Digital Catapult selects start-ups to join Machine Intelligence Garage

Digital Catapult selects start-ups to join Machine Intelligence Garage

Digital Catapult, the not-for-profit body that focuses on helping UK businesses to scale up, has selected the first start-ups to join its Machine Intelligence Garage program.

The Machine Intelligence Garage Program at Digital Catapult aims to support the ambition of making the UK a global centre for artificial intelligence (AI) development – and several start-ups have just been signed up for the program.

The companies selected are digital manufacturing start-ups Intellisense and Predina; digital health start-ups Cambridge Bio-Augmentation Systems and GTN; and the natural language processing start-up Bloomsbury AI.

“Members of the first cohort have been carefully selected for their innovative use cases in AI, ethical values and ambition,” said Dr. Marko Balabanovic, the chief technology officer at Digital Catapult.

“We’re confident that their journey with the Machine Intelligence Garage will provide them with the support, expertise and compute resource they need to reach their full potential,” he added.

Read more: Digital Catapult opens doors on Machine Intelligence Garage

Early access to machine intelligence

Simon Knowles, CTO and co-founder of AI chip company Graphcore said the start-ups will get early access to his company’s Intelligence Processing Unit, which was been designed for machine intelligence.

“Its unique architecture means developers can run current mchine learning models orders of magnitude faster,” he said.

“More importantly, it lets AI researchers undertake entirely new types of work, not possible using current technologies, to drive the next great breakthroughs in general machine intelligence,” he added.

Azeem Azhar, senior advisor at professional services company Accenture, a partner of Digital Catapult, said the program would help “ to level the playing field” by giving start-ups in the UK access to some of the best resources and expertise.

“I’m excited to see where the programme’s first cohorts are by the end of the year,” he said.

Azeem Azhar of Accenture speaking at this week’s Machine Intelligence Garage announcement. (Credit: Digital Catapult)

Read more: Digital Catapult to launch three new networks in UK

High barriers to entry

The program aims to give small and medium-sized enterprises (SMEs) access to cloud-based and physical computational power for AI. According to Digital Catapult, deep-learning techniques incur extortionately high computational costs – a single training run for a ML system can cost upwards of £10,000. This can be a serious barrier for UK innovators and researchers.

Start-ups that are developing products or services that use ML or AI can apply to join the Machine Intelligence Garage program. The last Open Call went live on 23 January 2018, with applications to roll every six weeks thereafter.

Applications will be assessed based on a number of criteria including strength of idea, technical implementation plan, availability of data, ethical use of data, and the immediacy of the need for computation power.

Read more: Digital Catapult explains why it’s backing immersive reality

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Health service could be saved by robots with artificial emotional intelligence

My new resolution is to be less robophobic, says Nick Booth. There are good reasons to fear these electronic ‘jobsworths’. Robots write apps, books, songs and slaughter chess grandmasters. Worse still, some fiend has invented a prototype robotic reporter. I’m now competing with an ‘aggrievance’ (I believe that’s the collective noun for journalists) of freelance cyber-scribblers.

Still, not all robots are like that. Some of them might actually help us. For example, the UK’s National Health Service (NHS) could use some support at the moment. As I write, it’s been announced that 50,000 operations are going to be cancelled.

Believe me, it’s no fun queueing for an operation. For some reason, it takes two weeks for a surgeon to write a letter to your doctor and post it second class. (Why didn’t someone buy them a computer? You can get one for less than £11 billion (€12.54 billion) these days, if you’re prepared to shop around. And, if you avoid the traditional NHS suppliers, the computer might even work!).

But under the current system, you can wait three years after your bike crash for an operation. By the time the surgeons hack away the scar tissue on your arm, the nerve they are trying to un-trap is dead anyway. So, having operated on you too late, they send you on your way with the cheery advice to expect ‘atrophy’ in your arm muscles.

Artificial empathy

Having been the subject of that case particular study, I’m suddenly in favour of robot surgeons. Let’s hope they can learn artificial empathy.

Artificial intelligence (AI) was founded as an academic discipline in 1956. Its founding logic was that any human activity, if broken down precisely enough, could be done by a machine if we put enough thought into the instructions.

I can see why surgeons might not give patients much thought. The theatre work alone would put most mortals into early retirement. Then there’s the emotional toll of dealing with patients, the constant pressure to update skills and keep up with the waves of research. Not to mention the politics. Anything that relieves them of any of those burdens is a good thing, surely.

