. Machines can be trained to recognise certain patterns in their environment and to respond appropriately. It happens every time your digital camera detects a face and throws a box around it to focus, or the personal assistant on your smartphone answers a question, or the adverts match your interests when you search online.

Machine learning is a way to program computers to learn from experience and improve their performance in a way that resembles how humans and animals learn tasks. As machine learning techniques become more common in everything from finance to healthcare, the issue of trust is becoming increasingly important, says Zoubin Ghahramani, Professor of Information Engineering in Cambridge's Department of Engineering.

Faced with a life or death decision, would a driverless car decide to hit pedestrians, or avoid them and risk the lives of its occupants? Providing a medical diagnosis, could a machine be wildly inaccurate because it has based its opinion on a too-small sample size? In making financial transactions, should a computer explain how robust is its assessment of the volatility of the stock markets?

“Machines can now achieve near-human abilities at many cognitive tasks even if confronted with a situation they have never seen before, or an incomplete set of data,” says Ghahramani. “But what is going on inside the ‘black box’? If the processes by which decisions were being made were more transparent, then trust would be less of an issue.”

His team builds the algorithms that lie at the heart of these technologies (the “invisible bit” as he refers to it). Trust and transparency are important themes in their work: “We really view the whole mathematics of machine learning as sitting inside a framework of understanding uncertainty. Before you see data – whether you are a baby learning a language or a scientist analysing some data – you start with a lot of uncertainty and then as you have more and more data you have more and more certainty.

“When machines make decisions, we want them to be clear on what stage they have reached in this process. And when they are unsure, we want them to tell us.”

One method is to build in an internal self-evaluation or calibration stage so that the machine can test its own certainty, and report back.

Two years ago, Ghahramani’s group launched the Automatic Statistician with funding from Google. ̽��ֱ��tool helps scientists analyse datasets for statistically significant patterns and, crucially, it also provides a report to explain how sure it is about its predictions.

“ ̽��ֱ��difficulty with machine learning systems is you don’t really know what’s going on inside – and the answers they provide are not contextualised, like a human would do. ̽��ֱ��Automatic Statistician explains what it’s doing, in a human-understandable form.”

Where transparency becomes especially relevant is in applications like medical diagnoses, where understanding the provenance of how a decision is made is necessary to trust it.

Dr Adrian Weller, who works with Ghahramani, highlights the difficulty: “A particular issue with new artificial intelligence (AI) systems that learn or evolve is that their processes do not clearly map to rational decision-making pathways that are easy for humans to understand.” His research aims both at making these pathways more transparent, sometimes through visualisation, and at looking at what happens when systems are used in real-world scenarios that extend beyond their training environments – an increasingly common occurrence.

“We would like AI systems to monitor their situation dynamically, detect whether there has been a change in their environment and – if they can no longer work reliably – then provide an alert and perhaps shift to a safety mode.” A driverless car, for instance, might decide that a foggy night in heavy traffic requires a human driver to take control.

Weller’s theme of trust and transparency forms just one of the projects at the newly launched £10 million Leverhulme Centre for the Future of Intelligence (CFI). Ghahramani, who is Deputy Director of the Centre, explains: “It’s important to understand how developing technologies can help rather than replace humans. Over the coming years, philosophers, social scientists, cognitive scientists and computer scientists will help guide the future of the technology and study its implications – both the concerns and the benefits to society.”

CFI brings together four of the world’s leading universities (Cambridge, Oxford, Berkeley and Imperial College, London) to explore the implications of AI for human civilisation. Together, an interdisciplinary community of researchers will work closely with policy-makers and industry investigating topics such as the regulation of autonomous weaponry, and the implications of AI for democracy.

Ghahramani describes the excitement felt across the machine learning field: “It’s exploding in importance. It used to be an area of research that was very academic – but in the past five years people have realised these methods are incredibly useful across a wide range of societally important areas.

“We are awash with data, we have increasing computing power and we will see more and more applications that make predictions in real time. And as we see an escalation in what machines can do, they will challenge our notions of intelligence and make it all the more important that we have the means to trust what they tell us.”

Artificial intelligence has the power to eradicate poverty and disease or hasten the end of human civilisation as we know it – according to a speech delivered by Professor Stephen Hawking 19 October 2016 at the launch of the Centre for the Future of Intelligence.

Computers that learn for themselves are with us now. As they become more common in ‘high-stakes’ applications like robotic surgery, terrorism detection and driverless cars, researchers ask what can be done to make sure we can trust them.

