a tool to complement humans, - cambridge consultants · fuels. taxes on petrol, diesel, natural gas...
TRANSCRIPT
INTERFACE
63
“ A tool to complement humans, not to substitute them”
03 A GENERATION OF ENERGY
05 CIRCULAR THINKING
07 BRIGHT IDEA? RIGHT IDEA?
09 TECHNOLOGY SANS FRONTIÈRES
11 FROM BENCH TO MARKET
13 YOU’LL PAY WHAT THE AI THINKS YOU SHOULD PAY
15 MADE TO BE MADE BY ROBOTS
16 YOU TALKIN’ TO ME?
17 BUILD THE RIGHT THING AND BUILD THE THING RIGHT
19 A VISION FOR A DIGITAL POLICE SERVICE
21 HOW LOW CAN WE GO?
23 SCALING UP CELL THERAPIES
25 A LONG DISTANCE RELATIONSHIP
27 MACHINE LEARNING WILL HELP
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WELCOME TO ISSUE 63 OF INTERFACEI took over as Chief Executive Officer of Cambridge Consultants on October 1st of this year, only the 6th CEO over the last 47 years. At such a milestone I felt it would be a good time to reflect on why you, our clients, put your trust in us to deliver breakthrough innovation.
Cambridge Consultants works hard to make itself the partner of choice when real breakthroughs are required. We only work on novel products, usually of considerable complexity and always required against demanding timescales. To that end we have a highly motivated team of software developers, RF engineers, mathematicians, physicists, mechanical engineers, Human Factors specialists and much more, all working closely with you. It is also why we have invested heavily in state of the art equipment, tools and facilities that mean our engineers have everything to hand to make the breakthroughs required at the quality standards you have come to expect.
Over the last few years Cambridge Consultants has taken on new offices such that we can offer seamless development capability in the most exciting technology hubs in the world. We cover 16 time zones, meaning we are relevant to clients with significant international operations. There is always more to do of course, but with hundreds of scientific, engineering and design problems tackled – and solved – each year, these are the points of difference that mean you trust us to deliver world-leading product development engineering and technology consulting.
I’m hugely proud to be leading this team into the future.
Eric Wilkinson, CEO – Autumn 2017
ENTERPRISE IN INTERNATIONAL TRADEWe have recently been awarded a Queen’s Award for Enterprise in International Trade. The accolade is in recognition of the success of our ambitious growth strategy, in which we have seen our overseas sales grow by 74 per cent since 2012. Overseas trade now accounts for around two-thirds of our business.
The prestigious award was presented by HM Lord Lieutenant of Cambridgeshire, Mrs Julie Spence and Deputy Lord Lieutenant James Buxton. It was accepted by Gemma Holbrow, Chair of the Staff Council, on behalf of our employees.
This is the third time we have won a Queen’s Award for Enterprise – and it is our second success in the International Trade category, which we first won in 2009. We have also won an Innovation award in 2011 for our ground-breaking through-wall radar technology Prism 200.
For more than a decade, we have grown every year – both in the UK and across key international markets – and we now have a global workforce of 750. In the last three years alone, we have doubled the scale of our US East Coast presence in Boston; acquired our 140-strong Synapse product development business on the West Coast of the US; and opened sales offices in Singapore and Tokyo; as well as growing our UK facilities.
We recruited 100 people last year and plan to recruit a further 100 or more employees by the end of 2017.
NEWS
TURNING OUR SKETCHES INTO ART WITH MACHINE LEARNINGOur machine learning team has developed “Vincent”, a world-first technology that is capable of completing a drawing that has been started with a human sketch. Completed ‘works of art’ combine a user’s sketch with the digested sum of art since the renaissance, as if Van Gogh, Cézanne and Picasso were inside the machine, producing art to order.
Never before has artificial intelligence (AI) had the ability to interpret what a human is drawing and then complete the piece for them. Beyond simple machine-generated art, Vincent is an engaging, interactive system in which the output is guided and influenced by the user.
Based on multiple Generative Adversarial Networks (GANs), Vincent was shown thousands of paintings to build an understanding of where contrast, colour and texture change. Now trained, Vincent is able to interpret important edges in paintings and uses this understanding to produce a complete picture.
A user simply draws directly on a tablet and Vincent will interpret the different lines being drawn, picking up where the user left off – taking as much information as it’s given to build the piece into a completed picture.
Potential applications for Vincent-like technology include autonomous vehicles and digital security. Here, the technology could be used to generate training scenarios and simulations, introducing almost limitless variation and convincing detail beyond what humans could efficiently produce.
Vincent is the latest output from Cambridge Consultants’ Digital Greenhouse – a unique experimental environment where data scientists and engineers explore and develop cutting edge machine learning techniques.
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A GENERATION OF ENERGY
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04 The year is 2040 and life is good, but my generation still reminisces about the golden age of fossil fuels. Only twenty years ago, everyone could afford to enjoy the sound, experience and convenience of an internal combustion engine car. We could heat our homes as much as we wanted or take long, hot baths courtesy of our cheap and powerful gas fi red boilers. We could eat fresh meat every day without breaking the bank and take our pick of a seemingly endless range of luxury products in the shops. I think everyone knew that couldn’t go on for ever.
It was just after 2017 when the symptoms of pollution and climate change started to cause serious environmental damage and forced the world to rally together to all but phase out fossil fuels. Taxes on petrol, diesel, natural gas and coal rose rapidly, causing a move towards electric vehicles and well-insulated smart homes. Initially it felt like we were making great progress but we were soon faced with the massive challenge of replacing the 80% of energy we used to consume from fossil fuels, with clean energy in the space of just one generation.
