̽��ֱ�� of Cambridge - Alex Kendall

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

Teaching machines to see: new smartphone-based system could accelerate development of driverless cars

sc604 — Mon, 21 Dec 2015 06:34:09 +0000

Two newly-developed systems for driverless cars can identify a user’s location and orientation in places where GPS does not function, and identify the various components of a road scene in real time on a regular camera or smartphone, performing the same job as sensors costing tens of thousands of pounds.

̽��ֱ��separate but complementary systems have been designed by researchers from the ̽��ֱ�� of Cambridge and demonstrations are freely available online. Although the systems cannot currently control a driverless car, the ability to make a machine ‘see’ and accurately identify where it is and what it’s looking at is a vital part of developing autonomous vehicles and robotics.

̽��ֱ��first system, called SegNet, can take an image of a street scene it hasn’t seen before and classify it, sorting objects into 12 different categories – such as roads, street signs, pedestrians, buildings and cyclists – in real time. It can deal with light, shadow and night-time environments, and currently labels more than 90% of pixels correctly. Previous systems using expensive laser or radar based sensors have not been able to reach this level of accuracy while operating in real time.

Users can visit the SegNet website and upload an image or search for any city or town in the world, and the system will label all the components of the road scene. ̽��ֱ��system has been successfully tested on both city roads and motorways.

For the driverless cars currently in development, radar and base sensors are expensive – in fact, they often cost more than the car itself. In contrast with expensive sensors, which recognise objects through a mixture of radar and LIDAR (a remote sensing technology), SegNet learns by example – it was ‘trained’ by an industrious group of Cambridge undergraduate students, who manually labelled every pixel in each of 5000 images, with each image taking about 30 minutes to complete. Once the labelling was finished, the researchers then took two days to ‘train’ the system before it was put into action.

“It’s remarkably good at recognising things in an image, because it’s had so much practice,” said Alex Kendall, a PhD student in the Department of Engineering. “However, there are a million knobs that we can turn to fine-tune the system so that it keeps getting better.”

SegNet was primarily trained in highway and urban environments, so it still has some learning to do for rural, snowy or desert environments – although it has performed well in initial tests for these environments.

̽��ֱ��system is not yet at the point where it can be used to control a car or truck, but it could be used as a warning system, similar to the anti-collision technologies currently available on some passenger cars.

“Vision is our most powerful sense and driverless cars will also need to see,” said Professor Roberto Cipolla, who led the research. “But teaching a machine to see is far more difficult than it sounds.”

As children, we learn to recognise objects through example – if we’re shown a toy car several times, we learn to recognise both that specific car and other similar cars as the same type of object. But with a machine, it’s not as simple as showing it a single car and then having it be able to recognise all different types of cars. Machines today learn under supervision: sometimes through thousands of labelled examples.

There are three key technological questions that must be answered to design autonomous vehicles: where am I, what’s around me and what do I do next. SegNet addresses the second question, while a separate but complementary system answers the first by using images to determine both precise location and orientation.

̽��ֱ��localisation system designed by Kendall and Cipolla runs on a similar architecture to SegNet, and is able to localise a user and determine their orientation from a single colour image in a busy urban scene. ̽��ֱ��system is far more accurate than GPS and works in places where GPS does not, such as indoors, in tunnels, or in cities where a reliable GPS signal is not available.

It has been tested along a kilometre-long stretch of King’s Parade in central Cambridge, and it is able to determine both location and orientation within a few metres and a few degrees, which is far more accurate than GPS – a vital consideration for driverless cars. Users can try out the system for themselves here.

̽��ֱ��localisation system uses the geometry of a scene to learn its precise location, and is able to determine, for example, whether it is looking at the east or west side of a building, even if the two sides appear identical.

“Work in the field of artificial intelligence and robotics has really taken off in the past few years,” said Kendall. “But what’s cool about our group is that we’ve developed technology that uses deep learning to determine where you are and what’s around you – this is the first time this has been done using deep learning.”

“In the short term, we’re more likely to see this sort of system on a domestic robot – such as a robotic vacuum cleaner, for instance,” said Cipolla. “It will take time before drivers can fully trust an autonomous car, but the more effective and accurate we can make these technologies, the closer we are to the widespread adoption of driverless cars and other types of autonomous robotics.”

̽��ֱ��researchers are presenting details of the two technologies at the International Conference on Computer Vision in Santiago, Chile.

Two technologies which use deep learning techniques to help machines to see and recognise their location and surroundings could be used for the development of driverless cars and autonomous robotics – and can be used on a regular camera or smartphone.

Vision is our most powerful sense and driverless cars will also need to see, but teaching a machine to see is far more difficult than it sounds.

Roberto Cipolla

Teaching machines to see

Alex Kendall

SegNet demonstration

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

Yes

Bullet holes and graphene caves: picturing engineering

sc604 — Tue, 10 Nov 2015 08:42:27 +0000

For many people, engineering conjures up images of bridges, tunnels and buildings. But the annual ̽��ֱ�� of Cambridge engineering photo competition shows that not only is engineering an incredibly diverse field, it’s a beautiful one too.

̽��ֱ��annual competition showcases the breadth of engineering research at the ̽��ֱ��, from objects at the nanoscale all the way to major infrastructure. ̽��ֱ��winning images can be viewed on the Engineering Department’s website from today, alongside dozens of other entries.

̽��ֱ��competition, sponsored by ZEISS, international leaders in the fields of optics and optoelectronics, had five categories this year; alongside those for first, second and third place, the ZEISS SEM prize was awarded for a micrograph captured using an electron microscope, and a Head of Department’s prize for the photo or video with the most innovative engineering story behind it.

First prize went to Rachel Garsed for her image of a bullet hole pattern in a liquid crystal, while second prize went to Andrew Payne for his image of a titanium ‘comet’. Other winners included Dilek Ozgit and Andrea De Luca’s image of carbon nanotubes, Kenichi Nakanishi’s image of cave-like formations made from graphene.

̽��ֱ��Head of Department’s prize went to Alex Kendall, for a video which demonstrate how a robot tourist would view Cambridge landmarks. Kendall’s system is able to take video or images from a smartphone and reconstruct what it saw in 3D, which can then be used so that a robot can learn both its position and orientation from an image.

̽��ֱ��panel of judges included Kenneth Png from ZEISS, and the Department of Engineering's Professor Roberto Cipolla, Dr Allan McRobie, Head of Department Professor David Cardwell, and Director of Research Philip Guildford. Guildford said that the judges were once again impressed by the quality of the images they received.

“I love the way in which the essence of engineering can be captured in a single beautiful image – these intriguing works of art convey wonderful stories of determined engineers battling to crack real world problems and finding the most elegant answers,” he said.

From a Cambridge guide for robot tourists, to titanium ‘comets’, the winners of the annual Department of Engineering photo competition highlight the variety and beauty of engineering.

I love the way in which the essence of engineering can be captured in a single beautiful image – these intriguing works of art convey wonderful stories of determined engineers battling to crack real world problems and finding the most elegant answers

Philip Guildford

Engineering photo competition 2015

Rachel Garsed

Shot in the dark’: the winning image from this year’s engineering photo competition.

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

Yes