Work automation and machine collaboration

By Rob Leslie-Carter et al

Edition 9 – December 2017 Pages 43-51

Tags: automation • artificial intelligence

Two things have happened recently that make the timing of this article on artificial intelligence (AI) particularly serendipitous. The first is the release of an incredible video showcasing the first segment of a steel bridge designed by Arup and MX3D but printed by robots. Amsterdam based start-up MX3D created intelligent software that transforms a robot and a welding machine into a large scale printer, enabling 3D printing of metals on an architectural scale. This new technique provides new opportunities for architects and engineers and has huge potential to reduce the delivery time and amount of material needed to make large structures. The printing and assembly began in March 2017, and the bridge is scheduled to be finalised in early 2018. More information about the printing process can be found at mx3d.com/visitor.

The second coincidence was arriving at our London office to an exhibition in the foyer devoted to AI. The various spaces, robots and screens showcase how new approaches to AI are already revolutionising our lives using real-time data. Visitors experience AI’s strengths and weaknesses, exploring the differences between fully learnt machines or machines learning at the edge, and get to play with some of today’s consumer solutions. The facial recognition screen correctly confirmed who I am (from my intranet profile photo I think), my 20 second cow sketch was sufficiently poor to flummox Google Quick Draw into concluding I’d been attempting a dog all along, and Amazon’s continually learning Echo obeyed my voice request to define ‘machine learning’.

The exhibition is part of Arup’s digital transformation programme, and is dedicated to assisting us to adapt to the rapid changes in AI around us – it runs from 2 October 2017 to 12 January 2018. The exhibits were put together with the collaboration of Arup Inspire, Ambi, Comfy, Autodesk, Google Creative Lab, Manou Mani-Architects, Nvidia, TED and IBM Watson and Yarn. The force behind this provocative event is our global Foresight team – Arup’s internal think-tank which deals with the future of the built environment and society at large.

Human Machine Collaboration – the current picture

The world is changing fast. A wide range of trends and challenges have a direct bearing on the future of work and place. It is vital that we understand these trends, so that we can better manage the risks facing our profession, and make the most of emerging opportunities. Our economy is increasingly driven by project-based work characterised by high degrees of collaboration. Innovation and creativity are the key components of value creation, while employee expectations and working cultures are changing all the time.

We are seeing new forms of working that are enabled by digital technologies, on projects that are both complex and global. Understanding and managing these changes is vital, if we want to continue to provide solutions that truly meet the needs of our clients and stakeholders. Driven by rapid advances in digital technologies, the nature of our work is being transformed. While artificial intelligence and robotics grow more sophisticated, jobs are being reinvented. Collaboration and communication through increasingly intuitive user-friendly interfaces could lead to fundamental changes in workplace structures and may offer new possibilities for productivity and creativity in the workforce. Human-machine collaboration will open the way to virtual and network-based companies as everything shifts online.

Organisations are already reconsidering the shape and composition of their workforce. According to Deloitte, 41 percent of surveyed companies have already implemented aspects of cognitive or artificial intelligence (AI) technologies in their workforce, whilst 37 percent are carrying out pilot programmes. However, only 17 percent of surveyed executives stated a readiness to manage a collaborative workforce of people, robots and AI.

The area with the greatest scope for change is in manufacturing – in the automation of repetitive tasks. In Germany, for example, it is estimated that up to 80 percent of jobs for people with low-level education are at risk from automation, compared with only 18 percent for people with a doctorate degree. It’s a similar story when we look at income levels: in the lowest 10 percent income group, 61 percent of jobs are projected to be at risk, while only 20 percent are under threat at the upper end.

As companies redesign jobs and workforces, questions arise around the eventual limits of automation. Could essential human skills, such as empathy, communication, persuasion, personal service, problem-solving, and strategic decision-making become even more valuable?