Getting through the “Energy Crunch” was not easy, but by investing the additional tax revenue in cost-effective low-carbon energy production, smarter transmission and distribution systems and promoting social responsibility we fi nally turned a corner on climate change.
ENERGY RACEAs the cost of energy production became more independent of the price of fossil fuels, the world’s leading economies quickly realised that whoever could produce the cheapest energy would be the most competitive. Developed countries used the fossil fuel tax revenue to provide grants and prizes for advances in low-cost, low-carbon energy generation technology; mobilising universities, start-ups and blue chips to set their brightest minds to the task. Developing countries, less entrenched in fossil fuel consumption, were able to move to renewables faster than developed countries, levelling the playing fi eld. As a result, the last 20 years have seen the effi ciency of wind, solar and other technologies leap closer to their theoretical limit, dropping the price per kWh signifi cantly.
SMART GRIDTransmission and distribution (T&D) grids were completely overhauled to adapt to the increase in demand for electricity and to the changing nature of energy production and consumption. The fraction of energy produced by periodic sources such as solar, wind and tidal has increased, as has the ownership of “fast-charge” electric cars, trucks and planes. To buffer this increasingly erratic supply/demand, high capacity, decentralised energy storage such as fl ow batteries, molten salt and pumped heat were developed and installed into the grid and homes. The demand, storage and distribution of energy are now managed by an autonomous AI system that can predict variations in generation from the weather and has creepily learned and infl uenced our consumption habits.
ATTITUDE SHIFT Since the environmental revelation back in 2017, society’s attitude to energy has progressively changed; energy is more precious now, it isn’t taken for granted or wasted like it was when I was younger. Things that we used to be nagged about, like leaving the bedroom light on or leaving the front door open, are now taboo (and costly). Although my generation might be partly responsible for the consequences of the “good old days”, I think we can be proud of what we have achieved in the last 20 years. I wonder what my children will reminisce about in 2060?
CIRCULAR THINKING
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Circular economy thinking is transforming businesses under pressure from consumers and environmental regulators as well as suffering increasing competition for raw materials. The circular economy model aspires to use resources to their maximum value, keeping them within the economy indefinitely, preferably as whole products rather than materials, and aiming to ‘design out’ waste from the system. In practice this means products which last longer, can be repaired, upgraded, remanufactured or recycled in combination with accompanying new business models. In simple terms the circular economy is exploring how to make more money whilst selling less stuff (or less new stuff!), an ambition which in principle should deliver both commercial and environmental benefits.
Circular economy proponents are quick to emphasise the importance of business model innovation in achieving their objectives. Undoubtedly new business models such as ‘access over ownership’, servitization and virtualisation are important. But the role that technology will play in delivering a circular economy is often underplayed. Here I consider five technologies that will deliver a circular economy.
01 SYNTHETIC BIOLOGY Synthetic biology is a new approach to design where rational and systematic engineering methods are applied to biological systems. Because nature is the ultimate example of efficient use of materials, this emerging field offers vast potential to contribute to a circular economy from process to product. For example, chemical processes using rare metal catalysts and organic solvents, often at high temperature or pressure, can be replaced with enzymatic processes carried out in aqueous solution and ambient temperature. Where heavy metals are still required, microbes can be used to sequester these metals from industrial waste – bringing the metal back into the supply chain. Advances in chemical and biological processes are also unlocking cost-effective conversion of biomass into platform chemicals that feed into existing supply chains to produce fuels, packaging, textiles and speciality chemicals. One interesting bio-derived polymer, PEF, is suitable for food and beverage packaging as well as for fibres for carpets and textiles. For the packaging industry, PEF offers better characteristics in comparison to conventional plastics, such as improved barrier properties for gases like carbon dioxide and oxygen, leading to a longer shelf life for packaged products.
02 INTERNET OF THINGS Many circular economy approaches involve asking how an asset could be used more intensively, which involves understanding asset location, asset condition and asset availability. An average car is only in use around 5% of the time. Conversely, car clubs, such as ZipCar, are able to achieve much greater utilisation rates by using location data to track asset location and availability and match supply and demand. Internet of things solutions have even been applied to the most humble of products: the bin. Sensors detect when municipal bins are nearly full and automatically schedule a collection. Routing algorithms can be used to optimise collection vehicle routes. Vehicle movements
are significantly reduced, saving fuel, labour costs and reducing the total number of refuse trucks required.
03 ADDITIVE MANUFACTURE3D printing will have an impact both on manufacturing processes, where it will drive resource efficiency improvements, and the repair market, where printing provides quick and easy access to parts which are rare or even obsolete. For example Boeing is looking to trim costs with the first ever 3D-printed plane structures and Mercedes-Benz Trucks already use 3D printing processes for plastic spare parts, including spring caps, air and cable ducts, clamps, mountings and control elements.
04 NEW REMANUFACTURING TECHNOLOGIESRemanufacturing is defined as returning a product to as good as new condition with a warranty to match. Remanufacturing, particularly of large complex engineered products, is nothing new: Renault have been remanufacturing near Paris since 1949: 25,370 engines, 15,930 gearboxes and 11,760 injection pumps have been reconditioned and given a second life. Modern advances in technology include automated disassembly using robotics, new surface reconditioning technologies such as high velocity arc spraying and nano-brush-plating technology, and novel methods of non-destructive testing such as ultrasonic testing, metal magnetic memory testing and eddy current testing. Remanufacturing is evolving from a niche, labour-intensive activity to a mainstream high-tech industry. Today, increasingly lower value products such as shopping trolleys and office furniture are remanufactured at significant scale.