An artificially intelligent robot can scan through Gigabytes of data at lightning speed. Researchers at the North Carolina School of Medicine used IBM Watson’s AI engine to examine 1,000 cancer diagnoses. In 99% of the cases Watson came up with the same treatment plans as the oncologists in a fraction of the time.

Watson has a limitless capacity to digest complex information without tiring. Which is possibly why, in 30% of cases, Watson spotted treatment options that physicians missed.

Robot surgeon

In London’s Princess Grace Hospital, they use a robot surgeon called MAKO for knee and hip replacements.

Sixty-four year-old Bernice Glatt had a third of her knee replaced by MAKO under a procedure from which the patient recovers much quicker than they would from traditional surgery, the hospital says.

The MAKO robot allows surgeons to plan and execute surgery more precisely. With an increasingly aged and obese population, the number of hip replacements will rise accordingly, so anything that can surgically cut the queue will […]

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CARFIT And CEA Create A Joint Lab On Artificial Intelligence

CARFIT and CEA Partner

CARFIT and CEA have signed an agreement to create a joint laboratory focused on Artificial Intelligence related to car vibrations and their interpretation. The lab will bring together teams from the List, a CEA Tech Institute, and from CARFIT to share knowledge and expertise. The joint lab will be dedicated to the development of artificial intelligence methods for identifying signs of mechanical failures exposed by car vibrations.

CARFIT develops technological solutions to simplify mobility, by proposing real-time car monitoring to offer a smart maintenance system, adapted to the driver’s car use. The CARFIT team is comprised of automobile specialists, scientists, and artificial intelligence experts. CARFIT wants to further develop its predictive maintenance expertise by exploiting automobile vibration data analysis.

As a major research player at the national and international level, the CEA fulfills its industry competitiveness mission through CEA Tech, the CEA Technological Research Division. More specifically, the List institute carries out research on smart digital systems. The List’s teams already lead research projects on in-vehicle systems, interactive systems, and sensors and signal processing. As a Carnot institute (TN@UPSaclay), the List’s collaboration with CARFIT joins Carnauto spinneret action to strengthen competitiveness and attractiveness of the companies of the automotive domain by facilitating their access to the innovation.

The activities of CARFIT and CEA are thus complementary, enabling a fruitful collaboration on automobile predictive maintenance by vibration analysis. The two partners have therefore agreed to conduct a common R&D, leading to the design and development of optimized, innovative solutions of predictive maintenance for light-duty vehicles (authorized loaded weight not to exceed 3.5 tons).

“As an autotech startup, the creation of a joint lab with the CEA is a key milestone for CARFIT. This lab will extend our capabilities in Artificial Intelligence beyond what we could have done alone and opens up opportunities within the automotive ecosystem already in collaboration with the CEA.” says Nicolas OLIVIER – CEO of CARFIT.

“The List institute contributes to the automotive revolution through the autonomous and connected car and the development of digital services.” declares Philippe Watteau, Directeur de l’Institut List. “The Artificial Intelligence is at the heart of this revolution and our collaboration with CARFIT is going to open the way to the maintenance of tomorrow which will be predictive and as a service.”

This cooperation opens the way to the development of innovative architecture taking advantage of the deep learning to improve the diagnostic accuracy, to anticipate the failures and to maximize the mechanical defect coverage of vehicle components by vibration analysis.

Disclaimer: CARFIT is an alumnus of our ReadWrite Labs accelerator program. Kyle Ellicott is also an advisor to the company.

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How we’re pushing the frontiers with artificial intelligence

The international audience at the recent World Summit AI 2017 in Amsterdam, The Netherlands, reportedly topped out at 2,400. Not bad for an inaugural event, writes Jeremy Cowan, editorial director of IoT Now. And it was the goal of a panel of AI heavyhitters to inspire. Judging by the attentive audience, they succeeded

Amazon’s Ralf Herbrich joked that it might be quicker to say where artificial intelligence (AI) is not impacting Amazon’s business. He cited current uses of AI in areas as diverse as demand pricing, fresh fruit ripeness prediction, and in Alexa.

Rob High chipped in saying that IBM Watson uses AI to help doctors identify the right time for oncology (cancer care) treatments, and “we allow other people to take AI into other areas such as conversational systems” or deeper insights into retail customer behaviour.