In moving towards greater automation, companies will have to rethink the role of people and provide training to prepare their employees for this new work environment. Robots and people work side-by-side at Ford’s Cologne plant, complementing each other’s skills (simple and heavy manual tasks vs creative thinking). Businesses might soon start dividing skills and reframing jobs according to essential human skills and non-essential tasks that could be carried out by machines.

Machine learning graduates to the built environment

Machine learning applications are already ubiquitous in our everyday life. When you log into Facebook and someone has tagged you in a photo that is a prime example of the roots of machine learning, which reside in image and facial recognition. Not only does it recognise that it is your face, but also that you have a human face based on the features and relationship between your pixels and all other pixels in the image.

When you speak to Siri on an iPhone, it ‘hears’ your words using speech recognition. When you use Google Translate the sequence of words you used is likely being translated now by something called a recurrent neural network.

When you open your email (mostly) free of unwanted messages, you can thank machine learning for the spam filter – which is likely powered by a technique which has classified junk from non-junk based on the nuanced features of many millions of spam-classified emails.

When online shopping, or browsing Netflix, recommendations are given to us on what we are likely to watch from an algorithm of people who are likely similar to us, and have made similar choices to us.

While AI encompasses the broader goal of computers that can learn and act, machine learning is much more specific sub-set of AI which can be used for solving well-defined problems. Deep learning is a further extension of machine learning, which expands the concept of neural networks (which are inspired by the functionality of the human brain).

Unlike usual algorithms used to perform specific tasks, machine learning methods are employed to learn how to perform a specific task – learning as more data is provided. Just as we have different learning styles, there are (quite a few) different ways which a machine can learn. These methods can be categorised into either supervised learning (where the algorithms have a training dataset to learn from) or unsupervised learning (where we are interested more in discovering underlying patterns and structure in data).

As our computing processing power increases, storage becomes cheaper and data sources richer there is an increasing demand to the develop methods and skills to solve problems with machine learning in many domains – which is potentially any that involves data and identifying patterns.

So away from use cases that we can mainly find on our mobile phones and the internet what are some potential applications in the physical, built environment?

Environment and waste management

Several years ago, Volvo announced that it was developing robots to replace the physically-demanding, sometimes dangerous task of garbage collection with a more automated, robotic system. To add to this, machine learning could track and predict waste levels in a city’s bins and manage demand (and charge users) accordingly.

Architecture and urban design

MIT Media lab have collated a large set of data on people’s perceptions of safety to feed a machine-learning algorithm which determines how safe a street may look to the human eye. This kind of research could alter the way we design spaces to suit the potential emotional goals or needs of a space, and help us understand which features we ought to include or exclude from design.

Energy and utilities – optimising consumer energy use

The optimal, sustainable use of our energy systems both at the supply-side and the user-side is critical to sustainable future. Being able to better predict accurate energy consumption forecasts can help implement better energy-saving policies in cities. The Nest Thermostat uses machine learning to learn a homeowner’s preferences and schedules to optimise heating and cooling.

The energy sector in Germany has employed machine learning to optimise the power grid and manage the maximisation of renewable versus non-renewable energy.

Intelligent transportation and autonomous vehicles

Machine learning is well-known to be integral to driverless cars, which use complex image and spatial recognition to identify road features, pedestrians and other vehicles in order to provide seamless, automated travel in cities. Using these sensors and on-board analytics, cars are able to recognise objects and react appropriately using Deep Learning. MIT’s Moral Machine is an experiment on how machines might decide in crash scenarios, where human moral decisions are collected and analysed against the same decisions machines would need to make.

Transport services on-demand

Whether empty-running or on fixed timetables, bus routes could be dynamically altered to meet passenger needs. When the weather is bad, buses could be put to use to keep up to pace with increase in ridership. Routes could be adjusted dynamically to better fit with door-to-door demand for the users who have opted into that service.

Personalised trip-planning

Mobile phone applications could review the travel options available to you and make personalised recommendations using machine learning to account for preferences such as lifestyle choices, fitness levels, previous locations visited, amenity along the way, budget and dynamic predictions of congestion en-route.