05 MARKER TECHNOLOGIESSorting increasingly complex, constantly evolving waste streams in order to reprocess materials and capture value remains challenging. Plastic packaging plays a vital role in protecting and prolonging the life of products, but the complex range of polymers, often laminated together, presents significant technical challenges to recyclers. Novel marking approaches to help detect different materials are under development. Machine-readable fluorescent inks can be used to distinguish between food contact and non-food contact plastic. Digital watermarks are patterns that can be applied in label or packaging design, or directly to the polymer surface; having minimal visual impact, created at very low cost and which can be detected by a camera.
It is clear that technology will be a fundamental circular economy enabler and that should not come as a surprise; an ambitious vision for a radically new model of production and consumption could not be realised without innovation. The Circular economy presents a clear opportunity for strategic businesses looking to decouple their growth from reliance on non-renewable resources. Unlocking this opportunity will require high-tech solutions across the entire product lifecycle from product design, production, tracking and tracing in use to end-of-life management.
BRIGHT IDEA? RIGHT IDEA?
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08 Bright ideas and discoveries are important in the success of innovative companies. However, not every innovation will be a success. So how do we calculate ahead of time whether an idea is worth taking forward?
It seems to go without saying that if you don’t ask the right questions of the right people, at the right time, you won’t get the right answers. And yet, often ideas are not considered in a broad enough or consistent enough manner, and assumptions are not validated. It’s easy to get caught up in ‘the wow’ of innovating, without thinking about ‘the how’ of successfully bringing an idea into reality.
Tough questions should be asked of an idea from the outset, and time should be allowed in the innovation funnel for this, to prevent wasting effort down the line. It is often useful to assess multiple ideas against the same criteria for a comparative review. There are inevitably constraints on time, budget and resource and the team needs to focus on making just the most appropriate ideas a reality. Our decision making framework is predicated on considering the three key aspects of: technology, market and business. We ask the following:
IS IT TECHNICALLY FEASIBLE?At an early stage we’re focused on solving a problem or addressing a customer need, not the specific technical solution, and so it may be hard to judge feasibility. However, based on our experience of bringing technology to market, we ask ourselves question such as, have we done something similar before? How mature is the technology? What risks remain? And, is there the expertise to deliver?
IS IT DESIRABLE TO THE CUSTOMER OR CONSUMER?By focusing on the need rather than the specific solution, we can establish whether someone will want an innovation, either now or in the future. An understanding of consumer and industry trends overlaid with evolving technologies helps us to identify whether something is a tech push with no market pull, or just an idea ahead of its time - Google Glass anyone?
IS IT COMMERCIALLY VIABLE?This comes down to whether someone will pay – is there a market for it? The commercial success of an idea hangs on how it could be adopted and its associated business model. This should all be overlaid with consideration of whether it is a fit with that company. Sometimes business transformation is required to support an innovation. This can be achieved by partnering or creating a spinout or may require innovative thinking within the business structure.
If the answer is ‘no’ or ‘not really’ to any of these questions, all is not lost! By considering alternative applications, the development journey for that initial idea can pivot and continue to a more attractive destination.
Finally, innovations should always be assessed by people who understand what it takes to deliver a successful product or service, and what may go wrong along the way. A multidisciplinary team avoids potential prejudices from, for example, a technologist wedded to a single technology, or a marketer motivated to address a particular market. Together the team can look beyond ‘the wow’ and identify the best ‘how’.
TECHNOLOGY SANS FRONTIÈRESHorizontal Innovation – technology transfer between industries – is often discussed in the technical
press, but in the oil & gas industry it has been largely around the transfer of technology into the sector. In the current ‘lower for longer’ climate, savvy technology providers are beginning to diversify
into alternative markets to grow revenues and reduce dependence on a single sector.
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Many industries face challenges similar to those already overcome in the oil & gas world, albeit on a different scale or with different economics. For example, let’s compare the challenges faced by the oil & gas and medical industries.
On first sight there would appear to be little common ground between these worlds. The medical world is perceived as needing extremely high precision; use of high-tech drugs in a surgically clean environment, whilst oil & gas conjures up images of enormous oil rigs, drilling hardware, refineries and technicians wearing oily overalls. However, look ‘beneath the skin’ and there are striking similarities. Both industries require careful measurement, monitoring and control of flows of complex fluids as well as high levels of automation; both must operate with the highest levels of quality, reliability and safety. Consider these examples:
Seismic surveys provide vital information when exploring for new hydrocarbon resources. This is perhaps the most obvious technology overlap with the medical sector. Acoustic signals are launched into the ground and the signals reflected from interfaces between different rock strata and oil/gas reservoirs are detected by sensors on the surface. Complex analysis of the return signals and their arrival time allows engineers to extract valuable information on the geology and likely presence and location of gases or fluids.
The medical ultrasound machine transmits high-frequency sound waves into the body using a transducer probe which both produces and receives sound waves. The sound waves travel into the body and encounter the boundaries between different tissue types, including those between bone, soft tissue and fluids. The probe receives the reflected sound waves, and the machine carries out complex analysis on the probe distance from the tissue or organ using the time of the return of the echo. This analysis enables the machine to display the distances and intensities as a 2D image. This image has a high value to clinical practice, from determining the size of a foetus to looking inside the heart to understand how well it is functioning.