If those are some of the areas in which AI is working now, said the moderator, Prof. Max Welling, what could AI do better?

We need more accuracy, less energy – Herbrich believed we can use AI to play games with humans but that’s not presently a good use of computing power. A video game would involve a human burning energy at the rate of 2,000 calories per day, while it would take 220 million calories for AI computing to do the same job. “So we have to reduce energy,” he said. “We need more accuracy per calorie, which means compressing neural networks.”

If that’s so where are the bottlenecks in AI, the moderator wondered.

Tencent’s Zhang Tong pointed to dialogue systems, “as an industry we’re not doing very well in natural language or robotics, how we take actions with AI. There’s room for improvement.”

As to where AI and machine learning (ML) can best help society, High felt, “cognitive computing and AI should be there to amplify our thinking. The danger isn’t simply that it might rise up and take over our role. There are things we do well, but there are a lot of things we don’t do well. AI can extend our reach, focusing on what gives us the biggest opportunity to enhance ourselves. We need to see through our biases to allow us to make better decisions.”

Asked if humans and AI are co-evolving, High said, “Yes, it’s a complement to human intelligence, (AI) will co-reside with us.”

AI as a team sport – Marco Vernocchi, senior managing director, Applied Intelligence lead at Accenture commented, “AI is a team sport. No single organisational unit or discipline can make it a success. Also, it’s an ecosystem play.”

Herbrich liked the team sport description. He went further saying, “For many years the academic community lacked the ability to know what AI’s applications will be. It was an unguided field but in AI industry and academia meet well.”

Prof. Welling was concerned to know if the right skills were available for AI to develop. Aren’t people in academia (with AI skills) being acquired by industry?

Zhang Tong was quick to scotch these concerns. “I was a professor at Rutherford University before joining Tencent. I see it as […]

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Case Study: ‘Intelligence on tap’, inside Veolia Water’s smart grid for water services deployment

Inside Veolia Water’s utilisation of Actility’s ThingPark Energy to power a smart grid for water services

When someone talks about a smart grid, the first thing you’ll think of is electricity; if they talk about industrial IoT you’ll probably be imagining factory monitoring and automation. But in this case study, we’ll be taking a look at how combining sensing and remote control throughout a network with data analytics, process modelling and forecasting can make a giant of the water industry more efficient and open up new business opportunities. Welcome to Veolia’s Smart Water Network.

Veolia Water is a world’s largest supplier of water services, originally based in France but now operating on a large scale across Europe, in the Americas, the Middle East, Africa and APAC. The company provides managed fresh water and waste water services for municipal and industrial clients, handling every step in the water cycle, from capture to treatment, distribution, waste water recovery and processing and release back into the environment.

Energy consumption is involved throughout this cycle, pumping water through the treatment facilities and the pipes connecting them, to homes and then back through the waste water processing. Buying electricity is a significant cost for Veolia. More than 80% of that electricity is used for pumping and aeration. Typically, pumping stations buy electricity at fixed rates, with little price difference between peak and off-peak hours. However, prices in the wider electricity market vary much more significantly.

This creates opportunities for consumers like Veolia, with the flexibility to schedule their operations to an alternative timetable and react swiftly, to create additional business value from its extensive storage capacity – water tanks – and large number of electrical devices such as pumps, valves and aerators, which can be activated and shut down very quickly.

Encouraged by an increase in water production and tougher environmental challenges Veolia selected Actility’s ThingPark Energy platform to improve the efficiency of its pumping stations and treatment plants. Actility brings its tried and tested real-time optimisation algorithms to bear on modelling the complex water process. Realtime planning and scheduling is driven by the demands on the water network at any given time combined with live data on electricity pricing from the supplier market. Hundreds of electricity consuming devices are remotely controlled, taking into account fluctuating energy prices. The final operational plan is scheduled to minimise the overall cost of electricity whilst ensuring that operational demands on the network are always met.

Being flexible pays dividends Actility’s ThingPark Energy Platform makes use of high performance data analytics tools originally developed for demand-response tools to secure electricity supply grids against unusual peaks in energy usage. The predictive modelling capability enables the system to respond automatically with the best solution when faced with unplanned situations. The system learns and the operational plan is updated automatically, whilst continuing to respect the constraints of the underlying processes. The automated system provides significant levels of support to on call-operation teams, reducing workload and supporting good decision making. The same prediction capabilities, combining with operational monitoring of systems […]

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