Crash and congestion monitoring and response

Image recognition could monitor and recognise both congestion and road accidents before, during and after they happen. These would allow systems to adjust the road conditions (such as variable message signs and speed limit) systems accordingly, as well as notify traffic control and emergency services.

How digital technology and big data are changing the property sector

The property sector is on the verge of a huge leap forward in how it uses data-driven, digital products and services to make better decisions, construct better projects, and achieve better outcomes. Ubiquitous sensors, flexible and open IT systems and powerful cloud computing are creating more seamless and integrated experiences in many sectors. But property development often hasn’t kept pace. For example, traditional project budgeting isn’t yet aligned with the needs of a more integrated world. Costing processes fail to deliver the digital experiences that tenants or employees increasingly expect. And the balance of CAPEX and OPEX is changing as some previously fixed products become services with recurring costs and revenues. New thinking is needed.

There are three stages to reimaging property in a digital world. We call the first stage of digital adoption the Run phase. Many firms are at this point, using digital tools and approaches for specific, tactical projects, gaining some tangible benefits.

The Grow phase occurs when leading-edge firms begin to move beyond using isolated tactical digital solutions to solve distinct problems in the lifecycle of their assets. Grow businesses widen their focus to see how the entire asset lifecycle can be improved using digital tools and approaches. In this phase of the digital journey additional emphasis is placed on the transitions and hand-offs between phases.

The third and final phase of digital maturity is Transform. Clients now benefit from a single, integrated, digital master plan that adds value not only across all phases of the lifecycle of any single asset, but also across the entire portfolio of assets, whether local, regional or global. This leads to increased long term valuations, improved end user experiences, and better operational asset performance

Digital transformation improves portfolio efficiency by increasing integration and automation of building operations. It makes the facilities manager’s role far more strategic, and maximises use of the building and its assets. It also drives up tenants and occupants’ expectations about the experience produced, both functionally and emotionally.

Users benefit from a range of integrated services that support their individual needs and preferences. And given buildings’ long lifecycles, digital transformation represents increased agility by giving property owners and managers new abilities to adapt to changing user needs over the lifecycle of the building.

These buildings provide a wealth of actionable data that allow far better portfolio management and planning for the future. For property decision makers, these new cloud-based data systems, powered by artificial intelligence, will make it possible to store, process and visualise data that create portfolio-wide insights.

 

The Human Factor(y)

“The full potential of the industrial Internet will be felt when the three primary digital elements – intelligent devices, intelligent systems and intelligent automation – fully merge with the physical machines, facilities, fleets and networks. When this occurs, the benefits of enhanced productivity, lower costs and reduced waste will propagate through the entire industrial economy.”

—Peter Evans and Macro Annunziata, GE, Industrial Internet: Pushing the Boundaries of Minds and Machines (2012)

A new relationship between people and machines

Since the 1970’s the proportion of German workers employed in the manufacturing sector has dropped by more than half, to about 20 percent. At the same time, exports of manufactured goods have increased and Germany continues to rank fourth by global manufacturing output. This trend is a reflection of a continued increase in automation within Germany’s production lines, allowing the nation to remain competitive despite relatively high labour costs.

Given a steady rate of production, continued advances in both factory automation and robotics reduce the number of people needed to produce goods. While some argue that this trend could make certain workers’ positions redundant, proponents assert that it will make workers more productive and relieve them of unpleasant or unsafe jobs.

Automation increases reliability and product quality, and often makes it easier to adapt production lines and create flexible production processes. For many organisations in the manufacturing sector, automation is also part of a strategy to deal with the emerging risk of a shrinking and aging labour force, or the ongoing risk of cheaper labour costs in other countries.

In Asia, labour costs are continuing to rise, cutting into the region’s competitive advantage. Nevertheless, China’s factories are still much cheaper than those in wealthier nations – employees’ minimum wages are less than a quarter of their counterparts in the United States.