There are clearly a number of areas where technology could be shared to provide future innovation. For instance, both seismic analysis and ultrasound imaging need image data processing to create 3D models, requiring pattern recognition and complex mathematical operations such as reducing image noise to create cleaner more effective 3D images. Another example is the data transforms which are used to interpret geophysical features; these may have relevance in interpreting occlusions in coronary arteries or finer detail within underlying organ structures.
Multiphase flow metering. All wells produce a mix of oil, gas and water. In order to control a well and ensure profitable operation, operators need real-time monitoring of the composition of produced fluids. The oil & gas industry has developed a number of elegant solutions using a combination of venturi flowmeters, gamma ray spectroscopy and acoustic measurements.
In the medical domain, there may be multiple applications for
techniques which can monitor multiphase flows. Characterising fluids from a patient during surgical procedures is important; for example, improved methods for characterising blood loss could lead to improved surgical outcomes. In extracorporeal treatment of blood, such as auto transfusion and haemodialysis, it could be useful to use such technologies to improve the monitoring of blood flow. Multiphase measurements of blood within vessels could lead to more information about vessel health and facilitate important conclusions about cardiac function.
Reliability Engineering is crucial in oilfields mainly because the cost of reinstalling the equipment is many times that of the equipment itself. Techniques such as FMECA (Failure Mode Effect & Criticality Analysis) and FTA (Fault Tree Analysis) are used to eliminate potential failure modes at the design stage, and subsequent testing of prototypes is used to verify performance. Oilfield equipment is often designed for more than 25 years’ continuous lifetime in a harsh environment, operating several kilometres underground, at over 150°C, with pressures of 15-20 kpsi.
In the medical industry lives depend on the reliability of the hardware. Devices need to operate without significant monitoring, especially implantable devices which would need to be replaced urgently if found to be failing. Although implants are used in less harsh environments, they need to be chemically inert and endure long-term use in the body – mechanical components, electronics and software must work without intervention. Medical devices are becoming more complicated, incorporating more automation, wireless components and smart algorithms.
Over the last fifteen years the US FDA has reported an increase in the number of serious patient adverse events – and this is driving the medical device industry to ensure that reliability and quality are key considerations during design, manufacturing and marketing. The tried-and-tested methods in reliability engineering in the oil and gas industry could deliver considerable value to medical devices. Commercially, a reputation for best-in-class reliability is incalculably important to a medical devices company, while a reputation for poor reliability, where patients’ health is potentially put at risk, could be fatal to business. Disruptive innovations in this area for a medical device company could deliver a strong competitive advantage.
We believe that many technologies and skills developed in the oil & gas industry could find applications in the medical world. It is often said that very few inventions are completely new; it is merely the application of an idea from one world in another.
There are clearly significant opportunities and potential for companies in the oil sector to look deeply at their technologies and to diversify into other industries providing much needed innovations. Perhaps now is the time for leaders in the oil & gas industry to look over the horizon and see what other exciting business opportunities are out there.
[email protected]@CambridgeConsultants.com
Point-of-Care (POC) diagnostics has the potential to revolutionise how healthcare is delivered and therefore could directly impact the quality of life for patients. For example blood glucose monitors, as used by diabetics at home around the world to manage their condition.
Even if POC devices comprise simple detection methods, there is a requirement that they are easy to use, they are as reliable as lab-based tests and are cost-effective. These requirements are not simple to achieve; they dictate long development time, and require a broad range of knowledge and expertise. The skill requirements change as the product moves from early stage proof-of-concept through development into manufacture and eventually sale. Each stage in the evolution brings with it different challenges and opportunities for success. Therein lies the challenge for any company wanting to develop a POC device. This is a particular challenge for start-ups.
The initial proof-of-concept phase is typically resource and finance constrained and is focused on demonstrating that the core technology works, but this is a critical stage in the evolution of a diagnostics product. A critical part of the development of any novel device or technology is how risk is identified, evaluated and, where possible, managed. Decisions at this early stage can have dramatic effects on the commercial viability of the final product. Therefore a balance between features, risk, regulations and complexity needs to be the core focus.
The product development stage requires the widest range of skills and expertise for the combined skills of system design, electronics, mechanical engineering, human factors, software, regulatory and marketing to name a few. Here companies need to balance the high cost of development, which must meet the needs of the regulators, users, purchasers and payers.
The transfer of the product to manufacture can be affected by decisions made in the earlier design phase. For example the selection of materials that cannot be joined or are expensive or need unusual treatments all add cost and slow down the manufacturing process. This has a direct impact on the cost of manufacturing goods and therefore the price at which they have to be sold. Going back and designing out these costs can be expensive and can lead to significant delays in the launch of the product. Factoring these into the initial design saves time, money and reduces commercial risk.
By the nature of how they are expected to be used, POC devices must be relatively low cost, easy to use, robust and most of all, for the company developing them, profitable. These can seem like opposing objectives, particularly in the early commercial phases where low volume manufacture can make the products expensive.
Outsourcing product design to an experienced product design consultancy, can be an effective risk management approach, providing access to a broad range of experienced engineers and scientists who can be applied to the various problems at the right time. For example, working with companies to develop their technology to a point where it requires further funding, we are able to engage with potential investors who appreciate the benefits we bring, both to the company looking for funding as well as the company looking to invest.
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FROM BENCH TO MARKET
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For thousands of years, buying goods involved haggling with a stall-keeper in a bazaar over the price that you should pay. The most successful stall-keepers were those that could deduce your willingness to pay and the value you placed on their goods. Indeed, this approach is still used in many parts of the world and in developed economies for some purchases, such as buying a used car. In 1861 a Philadelphia shopkeeper began a revolution in the retail industry. John Wannamaker introduced price tags in his store, thereby offering the same price to all his customers. This transparency gained the trust of his customers and changed the way we shop for goods.