As we see an escalation in what machines can do, they will challenge our notions of intelligence and make it all the more important that we have the means to trust what they tell us

Zoubin Ghahramani

Thierry Ehrmann

2019 by ExperiensS

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“ ̽��ֱ��best or worst thing to happen to humanity” - Stephen Hawking launches Centre for the Future of Intelligence

sjr81 — Wed, 19 Oct 2016 14:58:23 +0000

Speaking at the launch of the £10million Leverhulme Centre for the Future of Intelligence (CFI) in Cambridge, Professor Hawking said the rise of AI would transform every aspect of our lives and was a global event on a par with the industrial revolution.

“Success in creating AI could be the biggest event in the history of our civilisation,” said Professor Hawking. “But it could also be the last – unless we learn how to avoid the risks. Alongside the benefits, AI will also bring dangers like powerful autonomous weapons or new ways for the few to oppress the many.

“We cannot predict what we might achieve when our own minds are amplified by AI. Perhaps with the tools of this new technological revolution, we will be able to undo some of the damage done to the natural world by the last one – industrialisation.”

̽��ֱ��Centre for the Future of Intelligence will initially focus on seven distinct projects in the first three-year phase of its work, reaching out to brilliant researchers and connecting them and their ideas to the challenges of making the best of AI. Among the initial research topics are: ‘Science, value and the future of intelligence’; ‘Policy and responsible innovation’; ‘Autonomous weapons – prospects for regulation’ and ‘Trust and transparency’.

̽��ֱ��Academic Director of the Centre, and Bertrand Russell Professor of Philosophy at Cambridge, Huw Price, said: “ ̽��ֱ��creation of machine intelligence is likely to be a once-in-a-planet’s-lifetime event. It is a future we humans face together. Our aim is to build a broad community with the expertise and sense of common purpose to make this future the best it can be.”

Many researchers now take seriously the possibility that intelligence equal to our own will be created in computers within this century. Freed of biological constraints, such as limited memory and slow biochemical processing speeds, machines may eventually become more intelligent than we are – with profound implications for us all.

AI pioneer Professor Maggie Boden ( ̽��ֱ�� of Sussex) sits on the Centre’s advisory board and spoke at this evening’s launch. She said: “AI is hugely exciting. Its practical applications can help us to tackle important social problems, as well as easing many tasks in everyday life. And it has advanced the sciences of mind and life in fundamental ways. But it has limitations, which present grave dangers given uncritical use. CFI aims to pre-empt these dangers, by guiding AI development in human-friendly ways.”

“Recent landmarks such as self-driving cars or a computer game winning at the game of Go, are signs of what’s to come,” added Professor Hawking. “ ̽��ֱ��rise of powerful AI will either be the best or the worst thing ever to happen to humanity. We do not yet know which. ̽��ֱ��research done by this centre is crucial to the future of our civilisation and of our species.”

Transcript of Professor Hawking’s speech at the launch of the Leverhulme Centre for the Future of Intelligence, October 19, 2016

“It is a great pleasure to be here today to open this new Centre. We spend a great deal of time studying history, which, let’s face it, is mostly the history of stupidity. So it is a welcome change that people are studying instead the future of intelligence.

Intelligence is central to what it means to be human. Everything that our civilisation has achieved, is a product of human intelligence, from learning to master fire, to learning to grow food, to understanding the cosmos.

I believe there is no deep difference between what can be achieved by a biological brain and what can be achieved by a computer. It therefore follows that computers can, in theory, emulate human intelligence — and exceed it.

Artificial intelligence research is now progressing rapidly. Recent landmarks such as self-driving cars, or a computer winning at the game of Go, are signs of what is to come. Enormous levels of investment are pouring into this technology. ̽��ֱ��achievements we have seen so far will surely pale against what the coming decades will bring.

̽��ֱ��potential benefits of creating intelligence are huge. We cannot predict what we might achieve, when our own minds are amplified by AI. Perhaps with the tools of this new technological revolution, we will be able to undo some of the damage done to the natural world by the last one — industrialisation. And surely we will aim to finally eradicate disease and poverty. Every aspect of our lives will be transformed. In short, success in creating AI, could be the biggest event in the history of our civilisation.

But it could also be the last, unless we learn how to avoid the risks. Alongside the benefits, AI will also bring dangers, like powerful autonomous weapons, or new ways for the few to oppress the many. It will bring great disruption to our economy. And in the future, AI could develop a will of its own — a will that is in conflict with ours.

In short, the rise of powerful AI will be either the best, or the worst thing, ever to happen to humanity. We do not yet know which. That is why in 2014, I and a few others called for more research to be done in this area. I am very glad that someone was listening to me!

̽��ֱ��research done by this centre is crucial to the future of our civilisation and of our species. I wish you the best of luck!”

Alongside the benefits, AI will also bring dangers, like powerful autonomous weapons, or new ways for the few to oppress the many.

Stephen Hawking

̽��ֱ��best or worst thing to happen to humanity

Nick Saffell

Stephen Hawking speaking at tonight's launch

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