̽��ֱ�� of Cambridge - Carl Rasmussen

̽��ֱ��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.

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