We do still put up with price variation: supermarkets reduce perishable goods as they near their use-by date and the price of a plane ticket fluctuates as the day of departure nears. It’s easier for on-line retailers to adjust prices than their bricks-and-mortar competitors: Amazon updates its price list on average every 10 minutes, which would be impractical for physical stores.
The increasing availability of personalised data, and algorithms to process it, are enabling businesses to use dynamic pricing to greater effect. Coca-Cola recently experimented with vending machines that change price according to how hot the weather is. An analytics company that uses machine learning to automatically adjust the price of fuel for motorists through the day claims that its use increases retailers’ margins by around 5%.
Is it inevitable that emerging machine learning technologies will give rise to new pricing strategies? Are consumers ready? Imagine a shop where the price tags are automatically personalised to you as you walk around the store, based on your personal data held by the store, combined with public feeds from your social media posts. It would feel unfair and intrusive. Nobody would be happy about the person behind them in the queue paying less for the same item - the loss of trust could be damaging for the shop. Yet these possibilities are already being exploited by online retailers.
AI is finding applications in many sensitive areas of our lives, and yes, pricing algorithms is one of those areas. It will open new opportunities to organisations and strengthen business models which otherwise would not be viable. The trend of ‘everything-as-a-service’ and deep personalisation are gathering momentum, meaning that many products and services are no longer commodities. Offering a fixed one- price-suits-all model may not be as appropriate in some markets as it once was. As consumers get used to these variable pricing models we will accelerate even faster towards everything-as-a-service. Uber has been experimenting with ‘surge pricing’ for many years, occasionally with notorious results, but as one industry pioneers this approach, others are likely to follow.
I predict that we’re seeing the end of the one-price-suits-all approach to pricing that has dominated for over 150 years. Instead there will be increasing use of sophisticated machine learning algorithms, meticulously matching the price to your perception of value and your personal willingness to pay. Get ready.
YOU’LL PAY WHAT THE AI THINKS YOU SHOULD PAY
Investment in factory automation has skyrocketed due to ever-increasing labour rates and a shortage of workers. Annual spending on robot procurement in China is now in excess of $3B. Additionally, industrial robots are the highest growth-rate product made in China.
MADE TO BE MADE BY ROBOTS
What this means to the factory is that smart, flexible workers are replaced with less flexible, dumb devices. Humans adapt readily to change and have a fantastic set of senses including stereo vision, sophisticated touch, and hearing. Sensors on automation equipment may be limited, constraining adaptability. The result is that slight variations in components and materials can disrupt machines performing assembly. So it’s important that products are designed differently if they are to be efficiently assembled automatically.
Factory process constraints become an additional input to the product design process. The details of each assembly step need to be captured and key areas identified, such as the type of automation, the machine capabilities, and whether vision assist will be used. Design considerations include adding tapered lead-in features to guide parts in place, integrating electronic components onto the minimal number of circuit boards, minimising the number of fasteners, optimizing parts for symmetry or asymmetry, and determining how cosmetic parts will be safely grasped.
Is the design-for-automation effort worth the investment? Yes! Keep in mind that the amount of benefit scales with the production volume and other factors; the result will be higher factory throughput and fewer defects. An additional benefit of an integrated design is higher field reliability. Finally, if a product is designed for automated assembly it is highly likely to be easier for humans to build too.
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VUI designed products must listen to the user and mimic how we humans can tune into a single voice. Understanding this dialogue experience is vitally important, as it informs the physical product design in relation to audio source separation, intelligent signal processing, microphone placement and more. If noise suppression is not designed to immediately detect directed user dialogue, it is likely the entire experience design – the emotional part - will fail. Similarly, the VUI’s voice and ‘character’ are important when responding to the user’s tone of voice. It mustn’t feel like they are talking to a machine.
The VUI design challenge focuses on what the user is saying, not what they are touching. My experience to date suggests VUI completely upends an old design rule of thumb. Instead of 80% design and 20% experimentation, with VUI we are seeing 20% design and a whopping 80% of time spent on experimentation.
Cloud computing is now dominant. Users today retain their data when they upgrade their product to the latest device. This must be true of VUI technology too, preserving data and learning from numerous conversations. Whilst the physical ‘thing’ may remain locked in conventional product lifecycles, the VUI has loftier ambitions and stays with us, building a relationship over time.
You talkin’ to me?
Alexa and Siri blaze the trail for voice user interface (VUI) design: tech we talk to. But in these early days of VUI, now is the time to ask: how
should we design a new generation of digital services and physical devices that know us as well as our mother?
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BUILD THE RIGHT THING AND BUILD THE THING RIGHTWith budgets and schedules becoming ever tighter, building the correct product the first time is more important than ever. Over-engineering can be slow and costly whereas under-engineering can leave end users disappointed and return rates skyrocketing. At Synapse, we rely on a few key principles to deliver great products.
WHO - KNOW YOUR USERS AND WHAT THEY WANTA clear and complete understanding of users’ needs can drive these decisions towards a successful product. Early prototyping identifies what users want and, perhaps more importantly, what they don’t want or care about. Let careful listening guide your vision towards a successful product.
WHAT AND WHY - UNDERSTAND WHAT YOU WANT TO DELIVER AND WHYOne of the hardest parts of product development is finding the right mix of features, materials, and price point to suit both your users and your business model. A beautiful product that your users can’t afford is no better than a product riddled with reliability issues. Spend time upfront to define your product goals and keep them updated throughout the program.