As the global manufacturing hub, rising prices in Asia are reflected in an upwards adjustment of prices worldwide. Average pay in Asia almost doubled between 2000 and 2011, compared to an increase of about 23 percent worldwide (and a 5 percent increase in developed countries). The biggest increase was in China, which saw average salaries triple. Lower wage countries like Cambodia and Vietnam are beginning to attract manufacturers, meaning that China – which accounts for half of Asia’s output – is embracing greater automation to ensure that local factories remain competitive.

An example of the automation trend, Flextronics, a Singapore-based company with factories in China, initially made small, simple-to-assemble consumer electronics. But as wages, land costs and competition in China began to rise, shrinking margins prompted a focus on more complex, higher priced products. This required investments in automation, more precise manufacturing and increased staff training.

Higher-priced machines for the aerospace, robotics, automotive, and medical industries now make up 72 percent of the company’s Suzhou output. Flextronics has implemented automated processes wherever it has the potential to reduce labour costs and errors. Automated data about the assembly line is now collected in real-time and there is far more transparency of the supply chain.

Asia has become the largest market for industrial robotics, with China showing the fastest growth over the past five years. Global demand for industrial robots also continues to grow. The International Federation of Robotics (IFR) expects that between 2014 and 2016 the worldwide sale of robots will increase by an average of 6 percent per year. By 2016, the annual supply of industrial robots will reach more than 190,000 units. MGI research suggests that 15 to 25 percent of the tasks of industrial workers in developed countries, and 5 to 15 percent of those in developing countries, could be automated by 2025.

One significant development in workplace automation is that the factory robot of the future will be able to safely interact and cooperate with its human co-workers. The aim of industrial designers is to combine the ingenuity and versatility of people with the precision and repeatability of robots, enabling human-machine collaboration in dynamic and reconfigurable manufacturing environments. A world optimised for both humans and robots.

For example, Baxter, a robot manufactured by Rethink Robotics, can safely share a workspace with workers due to its variety of smart sensors and cameras. Interacting with Baxter is more like working with a person than operating a traditional industrial robot. Baxter’s sensors, including depth sensors as well as cameras in its wrists (allowing it to see with its hands), means it constantly builds and adjusts a mathematical model of the world, allowing it to recognise different objects.

The robot is also intuitive to use, allowing regular factory workers to function as programmers. A factory worker can show the robot a fragment of the task she is asking the robot to perform, and the robot infers the rest of the task. Workers are therefore not in competition with these machines, because they can serve as supervisors. A Baxter retails for around US$25,000 – roughly equivalent to the annual salary of an unskilled worker in the US.

By 2050, 21 percent of the global population will be 60 years old or older, up from 11 percent in 2013. This trend is even more notable in developed countries where 32 percent of people will be aged 60 or older by 2050. Within these ageing societies, the supply of working age people will decline as a proportion of the total population, and working age people will have to support more dependents. In less developed regions there will be more young people, providing a larger workforce and growing consumer markets.

In the next few decades, new forms of human enhancement and augmented capabilities may support mental performance and physical mobility, helping to counter the effects of an ageing population. This is already evident today in the growing application of cyber-physical systems (CPS). CPS are “physical and engineered systems whose operations are monitored, coordinated, controlled and integrated by a computing and communication core”. CPS will transform how people interact with and control the physical world around them. These systems will enable the physical world to merge with the virtual world, allowing factory workers to design products, control processes and manage operations in radically new ways, enabling greater flexibility, productivity and quality.

As production lines and machines become more advanced and specialised, companies must also invest more in training and specialised equipment to enable the workforce to manage and operate complex production lines. There will be a heightened need for skilled workers and managers who are adept in the STEM fields (science, technology, engineering and mathematics) as manufacturing shifts to more complex and technological processes. Collectively, this will lead to a shift to safer, more highly skilled jobs in manufacturing.