WHEN - HURRY BUT DON’T RUSHMany companies apply pressure to rush the product development process so they can be the first to deliver novel technologies and capture the largest market share. However, rushed designs lead to errors, quality issues, and time lost to corrections. While counterintuitive, the fastest products to market are those that pause to fix issues before scaling. The only timeline of interest should be the one to build a product correctly.
HOW - TEST, DESIGN, REPEATOnce you know what you want to deliver, you need to figure out how to make it. Work to understand the components, assemblies and the product as a whole and how they suit your needs.
Remember, quality can’t be tested into reality so plan time and resources to iterate. While suppliers can make promises about their goods, your product holds risk in the interfaces between its “guaranteed” parts. Focus on understanding your product and the risks within it.
WHERE - SELECT GREAT MANUFACTURING PARTNERSManufacturers turn your design into your product. They’re just as important as your design and have a large role in whether it shines or falls short. When our New Product Introduction (NPI) engineers look for manufacturing partners, we think about relationships and the goals of our client.
There is no universal ‘best’ partner. Each product company can be looking for a different blend of speed, quality, cost and risk profile. It’s very important to not over-value capabilities; don’t pay for capabilities you don’t need. Selecting the right capabilities is often relatively easy, but there are other critical factors such as: whether the manufacturer actually wants to make your product.
Like any relationship, you need to invest time and effort in order to get the most out of the partnership. You can’t assume that the manufacturer will understand what is most important to your project. We believe that we get the best from manufacturers by building this positive relationship, particularly in projects that have very aggressive goals. You’ll be spending a lot of time working with your manufacturing partner, so invest in the relationship to make it successful and fun!
A VISION FOR A DIGITAL POLICE SERVICE
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The UK’s Home Office is pioneering the use of 4G technology to replace the emergency services’ radios. The Emergency Services Network will be the world’s first to use public 4G networks to deliver group voice calls for tactical operations. In doing so, frontline personnel will receive always-on broadband connectivity. In the USA, a project called FirstNet will deliver the same 4G always-on broadband digital access.
Our emergency services must do substantial “office work”: form filling, data collection and more. The primary purposes of this work are to save lives, protect the public, protect property and maintain the peace. But I’ve never met anyone that joined the emergency services to perform “office work”. So how might we be more ambitious? How might we use the latest digital services to increase the productivity of the “real” work of the emergency services, and not just the office work?
A 2011 report from the National Policing Improvement Agency suggested that administrative work takes up 54% of an average police shift. Capturing accurate records is essential both for team working and to achieve an outcome in any legal process, but much of this today is simply office work, which standard IT tools make quicker. Portable office tools have moved this office work outside, improving teamwork and saving time moving between the station (office) and the incident, but these typically put an even greater typing burden on the police officer – and often through poorer mobile interfaces. Surely we can consider a more ambitious use of digital technology?
What if every officer was joined on patrol by an assistant: someone that could augment their capabilities, guiding their actions, maintaining records, providing expert legal or process advice and supporting the activities of the officer? And what if that assistant was omnipresent as well as intelligent, omniscient as well as tactical; someone on hand all the time and able to access and interpret intelligence records instantly? That assistant no longer needs to be human. This approach recognises the fundamental need for the emergency services to work “eyes up and hands free”.
We may soon see the early signs of such an approach. Motorola has recently trialled a move towards a “virtual partner” – a combination of AI and natural language processing, which would allow users to speak requests for information or give verbal commands without going through a human operator.
But, we believe that it’s a more ambitious digital service that operational police officers need; a new version of a personal assistant. It might sound like Alexa, Siri or Google Now, but it would be a world apart – a truly valuable tool able to untether every officer from office work and deliver support which is truly revolutionary and not just better, faster and more cost-effective.
True innovation in digital services is a different way of working – not simply tools to do the same work faster. Making the “office work” of a police officer disappear would have a major effect on their productivity – human augmentation for the benefit of us all.
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HOW LOW
CAN WE GO?
The Internet of Things (IoT) promises to revolutionize computing once more, by connecting and harvesting data from everything. Think of IoT as adding a digital life to all those objects around us as we move through the world.
Expectations are high. Gartner is forecasting that there will be 20 billion IoT devices by 2020, but there are mountains to be moved before such lofty predictions can be realised. Even a few cents is too much to add connectivity to low-value ‘things’. I’ve come to believe that we must reduce the cost of connectivity, far below today’s levels, in order to enable IoT to grow exponentially. This will, in turn, require radical simplicity.
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We’ve been working to envision a new category of ultra-low-cost connected devices – costing a handful of cents − that are the result of today’s technological advances and those we can see just around the corner.
A ruthless optimisation aimed at stripping out cost leaves us with a minimum viable IoT device that requires just power, connectivity, sensing and cloud access. Here’s how I see that:
Wireless connectivity should be based on the technologies already in smartphones. It should also have range – a bubble of connectivity – to avoid needing user actions to enable the connectivity.
Sensing in a minimum viable IoT device will be very simple – perhaps a trace on a circuit that gets broken, or an integrated temperature sensor.
For power, devices must be permanently powered. Power is needed for the connectivity and makes the sensing information that can be collected massively more valuable – not just because you can sense the whole time, but because power also allows you to determine when something has happened.
Some of the elements of a radically simple, low-cost IoT device have already arrived.