An open and engaging customer experience

Many developed and emerging economies are witnessing a transformation in how people consume products and services. In addition to a shift to more service-based consumption, a democratisation of product design and manufacturing is occurring. The maker movement, 3D printing, open product development platforms, crowd funding and peer-to-peer marketplaces are empowering more people to design, produce and share their own goods than ever before. In response, consumer product companies are integrating these types of experiences into their existing service offerings, enabling the mass customisation of products, or participation in open innovation processes. Faster innovation cycles, coupled with constantly changing market conditions and demand patterns, mean that manufacturers will need a more agile and flexible approach to production, both in terms of the machines deployed, but also in terms of the shape and function of buildings and the skillsets of people working within them. Another aspect of this transformation is a growing opportunity to utilise the factory as a showroom. Many companies have built sophisticated customer experiences around their factories. These showroom experiences are part of the larger trend of customers demanding “connected product experiences”, rather than just a product.

In Volkswagen’s ‘Glass Factory’, for example, customers and potential buyers of the Phaeton luxury saloon can watch their car’s final assembly process at close hand. The concept of the transparent factory and factory experience will gain increased importance as more people get involved in making things themselves or as they expect closer insight into how products are manufactured, especially at a customised level. The opportunity for factory owners and operators lies in adapting their existing spaces to enable these types of experiences to take place.

Chrysler is taking this idea one step further with a virtual reality experience of its factory floor. Users put on a headset to experience a four-minute, 4D immersive experience of how the 2015 Chrysler 200 is made. Users can interact with the car in real-time via the headset while exploring the three aspects of the car’s building process. In the body shop, 18 state-of-the-art framing robots weld the frame of the car together. The next stage is the paint shop, where the car is prepped for its paint job with the help of ostrich feathers before being given its coating. Finally, in the metrology centre, the vehicle’s fit and finish is checked and measured.

Brand experiences are not limited to ‘fun’ consumer products like cars. Saunier Duval, a manufacturer of heating, ventilation and air-conditioning technology, has created a factory tour at its plant in Nantes, France which takes in a 360 degree, 3D cinema show, an interactive display of the company’s products and a meal.

These sorts of consumer experiences help differentiate a company’s products. To remain competitive and adapt to changing consumer behaviour, companies are finding new concepts and marketing strategies to build brand loyalty. ‘Experience marketing’ of this kind can also be very useful in creating an image and corporate identity, capitalising on the idea that people “won’t remember what you said, but they will always remember how you made them feel.”

New robots will revolutionise the built environment

In the past, robots were used for specialist jobs that were too dull, too dangerous or too dirty for people to do. Today, thanks to their ability to process sensor data in real time, robots do an incredible range of things. They already clean your house. Soon they’ll be able to help design and build your house too.

The tipping point has come as robotics has shifted from being the domain of the mechanical engineer to the realm of network experts. They’ve applied smartphone technology to produce robots that can sense, process data, and communicate with each other via the cloud to learn.

One example is Roomba, a vacuum-cleaning robot. In its first generation, Roomba would bump around your walls. Then it learned how to sense and spare your furniture. Now in its third generation, Roomba takes a picture of your ceiling to know where it is, a technique called robotic mapping.

Telecoms company Qualcomm has demonstrated the potential of this approach by taking a smartphone and adding four wheels and a motor. The power of the smartphone industry’s skills and technology might enable developers to leap 30 years of development and produce a robot capable of much more sophisticated actions than anything that has gone before.

What does this mean for construction? Traditionally, building takes place onsite and by hand. In the future, according to conventional thinking, machines will perform construction offsite. But this has been proposed for years. I think there will be an alternative: robots working onsite alongside humans.

This is something researchers at ETH Zürich have begun to explore. Give a robot a pile of bricks, and it will build you a perfect wall. And it can achieve effects that a human bricklayer simply can’t, such as turning each brick by exactly one degree to produce a very subtly curved wall. It does the same with timber cladding and tiles.