Bluetooth ‘Beacons’ are simple transmitters that broadcast to nearby devices. Several Beacon formats have arrived in recent years. Having assessed each of the viable contenders, I’m firmly a supporter of Google’s Eddystone format as an enabler of ultra-low-cost IoT. Here’s how it works: data is encrypted in Beacon adverts that are heard by phones. If it’s an Android phone it doesn’t even need to have an app installed, only for Google’s nearby services to be enabled. When the phone hears a new advert it forwards it to a resolving service in the cloud which decrypts that payload. This means that the information created by the infrastructure, and the revenue associated with it are protected. In short, architecture such as Eddystone turns all phones into IoT gateways and enables secure monetization in the cloud.
But today’s Beacon implementations will be hampered by price: with cost still related to the area of silicon used, the biggest single component cost will be the Bluetooth chip. This is an area where our own work at Cambridge Consultants holds great promise. Our radio technology breakthrough Pizzicato is the world’s first all-digital radio transmitter. That means we’ve removed all analogue components on a silicon radio, enabling the radio to transcend today’s physical barriers and to directly benefit from Moore’s Law, further shrinking in size, cost and power consumption with each new generation. Pizzicato has enabled us to demonstrate a 7 cent Bluetooth radio – a new benchmark in low-cost IoT, but a price point that we believe can and will be further reduced.
Having addressed the most expensive component of our future IoT device, we then need to look at the whole device in order to simplify it further and strip out cost.
Complexity is a big problem. Each additional part in a device increases the assembly costs and the number of assembly stages needed, parts suppliers must have margins and so on.
Our work has focused on all the parts that go into a device and why they’re needed. For example, a core stage in stripping out components is achieved by moving from designing the battery into a product, to instead designing the product into the battery. We have identified injection moldable plastics that are compatible with proven battery systems: both the major substances and their various by-products (in fact some materials such as polyimides, which can be used to define circuits, are already used within some batteries).
This shift means that we can create low-cost devices that are made using existing high volume production techniques, but which also have fewer assembly steps and fewer components.
At this point, our ultra-low-cost IoT vision is looking strong, but there remains a major problem with this approach: billions of tiny batteries in disposable devices are a chemical pollution disaster. Perhaps there’s a more organic approach.
Maybe you’ve heard of the researchers that have turned clams into batteries? This work effectively reproduces the reactions taking place in living organisms that generate electricity, and duplicates them outside of the cell. There are many research groups looking at this, with several lines of attack, but there’s a trend that gives us cause for optimism: the cost of sequencing DNA is falling like a stone, unlocking progress across many areas of biotech. Indeed this rapid progress is one of the reasons that we set up our own synthetic biology lab in 2016. Research groups are reporting shelf lives and operational lives of bio batteries measured in months and heading towards a year. From a cost perspective there is research suggesting an enzyme cost of 6 cents for a bio battery.
We’ve been taking a ruthless view on reducing cost, which is helpful to reduce the bottom line, but what about the return?
The key shift in approach is to secure revenue from all touchpoints through the device’s life. I’ve suggested that we imagine a bubble around all connected ‘things’ within which they can use a phone as a gateway. Now even simple sensing can generate revenue. Think for example of an IoT beer bottle that can be monitored through its life from manufacture, bottling, distribution, retail, consumer and recycling, delivering small packets of value at each stage. This might relate to temperature sensing, anti-tamper through track breaking, liquid level sensing, maximising recycle rates and more. With this approach, the Amazon Dash-style replenish button is just one of many stages delivering value in the life of a product.
This is just a small insight into our current work and thoughts for the future. It’s inevitably a mix of the technologies on today’s bleeding edge, and speculations for the future. But we’re optimistic, seeing a range of technologies just over the horizon that will strip out cost and ensure that IoT really does become the “Cambrian explosion” of connectivity that’s promised.
SCALING UP CELL THERAPIES
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There is a new generation of pharmaceuticals on the horizon. Over the last 50 years the pharmaceutical industry has moved from small-molecule compounds through to antibody-based biologic drugs and whole-cell therapies. Now researchers are trialling therapies using modified and manipulated cells taken from and returned to the patient. These next generation Advanced Therapy Medicinal Products (ATMPs) are intended to manage, and perhaps cure, chronic and debilitating conditions such as cancer, heart failure and dementia. An example of such a therapy is chimeric antigen receptor T-cells (CAR-T). Recent results from clinical trials are showing complete remission in some patients of B-cell acute lymphoblastic leukaemia using this approach.
In a clinical trial context, the therapy can be made manually using existing equipment and highly-trained scientists. However, these therapies present a significant challenge for scale-up to bring them into general usage. Unlike traditional therapies, they are often unique to each patient: the production batch size is one dose. The traditional model of scale-up, where a large-scale high-capital-cost facility is built to make millions of doses no longer applies.
To give a feel for the scale of the challenge, consider what has to be done. Blood must be taken from the patient – who is often severely ill – and processed to separate out the T-cells. These cells must then be stimulated to grow, genetically modified to express the desired cancer receptor using a viral vector, grown up to a high concentration, purified and returned to the patient for infusion. All this must be done in sterile conditions and to the highest possible quality standards. And, of course, the correct treatment must get back to the correct patient for infusion without deteriorating. As the cells are delicate (for example, they must remain cold) there is a short time window to deliver the therapy, which is hard to manage in a busy hospital environment. Now imagine starting over 250 of these processes a day and running over 6000 processes at the same time, with zero errors – which is what must be achieved to treat 100,000 patients a year.