The leap in robotics has implications for design too. I don’t think we’ll see robots designing buildings autonomously. But I do think we could see them working alongside designers. You could use a robot to help you build a physical model, for example. And companies such as Skycatch already use drones for 3D scanning to provide cost-effective, high-speed and high-quality data about an area.

Should we be worried about any of this? Are machines going to take our place? I don’t think so. As I heard inventor Saul Griffiths point out, robots are still blind, stupid, fat, weak, slow and difficult. They’re an opportunity, not a threat.

Autonomy enables adaptive built environments

The future points to an autonomously crafted built working environment in which many assets can adapt intelligently to both users’ changing needs and the threats of a changing climate.

Designers’ novel thinking about data, tools and methods is advancing to a point where it’s possible to foresee an autonomously crafted built environment, one that mimics nature’s ability to adapt to environmental change over time. This technology will be a vital way of dealing with the effects of an increasingly volatile climate.

When built environments’ systems possess artificial intelligence (AI) fed by sensors a degree of autonomous decision making becomes possible. Autonomy is achieved by combining local learning from cameras and sensors, correlated to data and intelligence drawn from other AI-enabled assets.

Built environments that respond to a changing climate

This combination of advances means our built assets will be able to respond to their environment, autonomously reacting to changes in temperature, weather, human usage patterns, and other factors. In this convergence, designers and data scientists contribute their insights into the model, to ensure the variety of aspects taken into consideration and sheer volume of data is provided to shape the kinds of adapting preferred scenarios the artificial intelligence understands. These scenarios in turn train machines to rapidly produce the most sensitive and customised design solutions. A continuous feedback loop of data from the asset’s environment and its users ensures success.

The comfort and energy performance benefits of this new approach are clear. A feedback loop is used by the Hong Kong-based start-up Ambi Climate, an Internet of Things (IoT) app that controls individual air conditioning units located in different rooms from a smart phone. Ambi Climate learns the inhabitant’s preferences (times at work, temperatures enjoyed), applies this knowledge, and autonomously creates a tailored profile.

In the future, because homes, buildings and urban infrastructure will be connected and self-aware through smart components, design updates will occur autonomously. Inefficient, over-scheduled maintenance schemes will be replaced by machine-learning algorithms that are far more capable of knowing when preventative maintenance is needed, based on a growing bank of performance data from sensors. Resources and energy will all be saved.

Challenges of autonomy

Autonomy also represents a challenge to traditional human roles in the design of the built environment, because it can match and surpass human solutions at scale. This approach will be an improvement on today’s often outdated, inappropriate designs, ones that often focus only on the requirements of society’s top one per cent. The challenge for human designers will be how to embrace this new data paradigm of timely, appropriate and scalable design solutions.

This amount of autonomy also presents a challenge to the operators and regulators of the built environment. Current data privacy and confidentiality barriers will need to be overcome and new local connectivity systems for instant and robust connectivity (hubs) will need to be developed to provide instant yet democratic harmonization of the built environment.

Adoption a question of time?

Fully autonomous operation might still be in the future, but a measure of it has already been achieved on projects like the 3D printed Daedalus Pavilion. On this project an algorithm autonomously adapted the material density required for the building. At the same time, a robot fabricator with cameras connected to AI capabilities was able to judge how far its landing position to deposit material was from the design position, and thus able to correct itself. An AI feedback loop allowed it to be quicker by being more daring – it learnt from its mistakes.

With the amount of investment currently being directed at machine learning and artificial intelligence I think it is more a question of when, not if, autonomous decision making like this will become possible.

Can you imagine having a robotic co-worker?

I believe we appear to be at the tipping point of technology that will enable collaborative robots to be used much more widely in the workplace. Robots of the future will be able to interact safely and co-operate with human co-workers, as well as learn from them.

I am no expert on robotics and artificial intelligence but, as a futurist, I am increasingly drawn to this complex, rapidly evolving and very exciting field. The implications will be far-reaching, and we may have to adapt our lives to work alongside increasingly sophisticated robots (many of which won’t be humanoid in form, but could be a collection of disembodied sensors).

Robots will replace or augment not only unskilled, routine jobs, but also many highly paid and highly skilled jobs. These could include doctors, journalists and financial traders (automated algorithms are already responsible for high volumes of financial transactions).

In our recent Foresight publication Rethinking the Factory, we look at the increasing use of collaborative robots in manufacturing. Factories have long used industrial robots for tasks that involve heavy lifting or repetitive jobs that require speed and precision. However, these robots have been too unintelligent and dangerous to work alongside humans, who tend to perform more delicate final assembly jobs or tasks that require flexibility.

This is changing. Take Rethink Robotics’ Sawyer robot, which can be taught to perform tasks by human co-workers who have no programming expertise. A human can physically guide Saywer’s arm through part of an activity and Sawyer can then infer the rest of the task.

One of the most interesting developments is the ability of robots to learn to perform complex tasks without being reprogrammed, by building knowledge through trial and error. They learn from experience and can adapt their behaviour to improve upon a task.

For example, a robot from the Berkeley Robot Learning Lab called BRETT (which stands for Berkeley Robot for the Elimination of Tedious Tasks) uses deep learning to complete tasks without input from humans. Using trial and error it has learnt to assemble a basic toy plane and to place a Lego brick in the correct position.

In the next decade, we could see robots learning complex tasks from scratch. These robots will learn in a similar way to humans, through consuming information, demonstrations by others, and trial and error.

This ability to learn as well as interact safely with humans will have implications far beyond the factory floor. One day, we could see robots taking over manual labour tasks such as painting walls, cooking meals, repairing roads, folding laundry or walking the dog. But it is a mistake to think only manual labour will be affected; we all have elements of our jobs that are predictable and subject to automation.

While the reality of your employer replacing you with a robot may still be some way off, we need to start considering whether our education and professional training systems are fit for the robotic age. As robots become more flexible and responsive, human workers will need to develop new skills and take on more creative or supervisory roles, or they will become redundant. Ultimately, humans will need to possess more flexible skill sets than their robotic co-workers.

As futurist Alvin Toffler has noted: “The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn” W&P

References

The author would like to thank the colleagues from whose work this feature borrows its constituent parts as listed in the references below.

1. https://youtu.be/v2moJF8kqIg
2. More information about the printing process can be found at www.mx3d.com/visitor.
3. Machine learning graduates from Facebook to the built environment from an original article by Oliver Lock
4. How digital technology and big data is changing the property sector from Reimagining Property in a Digital World
5. The Human Factor(y) from Rethinking the Factory
6. New robots will revolutionise the built environment from an original article by Alvise Simondetti published in Arup Thoughts
7. Autonomy enables adaptive built environment from an original article by Alvise Simondetti published in Arup Thoughts
8. Can you imagine having a robotic co-worker? from an original article by Lynne Goulding published in Arup Thoughts

Quotes:

…Collaboration and communication through increasingly intuitive user-friendly interfaces could lead to fundamental changes in workplace structures and may offer new possibilities for productivity and creativity…

…Unlike usual algorithms used to perform specific tasks, machine learning methods are employed to learn how to perform a specific task…

…The property sector is on the verge of a huge leap forward in how it uses data-driven, digital products and services to make better decisions, construct better projects, and achieve better outcomes…

…For many organisations in the manufacturing sector, automation is also part of a strategy to deal with the emerging risk of a shrinking and aging labour force, or the ongoing risk of cheaper labour costs in other countries…

…In the next few decades, new forms of human enhancement and augmented capabilities may support mental performance and physical mobility, helping to counter the effects of an ageing population…

…The tipping point has come as robotics has shifted from being the domain of the mechanical engineer to the realm of network experts…

…When built environments’ systems possess artificial intelligence (AI) fed by sensors a degree of autonomous decision making becomes possible…

… One of the most interesting developments is the ability of robots to learn to perform complex tasks without being reprogrammed, by building knowledge through trial and error…

 

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