Scaling of these therapies requires a new way of thinking. We need to develop truly novel equipment and processes and doing this requires combining many scientific and engineering disciplines and learning from other industries. For example, we need to design easy-to-use process hardware that maintains closed sterile conditions local to the cells for the entire process, rather than relying on manual operations in expensive grade A clean rooms. We need modular manufacturing systems that allow scale-out as new therapies achieve regulatory clearance and demand rises over time. We need integrated sensing systems that allow continuous monitoring of the cells during processing and the earliest possible warning if there are problems. Finally we need product tracking and logistics management to ensure the therapy arrives at the right patient at the right time without compromising product quality.
At Cambridge Consultants we’re bringing together the teams to develop this technology through our synthetic biology and medical technology capabilities. Through our combined knowledge and insight we’re looking to help pharmaceutical companies, and their supply chains, build the drug factories of the future.
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My colleague Rob Milner has already mapped out the technical advances that will soon lead to ultra-low-cost IoT devices, but there’s another set of technologies emerging that will light a fire under the Internet of Things: LPWAN, or Low-Power Wide-Area Networks.
LPWAN technologies are focused on long range connectivity, perhaps across cities and rural areas, with long battery life and providing low bandwidth for sensing applications. LPWAN technologies are important because they can be deployed in remote areas without requiring fixed power and because they enable data to be sent straight to the internet - ‘direct to cloud’ - without using traditional WiFi or cellular connections.
LPWAN technologies will open up dozens of new applications because they allow companies to get data back from sensor networks and then to make highly accurate and efficient resource decisions. The implications are huge: agricultural applications alone could help to feed the growing global population, while reducing water consumption.
Early LPWAN technologies required no cellular technology, but more recently the mobile network operators (MNOs), with their licensed spectrum, are making a play for the emerging LPWAN market. MNOs are putting their considerable weight behind an LPWAN technology named NB-IoT, which operates in licensed spectrum and can be provisioned on their networks almost at the flick of a switch. Their vision is for NB-IoT devices in the $5 - $10 range and, with no requirement to fund expensive infrastructure roll-out, we expect that the business case for NB-IoT will be compelling.
So far in 2017 we’ve seen NB-IoT roll-out announcements from Vodafone, Deutsche Telekom, China Telecom and others. I expect that 2018 will see the emergence of compelling services and applications.
A LONG DISTANCE RELATIONSHIP
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Regulated industries such as in vitro diagnostics have a compulsory requirement to receive and respond to enquiries from their clients every single time their users come across an unexpected event such as an error message. Failure to reply swiftly may result in the regulators taking strong measures in order to protect the safety of patients and users. Measures as dramatic as the closure of a manufacturing unit or the market withdrawal of a product incur substantial financial and reputational damage.
What is the current practice for this regulated client response? Let’s assume that a user is running samples on a brand new instrument purchased from a very reputable manufacturer. In the first week the user repeatedly receives the error message ‘Light ingress into the instrument: Repeat the test’. The user calls the local representative of the manufacturer to resolve the issue, who tries to advise over the phone. If the error message does not reset, then the instrument is returned to base and a new instrument is dispatched. If the replacement instrument gives the same error, you have two incidents in a week and an official investigation has to be carried out.
These investigations often reach all the way to the R&D department that initially designed the instrument, where the senior team of designers, engineers and scientists are involved in minuted meetings and provide an official answer to what is often a trivial matter. Valuable human resources are utilised to deal with these tasks.
IF ONLY A COMPUTER COULD HELP…Machine learning has taken great strides recently in all matters involving tasks that could be described as ‘reading and making sense of it’. Google Translate surprised the international community last November with their giant leap in the automated translation of texts between Japanese and English. Suddenly Hemingway could be translated into Japanese by machine learning, and still make sense (not an easy matter considering the author’s individual style of writing).
In the Spring of 2016, undergraduate students at Georgia Tech had (unbeknown to them) a virtual machine learning Assistant Professor who replied for a whole term to all their on-line enquires such as type of format for essays, requests for extended deadlines… very time consuming matters for a professor to answer, albeit important for the students.
To generate this type of AI response system, the machine learning software read 40,000 previous questions and the answers given by a human professor, until it ‘understood the general gist’ of what students typically asked and what were the right ‘human-replies’ previously given. In six months no student realised they were exchanging emails with a computer, and they actually loved the speedy and accurate responses.
Back to the R&D department at a diagnostics aeronautics and medical consumables manufacturer. They must officially respond to queries from clients reporting ‘incidents in the field’, and to write those responses they need time from the most senior scientists and engineers with years of accumulated experience.
What if, like the Georgia Tech system did, you could get machine learning systems to read years and years and thousands and thousands of enquiries from clients and the responses written by human experts? Would such machine learning systems be able to automatically read new queries and write substantiated responses with the most probable causes for those technical issues, just like a group of humans would do?
Would this be a useful implementation of machine learning? Would this provide automated, fast, accurate and regulated responses to clients, with matching quality to that which a group of experienced engineers could achieve, whilst freeing their time?
Would machine learning be the tool that helps regulated industries keep good communication with their clients, while ensuring that safety is paramount as all incidents are reviewed promptly?
Would these machine learning tools free human minds to get on with more creative matters?
WOULD IT WORK…?I see machine learning as a tool that can complement the minds of creative scientists and clinicians around the world, freeing us from repetitive tasks and supporting our creative efforts. A complementary tool for humans, not a substitute. I look forward to working with my Machine Learning assistant very soon.
MACHINE LEARNING WILL HELP27
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Editor: Richard Leyland Design: 2i Design Ltd Neither the editor, nor Cambridge Consultants Ltd, necessarily endorse any opinion, real or implied, expressed by contributors to Interface. No part of this publication may be reproduced without written permission of Cambridge Consultants Ltd. ©2017. All rights reserved.